To see the other types of publications on this topic, follow the link: State of health (SOH).
Journal articles on the topic 'State of health (SOH)'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'State of health (SOH).'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Fang, Liu, Liu Xinyi, Su Weixing, Chen Hanning, He Maowei, and Liang Xiaodan. "State-of-Health Online Estimation for Li-Ion Battery." SAE International Journal of Electrified Vehicles 9, no. 2 (December 31, 2020): 185–96. http://dx.doi.org/10.4271/14-09-02-0012.
Full textAbstract:
To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.
2
O. Hadi, Pradita, and Goro Fujita. "Battery Charge Control by State of Health Estimation." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 3 (March 1, 2017): 508. http://dx.doi.org/10.11591/ijeecs.v5.i3.pp508-514.
Full textAbstract:
Battery lifetime is one of importance consideration in smart system with energy storage system, because it is shorter than others. Extended of battery lifetime can give benefit to entire system, especially to reduce cost. The lifetime is commonly estimated by State of Health (SOH). Decreasing of SOH indicates degradation of battery. It can be influenced by the battery operation, so that operational management is needed. This study proposes control block for charging battery by using decreasing value of SOH as reference. The control block is implemented in battery system that connected to DC bus by bidirectional chopper. Numerical simulation study is performed by using PSIM software version 10.0. The result shows that the proposed block control is successfully used. Moreover, the relative error is less than 2% for delta SOH and less than 1% for battery power.<em> </em>
3
Noura, Nassim, Loïc Boulon, and Samir Jemeï. "A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges." World Electric Vehicle Journal 11, no. 4 (October 16, 2020): 66. http://dx.doi.org/10.3390/wevj11040066.
Full textAbstract:
To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy.
4
Al-Gabalawy, Mostafa, Karar Mahmoud, Mohamed M. F. Darwish, James A. Dawson, Matti Lehtonen, and Nesreen S. Hosny. "Reliable and Robust Observer for Simultaneously Estimating State-of-Charge and State-of-Health of LiFePO4 Batteries." Applied Sciences 11, no. 8 (April 16, 2021): 3609. http://dx.doi.org/10.3390/app11083609.
Full textAbstract:
Batteries are everywhere, in all forms of transportation, electronics, and constitute a method to store clean energy. Among the diverse types available, the lithium-iron-phosphate (LiFePO4) battery stands out for its common usage in many applications. For the battery’s safe operation, the state of charge (SOC) and state of health (SOH) estimations are essential. Therefore, a reliable and robust observer is proposed in this paper which could estimate the SOC and SOH of LiFePO4 batteries simultaneously with high accuracy rates. For this purpose, a battery model was developed by establishing an equivalent-circuit model with the ambient temperature and the current as inputs, while the measured output was adopted to be the voltage where current and terminal voltage sensors are utilized. Another vital contribution is formulating a comprehensive model that combines three parts: a thermal model, an electrical model, and an aging model. To ensure high accuracy rates of the proposed observer, we adopt the use of the dual extend Kalman filter (DEKF) for the SOC and SOH estimation of LiFePO4 batteries. To test the effectiveness of the proposed observer, various simulations and test cases were performed where the construction of the battery system and the simulation were done using MATLAB. The findings confirm that the best observer was a voltage-temperature (VT) observer, which could observe SOC accurately with great robustness, while an open-loop observer was used to observe the SOH. Furthermore, the robustness of the designed observer was proved by simulating ill-conditions that involve wrong initial estimates and wrong model parameters. The results demonstrate the reliability and robustness of the proposed observer for simultaneously estimating the SOC and SOH of LiFePO4 batteries.
5
Yao, Lei, Shiming Xu, Aihua Tang, Fang Zhou, Junjian Hou, Yanqiu Xiao, and Zhijun Fu. "A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods." World Electric Vehicle Journal 12, no. 3 (August 10, 2021): 113. http://dx.doi.org/10.3390/wevj12030113.
Full textAbstract:
Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.
6
Jia, Guan, and Wu. "A State of Health Estimation Framework for Lithium-Ion Batteries Using Transfer Components Analysis." Energies 12, no. 13 (June 30, 2019): 2524. http://dx.doi.org/10.3390/en12132524.
Full textAbstract:
As different types of lithium batteries are increasingly employed in various devices, it is crucial to predict the state of health (SOH) of lithium batteries. There are plenty of methods for SOH estimation of a lithium-ion battery. However, existing technologies often have computational complexity. Furthermore, it is difficult to use least the previous 30% of data of the battery degradation process to predict the SOH variation of the entire degradation process. To address this problem, in this paper, the SOH of the target battery is estimated based on the transfer of different battery data sets. Firstly, according to importance sampling (IS), valid features are extracted from cycles of charging voltage in both the source and target battery. Secondly, transfer component analysis (TCA) is used to map the source data set to the target data set. Moreover, an extreme learning machine (ELM) algorithm is employed to train a single hidden layer feed forward neural network (SLFN) for its fast training speed and facile to set up. Finally, validation experiments and the comparisons on the results are conducted. The results showed that the proposed framework has a good capability of predicting the SOH of lithium batteries.
7
Yang, Yanru, Jie Wen, Yuanhao Shi, and Jianchao Zeng. "State of Health Prediction of Lithium-Ion Batteries Based on the Discharge Voltage and Temperature." Electronics 10, no. 12 (June 21, 2021): 1497. http://dx.doi.org/10.3390/electronics10121497.
Full textAbstract:
Accurate state of health (SOH) prediction of lithium-ion batteries is essential for battery health management. In this paper, a novel method of predicting the SOH of lithium-ion batteries based on the voltage and temperature in the discharging process is proposed to achieve the accurate prediction. Both the equal voltage discharge time and the temperature change during the discharge process are regarded as health indicators (HIs), and then, the Pearson and Spearman relational analysis methods are applied to evaluate the relevance between HIs and SOH. On this basis, we modify the relevance vector machine (RVM) to a multiple kernel relevance vector machine (MKRVM) by combining Gaussian with sigmoid function to improve the accuracy of SOH prediction. The particle swarm optimization (PSO) is used to find the optimal weight and kernel function parameters of MKRVM. The aging data from NASA Ames Prognostics Center of Excellence are used to verify the effectiveness and accuracy of the proposed method in numerical simulations, whose results show that the MKRVM method has higher SOH prediction accuracy of lithium-ion batteries than the relevant methods.
8
Qu, Shaofei, Yongzhe Kang, Pingwei Gu, Chenghui Zhang, and Bin Duan. "A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis." Energies 12, no. 17 (August 29, 2019): 3333. http://dx.doi.org/10.3390/en12173333.
Full textAbstract:
Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.
9
Che, Yunhong, Aoife Foley, Moustafa El-Gindy, Xianke Lin, Xiaosong Hu, and Michael Pecht. "Joint Estimation of Inconsistency and State of Health for Series Battery Packs." Automotive Innovation 4, no. 1 (January 8, 2021): 103–16. http://dx.doi.org/10.1007/s42154-020-00128-8.
Full textAbstract:
AbstractBattery packs are applied in various areas (e.g., electric vehicles, energy storage, space, mining, etc.), which requires the state of health (SOH) to be accurately estimated. Inconsistency, also known as cell variation, is considered a significant evaluation index that greatly affects the degradation of battery pack. This paper proposes a novel joint inconsistency and SOH estimation method under cycling, which fills the gap of joint estimation based on the fast-charging process for electric vehicles. First, fifteen features are extracted from current change points during the partial charging process. Then, a joint estimation system is designed, where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency. A wrapper is used to select the optimal feature subset, and Gaussian process regression is implemented to estimate the SOH. Finally, the estimation performance is assessed by the test data. The results show that the inconsistency evaluation can reflect the aging conditions, and the inconsistency does affect the aging process. The wrapper selection method improves the accuracy of SOH estimation by about 75.8% compared to the traditional filter method when only 10% of data is used for model training. The maximum absolute error and root mean square error are 2.58% and 0.93%, respectively.
10
Lee, Jong-Hyun, and In-Soo Lee. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result." Energies 14, no. 15 (July 26, 2021): 4506. http://dx.doi.org/10.3390/en14154506.
Full textAbstract:
Lithium batteries are the most common energy storage devices in items such as electric vehicles, portable devices, and energy storage systems. However, if lithium batteries are not continuously monitored, their performance could degrade, their lifetime become shortened, or severe damage or explosion could be induced. To prevent such accidents, we propose a lithium battery state of health monitoring method and state of charge estimation algorithm based on the state of health results. The proposed method uses four neural network models. A neural network model was used for the state of health diagnosis using a multilayer neural network model. The other three neural network models were configured as neural network model banks, and the state of charge was estimated using a multilayer neural network or long short-term memory. The three neural network model banks were defined as normal, caution, and fault neural network models. Experimental results showed that the proposed method using the long short-term memory model based on the state of health diagnosis results outperformed the counterpart methods.
11
Huang, Kai, Yong-Fang Guo, Ming-Lang Tseng, Kuo-Jui Wu, and Zhi-Gang Li. "A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach." Applied Sciences 8, no. 10 (October 2, 2018): 1803. http://dx.doi.org/10.3390/app8101803.
Full textAbstract:
The maximum available capacity is an important indicator for determining the State-of-Health (SOH) of a lithium-ion battery. Upon analyzing the experimental results of the cycle life and open circuit voltage tests, a novel health factor which can be used to characterize the maximum available capacity was proposed to predict the battery’s SOH. The health factor proposed contains the features extracted from the terminal voltage drop during the battery rest. In real applications, obtaining such health factor has the following advantages. The battery only needs to have a rest after it is charged or discharged, it is easy to implement. Charging or discharging a battery to a specific voltage rather than a specific state of charge which is difficult to obtain the accurate value, so the health factor has high accuracy. The health factor is not dependent on the cycle number of the cycle life test of the battery and it is less dependent on charging or discharging current rate, as a result, the working conditions have less effect on the health factor. Further, the paper adopted a support vector machine approach to connect the healthy factor to the maximum available battery capacity of the battery. The experimental results show that the proposed method can predict the SOH of the battery well.
12
Surya, Sumukh, Vidya Rao, and Sheldon S. Williamson. "Comprehensive Review on Smart Techniques for Estimation of State of Health for Battery Management System Application." Energies 14, no. 15 (July 30, 2021): 4617. http://dx.doi.org/10.3390/en14154617.
Full textAbstract:
Electric Vehicles (EV) and Hybrid EV (HEV) use Lithium (Li) ion battery packs to drive them. These battery packs possess high specific density and low discharge rates. However, some of the limitations of such Li ion batteries are sensitivity to high temperature and health degradation over long usage. The Battery Management System (BMS) protects the battery against overvoltage, overcurrent etc., and monitors the State of Charge (SOC) and the State of Health (SOH). SOH is a complex phenomenon dealing with the effects related to aging of the battery such as the increase in the internal resistance and decrease in the capacity due to unwanted side reactions. The battery life can be extended by estimating the SOH accurately. In this paper, an extensive review on the effects of aging of the battery on the electrodes, effects of Solid Electrolyte Interface (SEI) deposition layer on the battery and the various techniques used for estimation of SOH are presented. This would enable prospective researchers to address the estimation of SOH with greater accuracy and reliability.
13
Liao, Yuan, Ju Hua Huang, and Qun Zeng. "A Novel Method for Estimating State of Charge of Lithium Ion Battery Packs." Advanced Materials Research 152-153 (October 2010): 428–35. http://dx.doi.org/10.4028/www.scientific.net/amr.152-153.428.
Full textAbstract:
In this paper a novel method for estimating state of charge (SOC) of lithium ion battery packs in battery electric vehicle (BEV), based on state of health (SOH) determination is presented. SOH provides information on aging of battery packs and it declines with repeated charging and discharging cycles of battery packs, so SOC estimation depends considerably on the value of SOH. Previously used SOC estimation methods are not satisfactory as they haven’t given enough attention to the decline of SOH. Therefore a novel SOC estimation method based on SOH determination is introduced in this paper; trying to compensate the deficiency for lack of attention to SOH. Real time road data are used to compare the performance of the conventionally often used Ah counting method which doesn’t give any consideration to SOH with the performance of the proposed SOC estimation method, and better results are obtained by the proposed method in comparison with the conventional method.
14
Venugopal, Prakash, and Vigneswaran T. "State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition." Energies 12, no. 22 (November 14, 2019): 4338. http://dx.doi.org/10.3390/en12224338.
Full textAbstract:
In electric vehicles (EVs), battery management systems (BMS) carry out various functions for effective utilization of stored energy in lithium-ion batteries (LIBs). Among numerous functions performed by the BMS, estimating the state of health (SOH) is an essential and challenging task to be accomplished at regular intervals. Accurate estimation of SOH ensures battery reliability by computing remaining lifetime and forecasting its failure conditions to avoid battery risk. Accurate estimation of SOH is challenging, due to uncertain operating conditions of EVs and complex non-linear electrochemical characteristics demonstrated by LIBs. In most of the existing studies, standard charge/discharge patterns with numerous assumptions are considered to accelerate the battery ageing process. However, such patterns and assumptions fail to reflect the real world operating condition of EV batteries, which is not appropriate for BMS of EVs. In contrast, this research work proposes a unique SOH estimation approach, using an independently recurrent neural network (IndRNN) in a more realistic manner by adopting the dynamic load profile condition of EVs. This research work illustrates a deep learning-based data-driven approach to estimate SOH by analyzing their historical data collected from LIBs. The IndRNN is adapted due to its ability to capture complex non-linear characteristics of batteries by eliminating the gradient problem and allowing the neural network to learn long-term dependencies among the capacity degradations. Experimental results indicate that the IndRNN based model is able to predict a battery’s SOH accurately with root mean square error (RMSE) reduced to 1.33% and mean absolute error (MAE) reduced to 1.14%. The maximum error (MAX) produced by IndRNN throughout the testing process is 2.5943% which is well below the acceptable SOH error range of ±5% for EVs. In addition, to demonstrate effectiveness of the IndRNN attained results are compared with other well-known recurrent neural network (RNN) architectures such as long short-term memory (LSTM) and gated recurrent unit (GRU). From the comparison of results, it is clearly evident that IndRNN outperformed other RNN architectures with the highest SOH accuracy rate.
15
Choi, Woongchul. "A Study on State of Charge and State of Health Estimation in Consideration of Lithium-Ion Battery Aging." Sustainability 12, no. 24 (December 14, 2020): 10451. http://dx.doi.org/10.3390/su122410451.
Full textAbstract:
Due to rapid development of industries around the world, more and more consumption of fossil fuels was unavoidable, resulting in serious environmental problems. The many pollutant emissions—a major contributor to global warming and weather pattern change—have been at the center of concern. In order to solve this issue, research and development of electric vehicles and energy storage systems made great progress and successfully introduced products in the market. Nevertheless, accurate measurement of the state of charge (SOC) and state of health (SOH) of the Li-ion battery, the most popular electric energy storage device, has not yet been fully understood due to the nature of battery aging. In this study, ideas to estimate the capacity and ultimately SOC and SOH of Li-ion batteries are discussed. With these ideas, we expect not only to accommodate the issues with battery aging but also to implement an algorithm for an on-board battery management system. The key idea is to chase and monitor internal resistance continuously in a fast and reliable manner in real time. With further investigation of the key idea, we also fully expect to come up with a reliable SOC and SOH measurement scheme in the near future.
16
Riviere, Elie, Ali Sari, Pascal Venet, Frédéric Meniere, and Yann Bultel. "Innovative Incremental Capacity Analysis Implementation for C/LiFePO4 Cell State-of-Health Estimation in Electrical Vehicles." Batteries 5, no. 2 (April 1, 2019): 37. http://dx.doi.org/10.3390/batteries5020037.
Full textAbstract:
This paper presents a fully embedded state of health (SoH) estimator for widely used C/LiFePO4 batteries. The SoH estimation study was intended for applications in electric vehicles (EV). C/LiFePO4 cells were aged using pure electric vehicle cycles and were monitored with an automotive battery management system (BMS). An online capacity estimator based on incremental capacity analysis (ICA) is developed. The proposed estimator is robust to depth of discharge (DoD), charging current and temperature variations to satisfy real vehicle requirements. Finally, the SoH estimator tuned on C/LiFePO4 cells from one manufacturer was tested on C/LiFePO4 cells from another LFP (lithium iron phosphate) manufacturer.
17
Gismero, Alejandro, Erik Schaltz, and Daniel-Ioan Stroe. "Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage." Energies 13, no. 7 (April 9, 2020): 1811. http://dx.doi.org/10.3390/en13071811.
Full textAbstract:
The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be implemented for all kinds of applications, its accuracy is strongly dependent on the operation conditions. In this work, the behavior of the batteries at different current and temperature conditions is analyzed in order to adjust the charge measurement according to the battery efficiency at the specific operating conditions. The open-circuit voltage (OCV) is used to reset the SOC estimation and prevent the error accumulation. Furthermore, the SOH is estimated by evaluating the accumulated charge between two different SOC using a recursive least squares (RLS) method. The SOC and SOH estimations are verified through an extensive test in which the battery is subjected to a dynamic load profile at different temperatures.
18
Tan, Cher Ming, Preetpal Singh, and Che Chen. "Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries." Applied Sciences 10, no. 21 (November 5, 2020): 7836. http://dx.doi.org/10.3390/app10217836.
Full textAbstract:
Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.
19
Krupp, Amelie, Ernst Ferg, Frank Schuldt, Karen Derendorf, and Carsten Agert. "Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells." Batteries 7, no. 1 (December 30, 2020): 2. http://dx.doi.org/10.3390/batteries7010002.
Full textAbstract:
Incremental capacity analysis (ICA) has proven to be an effective tool for determining the state of health (SOH) of Li-ion cells under laboratory conditions. This paper deals with an outstanding challenge of applying ICA in practice: the evaluation of battery series connections. The study uses experimental aging and characterization data of lithium iron phosphate (LFP) cells down to 53% SOH. The evaluability of battery series connections using ICA is confirmed by analytical and experimental considerations for cells of the same SOH. For cells of different SOH, a method for identifying non-uniform aging states on the modules’ IC curve is presented. The findings enable the classification of battery modules with series and parallel connections based on partial terminal data.
20
Knap, Vaclav, Daniel Auger, Karsten Propp, Abbas Fotouhi, and Daniel-Ioan Stroe. "Concurrent Real-Time Estimation of State of Health and Maximum Available Power in Lithium-Sulfur Batteries." Energies 11, no. 8 (August 16, 2018): 2133. http://dx.doi.org/10.3390/en11082133.
Full textAbstract:
Lithium-sulfur (Li-S) batteries are an emerging energy storage technology with higher performance than lithium-ion batteries in terms of specific capacity and energy density. However, several scientific and technological gaps need to be filled before Li-S batteries will penetrate the market at a large scale. One such gap, which is tackled in this paper, is represented by the estimation of state-of-health (SOH). Li-S batteries exhibit a complex behaviour due to their inherent mechanisms, which requires a special tailoring of the already literature-available state-of-charge (SOC) and SOH estimation algorithms. In this work, a model of SOH based on capacity fade and power fade has been proposed and incorporated in a state estimator using dual extended Kalman filters has been used to simultaneously estimate Li-S SOC and SOH. The dual extended Kalman filter’s internal estimates of equivalent circuit network parameters have also been used to the estimate maximum available power of the battery at any specified instant. The proposed estimators have been successfully applied to both fresh and aged Li-S pouch cells, showing that they can accurately track accurately the battery SOC, SOH, and power, providing that initial conditions are suitable. However, the estimation of the Li-S battery cells’ capacity fade is shown to be more complex, because the practical available capacity varies highly with the applied current rates and the dynamics of the mission profile.
21
Bonfitto, Angelo. "A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks." Energies 13, no. 10 (May 18, 2020): 2548. http://dx.doi.org/10.3390/en13102548.
Full textAbstract:
This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system.
22
Zhang, Tao, Ningyuan Guo, Xiaoxia Sun, Jie Fan, Naifeng Yang, Junjie Song, and Yuan Zou. "A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles." Sustainability 13, no. 9 (May 5, 2021): 5166. http://dx.doi.org/10.3390/su13095166.
Full textAbstract:
Due to its advantages of high voltage level, high specific energy, low self-discharging rate and relatively longer cycling life, the lithium-ion battery has been widely used in electric vehicles. To ensure safety and reduce degradation during the lithium-ion battery’s service life, precise estimation of its states like state of charge (SOC), capacity and peak power is indispensable. This paper proposes a systematic co-estimation framework for the lithium-ion battery in electric vehicle applications. First, a linearized equivalent circuit-based battery model, together with an affine projection algorithm is used to estimate the model parameters. Then the state of health (SOH) estimator is triggered weekly or semi-monthly offline to update capacity based on the three-dimensional response surface open circuit voltage model and particle swarm optimization algorithm for accurate online SOC and state of power (SOP) estimation. At last, the Unscented Kalman Filter utilizes the estimated model parameters and updated capacity to estimate SOC online and the SOP estimator provides the power limitations considering SOC, current and voltage constraints, taking advantage of the information from both SOH and SOC estimators. Experiments show that the relative error of the SOH estimator is under 1% in all aging states whatever the loading profile is. The mean absolute SOC estimation error is under 1.6% even when the battery undergoes 744 aging cycles. The SOP estimator is validated by means of the calibrated battery model based on the HPPC test and its performance is ideal.
23
Liu, Hong Wei, Wen Jing Xu, and Chong Guo. "Study on State of Health Estimation Algorithm for Lithium Power Battery Used on Pure Electric Vehicle." Advanced Materials Research 608-609 (December 2012): 1577–81. http://dx.doi.org/10.4028/www.scientific.net/amr.608-609.1577.
Full textAbstract:
With the continuous development of pure electric vehicles, lithium-ion battery is getting more attention. The SOH is characterized by the capacity of battery charging and discharging, so to meet the national regulations, it is necessary to detect it. This article focuses on estimating the current SOH of the battery based on voltage characterization when charging, and realizes real-time detection of health status in remote monitoring system.
24
Martínez, Jimmy, Jordi-Roger Riba, and Manuel Moreno-Eguilaz. "State of Health Prediction of Power Connectors by Analyzing the Degradation Trajectory of the Electrical Resistance." Electronics 10, no. 12 (June 11, 2021): 1409. http://dx.doi.org/10.3390/electronics10121409.
Full textAbstract:
Estimating the remaining useful life (RUL) or the state of health (SoH) of electrical components such as power connectors is still a challenging and complex task. Power connectors play a critical role in medium- and high-voltage power networks, their failure leading to important consequences such as power outages, unscheduled downtimes, safety hazards or important economic losses. Online condition monitoring strategies allow developing improved predictive maintenance plans. Due to the development of low-cost sensors and electronic communication systems compatible with Internet of Things (IoT) applications, several methods for online and offline SoH determination of diverse power devices are emerging. This paper presents, analyzes and compares the performance of three simple and effective methods for online determination of the SoH of power connectors with low computational requirements. The proposed approaches are based on monitoring the evolution of the connectors’ electrical resistance, which defines the degradation trajectory because the electrical resistance is a reliable indicator or signature of the SoH of the connectors. The methods analyzed in this paper are validated by means of experimental ageing tests emulating real degradation conditions. Laboratory results prove the suitability and feasibility of the proposed approach, which could be applied to other power products and apparatus.
25
Kitchens, Carl. "Health Divided: Public Health and Individual Medicine in the Making of the Modern American State by Daniel Sledge." Journal of Southern History 84, no. 2 (2018): 491–92. http://dx.doi.org/10.1353/soh.2018.0143.
Full text
26
Rastegarpanah, Alireza, Jamie Hathaway, and Rustam Stolkin. "Rapid Model-Free State of Health Estimation for End-Of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy." Energies 14, no. 9 (May 1, 2021): 2597. http://dx.doi.org/10.3390/en14092597.
Full textAbstract:
The continually expanding number of electric vehicles in circulation presents challenges in terms of end-of-life disposal, driving interest in the reuse of batteries for second-life applications. A key aspect of battery reuse is the quantification of the relative battery condition or state of health (SoH), to inform the subsequent battery application and to match batteries of similar capacity. Impedance spectroscopy has demonstrated potential for estimation of state of health, however, there is difficulty in interpreting results to estimate state of health reliably. This study proposes a model-free, convolutional-neural-network-based estimation scheme for the state of health of high-power lithium-ion batteries based on a dataset of impedance spectroscopy measurements from 13 end-of-first-life Nissan Leaf 2011 battery modules. As a baseline, this is compared with our previous approach, where parameters from a Randles equivalent circuit model (ECM) with and without dataset-specific adaptations to the ECM were extracted from the dataset to train a deep neural network refined using Bayesian hyperparameter optimisation. It is demonstrated that for a small dataset of 128 samples, the proposed method achieves good discrimination of high and low state of health batteries and superior prediction accuracy to the model-based approach by RMS error (1.974 SoH%) and peak error (4.935 SoH%) metrics without dataset-specific model adaptations to improve fit quality. This is accomplished while maintaining the competitive performance of the previous model-based approach when compared with previously proposed SoH estimation schemes.
27
Xia, Bizhong, Guanghao Chen, Jie Zhou, Yadi Yang, Rui Huang, Wei Wang, Yongzhi Lai, Mingwang Wang, and Huawen Wang. "Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization." Energies 12, no. 15 (July 31, 2019): 2939. http://dx.doi.org/10.3390/en12152939.
Full textAbstract:
The state of charge (SOC) and the state of health (SOH) are the two most important indexes of batteries. However, they are not measurable with transducers and must be estimated with mathematical algorithms. A precise model and accurate available battery capacity are crucial to the estimation results. An improved speed adaptive velocity particle swarm optimization algorithm (SAVPSO) based on the Thevenin model is used for online parameter identification, which is used with an unscented Kalman filter (UKF) to estimate the SOC. In order to achieve the cyclic update of the SOH, the concept of degree of polarization (DOP) is proposed. The cyclic update of available capacity is thus obtainable to conversely promote the estimation accuracy of the SOC. The estimation experiments in the whole aging process of batteries show that the proposed method can enhance the SOC estimation accuracy in the full battery life cycle with the cyclic update of the SOH, even in cases of operating aged batteries and under complex operating conditions.
28
Lin, Qiongbin, Zhifan Xu, and Chih-Min Lin. "State of health estimation and remaining useful life prediction for lithium-ion batteries using FBELNN and RCMNN." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 10919–33. http://dx.doi.org/10.3233/jifs-201952.
Full textAbstract:
This study proposes the novel method of lithium-ion battery state of health (SoH) estimation and remaining useful life (RUL) prediction to ensure the safety and reliability of the energy storage system. A fuzzy brain emotional learning neural network (FBELNN) is employed to estimate SoH and a recurrent cerebellar model neural network (RCMNN) is used for the RUL prediction. The inputs of FBELNN are extracted features from the monitoring curve of the constant voltage and current, because the lithium-ion battery is seldom completely discharged and the discharging situation in actual operating process is complex. The FBELNN learns the battery aging features that are extracted and selected by discrete wavelet transform and principal component analysis, respectively. The SoH estimation results from the FBELNN are accurate due to the special structure and parameters adaptive laws. The RCMNN and online training again can improve the performance of RUL prediction, because recurrent units can capture the dynamic features. Experimental data are performed by using NASA Prognostics Center of Excellence battery datasets to verify the effectiveness of the proposed method. The results show that the root mean square error of SoH estimation is smaller by the FBELNN and the prediction accuracy of RUL is higher by RCMNN under the different starting points.
29
Ezemobi, Ethelbert, Andrea Tonoli, and Mario Silvagni. "Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine." Energies 14, no. 8 (April 16, 2021): 2243. http://dx.doi.org/10.3390/en14082243.
Full textAbstract:
The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 μs in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications.
30
Rahbari, Omid, Clément Mayet, Noshin Omar, and Joeri Van Mierlo. "Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques." Applied Sciences 8, no. 8 (August 4, 2018): 1301. http://dx.doi.org/10.3390/app8081301.
Full textAbstract:
In this article, two techniques that are congruous with the principle of control theory are utilized to estimate the state of health (SOH) of real-life plug-in hybrid electric vehicles (PHEVs) accurately, which is of vital importance to battery management systems. The relation between the battery terminal voltage curve properties and the battery state of health is modelled via an adaptive neuron-fuzzy inference system and a group method of data handling. The comparison of the results demonstrates the capability of the proposed techniques for accurate SOH estimation. Moreover, the estimated results are compared with the direct actual measured SOH indicators using standard tests. The results indicate that the adaptive neuron-fuzzy inference system with fifteen rules based on a SOH estimator has better performances over the other technique, with a 1.5% maximum error in comparison to the experimental data.
31
Yan, Xiang Wu, Qi Guo, and Heng Bo Xu. "A Novel Method to Estimate the State of Health of each Cell in Battery Pack." Advanced Materials Research 1044-1045 (October 2014): 545–48. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.545.
Full textAbstract:
In order to estimate the state of health (SOH) of each cell in the battery pack, polarization resistance and ohmic resistance were analyzed in the aging process of the battery pack. Accelerating aging test for the battery was done, quantitative relationship between the ohmic resistance and the capacity aging was obtained, a method of relative state of health (RSOH) evaluation was proposed accordingly, Experiments on the LiFePO4 battery pack which is connected in series by 100 cells have been taken, the experimental results show that the evaluation method of RSOH can evaluate the cells SOH accurately and is not limited by the operating conditions.
32
Jia, Jianfang, Jianyu Liang, Yuanhao Shi, Jie Wen, Xiaoqiong Pang, and Jianchao Zeng. "SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators." Energies 13, no. 2 (January 13, 2020): 375. http://dx.doi.org/10.3390/en13020375.
Full textAbstract:
The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy.
33
Pan, Haipeng, Chengte Chen, and Minming Gu. "A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration." Energies 14, no. 16 (August 15, 2021): 5000. http://dx.doi.org/10.3390/en14165000.
Full textAbstract:
Accurately estimating the state of health (SOH) of a lithium-ion battery is significant for electronic devices. To solve the nonlinear degradation problem of lithium-ion batteries (LIB) caused by capacity regeneration, this paper proposes a new LIB degradation model and improved particle filter algorithm for LIB SOH estimation. Firstly, the degradation process of LIB is divided into the normal degradation stage and the capacity regeneration stage. A multi-stage prediction model (MPM) based on the calendar time of the LIB is proposed. Furthermore, the genetic algorithm is embedded into the standard particle filter to increase the diversity of particles and improve prediction accuracy. Finally, the method is verified with the LIB dataset provided by the NASA Ames Prognostics Center of Excellence. The experimental results show that the method proposed in this paper can effectively improve the accuracy of capacity prediction.
34
Li, Peiqing, Huile Wang, Zixiao Xing, Kanglong Ye, and Qipeng Li. "Joint estimation of SOC and SOH for lithium-ion batteries based on EKF multiple time scales." Journal of Intelligent Manufacturing and Special Equipment 1, no. 1 (December 3, 2020): 107–20. http://dx.doi.org/10.1108/jimse-09-2020-0008.
Full textAbstract:
PurposeThe operation state of lithium-ion battery for vehicle is unknown and the remaining life is uncertain. In order to improve the performance of battery state prediction, in this paper, a joint estimation method of state of charge (SOC) and state of health (SOH) for lithium-ion batteries based on multi-scale theory is designed.Design/methodology/approachIn this paper, a joint estimation method of SOC and SOH for lithium-ion batteries based on multi-scale theory is designed. The venin equivalent circuit model and fast static calibration method are used to fit the relationship between open-circuit voltage and SOC, and the resistance and capacitance parameters in the model are identified based on exponential fitting method. A battery capacity model for SOH estimation is established. A multi-time scale EKF filtering algorithm is used to estimate the SOC and SOH of lithium-ion batteries.FindingsThe SOC and SOH changes in dynamic operation of lithium-ion batteries are accurately predicted so that batteries can be recycled more effectively in the whole vehicle process.Originality/valueA joint estimation method of SOC and SOH for lithium-ion batteries based on multi-scale theory is accurately predicted and can be recycled more effectively in the whole vehicle process.
35
Lee, Park, and Kim. "Incremental Capacity Curve Peak Points-Based Regression Analysis for the State-of-Health Prediction of a Retired LiNiCoAlO2 Series/Parallel Configured Battery Pack." Electronics 8, no. 10 (October 4, 2019): 1118. http://dx.doi.org/10.3390/electronics8101118.
Full textAbstract:
To recycle retired series/parallel battery packs, it is necessary to know their state-of-health (SOH) correctly. Unfortunately, voltage imbalances between the cells occur repeatedly during discharging/charging. The voltage ranges for the discharge/charge of a retired series/parallel battery pack are reduced owing to the voltage imbalances between the cells. To determine the accurate SOH of a retired series/parallel battery pack, it is necessary to calculate the total discharge capacity using fully discharging/charging tests. However, a fully discharging/charging test is impossible owing to the reduced voltage range. The SOH of a retired series/parallel battery pack with a voltage imbalance should be estimated within the reduced discharging/charging voltage range. This paper presents a regression analysis of the peak point in the incremental capacity (IC) curve from the fresh state to a 100-cycle aging state. Moreover, the SOH of the considered retired series/parallel battery pack was estimated using a regression analysis model. The error in the SOHs of the retired series/parallel battery pack and linear regression analysis model was within 1%, and hence a good accuracy is achieved.
36
Zhou, Di, Hongtao Yin, Ping Fu, Xianhua Song, Wenbin Lu, Lili Yuan, and Zuoxian Fu. "Prognostics for State of Health of Lithium-Ion Batteries Based on Gaussian Process Regression." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/8358025.
Full textAbstract:
Accurate estimation and prediction of the lithium-ion (Li-ion) batteries’ performance has important theoretical and practical significance to make better use of lithium-ion battery and to avoid unnecessary losses. State of health (SOH) estimation is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system. To evaluate and predict the SOH of batteries, the Gaussian process regression with neural network (GPRNN) as its variance function is proposed. Experimental results confirm that the proposed method can be effectively applied to Li-ion battery monitoring and prognostics by quantitative comparison with basic GPR, combination LGPFR, combination QGPFR, and the multiscale GPR (SMK-GPR, P-MGPR, and SE-MGPR). The criteria of RMSE and MAPE of the proposed three models are reduced significantly compared to those of other existing methods.
37
Jiang, Shida, and Zhengxiang Song. "Estimating the State of Health of Lithium-Ion Batteries with a High Discharge Rate through Impedance." Energies 14, no. 16 (August 8, 2021): 4833. http://dx.doi.org/10.3390/en14164833.
Full textAbstract:
Lithium-ion batteries are an attractive power source in many scenarios. In some particular cases, including providing backup power for drones, frequency modulation, and powering electric tools, lithium-ion batteries are required to discharge at a high rate (2~20 C). In this work, we present a method to estimate the state of health (SOH) of lithium-ion batteries with a high discharge rate using the battery’s impedance at three characteristic frequencies. Firstly, a battery model is used to fit the impedance spectrum of twelve LiFePO4 batteries. Secondly, a basic estimation model is built to estimate the SOH of the batteries via the parameters of the battery model. The model is trained using the data of six batteries and is tested on another six. The RMS of relative error of the model is lower than 4.2% at 10 C and lower than 2.8% at 15 C, even when the low-frequency feature of the impedance spectrum is ignored. Thirdly, we adapt the basic model so that the SOH estimation can be performed only using the battery’s impedance at three characteristic frequencies without having to measure the entire impedance spectrum. The RMS of relative error of this adapted model at 10 C and 15 C is 3.11% and 4.25%, respectively.
38
Liu, Zhengyu, Jingjie Zhao, Hao Wang, and Chao Yang. "A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs." Energies 13, no. 4 (February 14, 2020): 830. http://dx.doi.org/10.3390/en13040830.
Full textAbstract:
An accurate lithium-ion battery state of health (SOH) estimate is a key factor in guaranteeing the reliability of electronic equipment. This paper proposes a new method that is based on an indirect enhanced health indicator (HI) and uses support vector regression (SVR) to estimate SOH values. First, three original features that can describe the dynamic changes of the battery charging and discharging processes are extracted. Considering the coupling relationship between pairs of the original health indicators, we use the differential evolution (DE) algorithm to optimize their corresponding feature parameters and combine them to form an enhanced health indicator. Second, this paper modifies the kernel function of the SVR model to describe the trend of SOH as the number of cycles increases, with simultaneous hyperparameters optimization via DE algorithm. Third, the proposed model and other published methods are compared in terms of accuracy on the same NASA datasets. We also evaluated the generalization performance of the model in dynamic discharging experiments. The simulation results demonstrate that the proposed method can provide more accurate SOH estimation values.
39
Park, Jinhyeong, Munsu Lee, Gunwoo Kim, Seongyun Park, and Jonghoon Kim. "Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH." Energies 13, no. 9 (April 29, 2020): 2138. http://dx.doi.org/10.3390/en13092138.
Full textAbstract:
To enhance the efficiency of an energy storage system, it is important to predict and estimate the battery state, including the state of charge (SOC) and state of health (SOH). In general, the statistical approaches for predicting the battery state depend on historical data measured via experiments. The statistical methods based on experimental data may not be suitable for practical applications. After reviewing the various methodologies for predicting the battery capacity without measured data, it is found that a joint estimator that estimates the SOC and SOH is needed to compensate for the data shortage. Therefore, this study proposes an integrated model in which the dual extended Kalman filter (DEKF) and autoregressive (AR) model are combined for predicting the SOH via a statistical model in cases where the amount of measured data is insufficient. The DEKF is advantageous for estimating the battery state in real-time and the AR model performs better for predicting the battery state using previous data. Because the DEKF has limited performance for capacity estimation, the multivariate AR model is employed and a health indicator is used to enhance the performance of the prediction model. The results of the multivariate AR model are significantly better than those obtained using a single variable. The mean absolute percentage errors are 1.45% and 0.5183%, respectively.
40
Bhagavatula, Sai Vasudeva, Venkata Rupesh Bharadwaj Yellamraju, Karthik Chandra Eltem, Phaneendra Babu Bobba, and Naveenkumar Marati. "ANN based Battery Health Monitoring - A Comprehensive Review." E3S Web of Conferences 184 (2020): 01068. http://dx.doi.org/10.1051/e3sconf/202018401068.
Full textAbstract:
The development of electric vehicles has bought a great revolution in the field of battery management as it deals with the health of the battery and also the protection of the battery. State of Charge (SoC) and State of Health (SoH) are the important parameters in determining the battery’s health. Advancements in Artificial Neural Networks and Machine Learning, a growing field in recent years has bought many changes in estimating these parameters. Access to huge battery data has become very advantageous to these methods. This manuscript presents an overview of different Artificial Neural Network techniques like Feedforward Neural Network (FNN), Extreme Learning Machine (ELM), and the Long Short Term Memory (LSTM). These techniques are trained with already existing data samples consisting of different values of voltages, currents at different temperatures with different charging cycles and epochs. The errors in each technique are different from the other as the constraints in one method are rectified using the other method to get the least error percentage and get the nearest estimate of the SoC and SOH. Each method needs to be trained for several epochs. This manuscript also presents a comparison of different methods with input parameters and error percentages.
41
Khaleghi, Sahar, Yousef Firouz, Maitane Berecibar, Joeri Van Mierlo, and Peter Van Den Bossche. "Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features." Energies 13, no. 5 (March 9, 2020): 1262. http://dx.doi.org/10.3390/en13051262.
Full textAbstract:
The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.
42
Kurzweil, Peter, and Wolfgang Scheuerpflug. "State-of-Charge Monitoring and Battery Diagnosis of NiCd Cells Using Impedance Spectroscopy." Batteries 6, no. 1 (January 9, 2020): 4. http://dx.doi.org/10.3390/batteries6010004.
Full textAbstract:
With respect to aeronautical applications, the state-of-charge (SOC) and state-of-health (SOH) of rechargeable nickel–cadmium batteries was investigated with the help of the frequency-dependent reactance Im Z(ω) and the pseudo-capacitance C(ω) in the frequency range between 1 kHz and 0.1 Hz. The method of SOC monitoring using impedance spectroscopy is evaluated with the example of 1.5-year long-term measurements of commercial devices. A linear correlation between voltage and capacitance is observed as long as overcharge and deep discharge are avoided. Pseudo-charge Q(ω) = C(ω)⋅U at 1 Hz with respect to the rated capacity is proposed as a reliable SOH indicator for rapid measurements. The benefit of different evaluation methods and diagram types for impedance data is outlined.
43
Zhang, Sihan, Md Sazzad Hosen, Theodoros Kalogiannis, Joeri Van Mierlo, and Maitane Berecibar. "State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network." World Electric Vehicle Journal 12, no. 3 (September 15, 2021): 156. http://dx.doi.org/10.3390/wevj12030156.
Full textAbstract:
The global electric vehicle (EV) is expanding enormously, foreseeing a 17.4% increase in compound annual growth rate (CAGR) by the end of 2027. The lithium-ion battery is considered as the most widely used battery technology in EV. The accurate and reliable diagnostic and prognostic of battery state guarantees the safe operation of EV and is crucial for durable electric vehicles. Research focusing on lithium-ion battery life degradation has grown more important in recent years. In this study, a model built for state of health (SoH) estimation for the LTO anode-based lithium-ion battery is presented. First, electrochemical impedance spectroscopy (EIS) is used to study the deterioration in battery performance, measurements such as charge transfer resistance and ohmic resistance are analyzed for different operational conditions and selected as key characteristic parameters for the model. Then, the model based on a backpropagation neural network (BPNN) along with the characteristic parameters is trained and validated with a real-life driving profile. The model shows a relatively accurate estimation of SoH with a mean-squared-error (MSE) of 0.002.
44
Kwon, Sanguk, Dongho Han, Seongyun Park, and Jonghoon Kim. "Long Short Term Memory-Based State-of-Health Prediction Algorithm of a Rechargeable Lithium-Ion Battery for Electric Vehicle." Transactions of The Korean Institute of Electrical Engineers 68, no. 10 (October 31, 2019): 1214–21. http://dx.doi.org/10.5370/kiee.2019.68.10.1214.
Full text
45
Song, Shuxiang, Chen Fei, and Haiying Xia. "Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction." Energies 13, no. 4 (February 13, 2020): 812. http://dx.doi.org/10.3390/en13040812.
Full textAbstract:
SOH (state of health) estimation is important for battery management. Since the electrochemical reaction inside LIBS (lithium-ion battery system) is extremely complex and the external working environment is uncertain, it is difficult to achieve accurate determination of SOH. To improve the accuracy of SOH estimation, we propose a SOH estimation method for lithium-ion battery based on XGBoost algorithm with accuracy correction. We extract several features, including average voltage, voltage difference, current difference, and temperature difference, to describe the aging process of batteries. Due to the higher prediction accuracy and generalization ability of ensemble learning algorithm, the XGBoost model is established to estimate the SOH of lithium-ion battery. Then, the estimation values are corrected by Markov chain. Compared with the methods by XGBoost, random forest, k-nearest neighbor algorithm (KNN), SVM, linear regression, our proposed method shows an accuracy improvement by 10% to 20%. Additionally, the errors of our method are also superior to the others in terms of the average absolute error, root mean square error, and root mean square error.
46
Wang, Yuefei, Fei Huang, Bin Pan, Yang Li, and Baijun Liu. "Augmented system model-based online collaborative determination of lead–acid battery states for energy management of vehicles." Measurement and Control 54, no. 1-2 (January 2021): 88–101. http://dx.doi.org/10.1177/0020294020983376.
Full textAbstract:
State of charge (SOC) and state of health (SOH) of batteries are the indispensable control decision variables for online energy management system (EMS) in modern internal combustion engine vehicles. The real-time and accurate determination of SOC and SOH is essential to the reliability and safety of EMS operation. Obtaining good accuracy for the SOC estimation is difficult without considering SOH because of their coupling relationship. Although several works on the joint estimation of SOC and SOH of lithium–ion batteries are available, these studies cannot be applied to lead–acid batteries because of the differences in physical structure and characteristics. This study handles the problem of modeling the relationship between SOC and SOH of lead–acid battery and their online collaborative estimation. First, the structure and control strategy of a bus-based EMS is discussed, and the improper energy control actions of EMS due to the inaccurate SOC estimation are analyzed. Second, an instantaneous correlation factor β for SOC and SOH is defined as a new state estimating variable, and the simplified linear relationship model between β and open circuit voltage is established through the battery experiments. Third, a discretized augmented system equation of β is deduced according to the relationship model and the Randles circuit model. The least square circuit parameter identification (LSCPI) algorithm is presented to identify the time-varying circuit model parameters, while the adaptive Kalman filter for augmented system (AKFAS) algorithm is employed to estimate β online. A collaborative estimation algorithm is proposed on the basis of the LSCPI and AKFAS to determine SOC and SOH of lead–acid battery in real time, and a demo intelligent battery sensor is developed for its implementation. The results of battery charging and discharging experiments indicate that the proposed method has high accuracy. The estimation accuracy of SOC of this method reaches 3.13%, which is 7% higher than that of the existing method.
47
Zeng, Miaomiao, Peng Zhang, Yang Yang, Changjun Xie, and Ying Shi. "SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm." Energies 12, no. 16 (August 14, 2019): 3122. http://dx.doi.org/10.3390/en12163122.
Full textAbstract:
In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor (RC) equivalent circuit model established and an online parameter identification used by Bayes. Ohmic resistance is treated as a battery state of health (SOH) characteristic parameter, F-UKF algorithms are used for the joint estimation of battery state of charge (SOC) and SOH. The experimental data obtained from the ITS5300-based battery test platform are adopted for the simulation verification under discharge conditions with constant-current pulses and urban dynamometer driving schedule (UDDS) conditions in the MATLAB environment. The experimental results show that the F-UKF algorithm is insensitive to the initial value of the SOC under discharge conditions with constant-current pulses, and the SOC and SOH estimation accuracy under UDDS conditions reaches 1.76% and 1.61%, respectively, with the corresponding convergence time of 120 and 140 s, which proves the superiority of the joint estimation algorithm.
48
Wang, YaNan, YangQuan Chen, and XiaoZhong Liao. "State-of-art survey of fractional order modeling and estimation methods for lithium-ion batteries." Fractional Calculus and Applied Analysis 22, no. 6 (December 18, 2019): 1449–79. http://dx.doi.org/10.1515/fca-2019-0076.
Full textAbstract:
Abstract This paper presents a state-of-art survey of the research on fractional-order (FO) modeling with parameter identification, and FO estimation methods for state of charge (SOC), state of health (SOH), and remaining usage life (RUL) of lithium-ion batteries (LIBs) mainly in recent five years. FO electrochemical models and six different types of FO equivalent circuit models (ECMs) are introduced in detail. Then, the corresponding tuning algorithm for parameters of these FO models are also provided in brief. Moreover, FO estimation methods for SOC are listed and analyzed, mainly including FO observers, and FO Kalman filters (FO-KFs). SOH and RUL estimation is another vital aspect for LIBs ageing and degradation monitoring, thus FO estimation methods proposed in recent research within five years are all listed. Finally, some suggestions that may be helpful for further research are proposed in conclusion.
49
Qian, Kun, Binhua Huang, Aihua Ran, Yan-Bing He, Baohua Li, and Feiyu Kang. "State-of-health (SOH) evaluation on lithium-ion battery by simulating the voltage relaxation curves." Electrochimica Acta 303 (April 2019): 183–91. http://dx.doi.org/10.1016/j.electacta.2019.02.055.
Full text
50
Komsiyska, Lidiya, Sergio A. Garnica Barragan, Meinert Lewerenz, Daniela Ledwoch, and Oliver Osters. "Detecting Aging Phenomena in Commercial Cathodes for Li-Ion Batteries Using High Resolution Computed Tomography." Advances in Science and Technology 93 (October 2014): 158–63. http://dx.doi.org/10.4028/www.scientific.net/ast.93.158.
Full textAbstract:
Using high resolution computed tomography (CT) the change of the morphometric parameters in depth of electrodes for lithium ion batteries with aging has been examined. Commercially available 2 Ah Li-ion cells were continuously cycled to different state of health (SOH). The cathodes were subsequently analyzed using CT with voxel size resolution of about 400 nm. For a quantitative analysis binarized images were evaluated and various properties such as the size distribution of active particles analyzed. Using this technique a decrease in the average particle size and an increase in number of particles of LiCoO2 with decreasing SOH of the battery is confirmed experimentally for the first time.