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Journal articles on the topic 'State of charge estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Bai, Wenyuan, Xinhui Zhang, Zhen Gao, Shuyu Xie, Ke Peng, and Yu Chen. "Sensorless Coestimation of Temperature and State-of-Charge for Lithium-Ion Batteries Based on a Coupled Electrothermal Model." International Journal of Energy Research 2023 (February 6, 2023): 1–18. http://dx.doi.org/10.1155/2023/4021256.

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Accurate estimations of the temperature and the state-of-charge (SOC) are of extreme importance for the safety of lithium-ion battery operation. Traditional battery temperature and SOC estimation methods often omit the relation between battery temperature and SOC, which may lead to significant errors in the estimations. This study presents a coupled electrothermal battery model and a coestimation method for simultaneously estimating the temperature and SOC of lithium-ion batteries. The coestimation method is performed by a coupled model-based dual extended Kalman filter (DEKF). The coupled est

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2

Vinh, Thuy Nguyen, Chi Nguyen Van, and Vy Nguyen Van. "State-augmented adaptive sliding-mode observer for estimation of state of charge and measurement fault in lithium-ion batteries." International Journal of Applied Power Engineering (IJAPE) 14, no. 2 (2025): 291. https://doi.org/10.11591/ijape.v14.i2.pp291-299.

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Estimating the state of charge (SoC) in lithium-ion batteries (LiB) encounters challenges due to model uncertainties and sensor measurement errors. To solve this issue, this study introduces an estimator based on an innovative adaptive augmented sliding mode approach. This approach incorporates measurement faults as additional state variables to minimize the impacts of uncertainties effectively. Furthermore, based on the sliding mode framework, the design of this estimator addresses resistance to model uncertainties. However, sliding estimators commonly face the chattering issue. To counteract

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3

Yoo, Min Young, Jung Heon Lee, Joo-Ho Choi, Jae Sung Huh, and Woosuk Sung. "State-of-Charge Estimation of Batteries for Hybrid Urban Air Mobility." Aerospace 10, no. 6 (2023): 550. http://dx.doi.org/10.3390/aerospace10060550.

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This paper proposes a framework for accurately estimating the state-of-charge (SOC) and current sensor bias, with the aim of integrating it into urban air mobility (UAM) with hybrid propulsion. Considering the heightened safety concerns in an airborne environment, more reliable state estimation is required, particularly for the UAM that uses a battery as its primary power source. To ensure the suitability of the framework for the UAM, a two-pronged approach is taken. First, realistic test profiles, reflecting actual operational scenarios for the UAM, are used to model the battery and validate

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4

Chang, Wen-Yeau. "The State of Charge Estimating Methods for Battery: A Review." ISRN Applied Mathematics 2013 (July 23, 2013): 1–7. http://dx.doi.org/10.1155/2013/953792.

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An overview of new and current developments in state of charge (SOC) estimating methods for battery is given where the focus lies upon mathematical principles and practical implementations. As the battery SOC is an important parameter, which reflects the battery performance, so accurate estimation of SOC cannot only protect battery, prevent overcharge or discharge, and improve the battery life, but also let the application make rationally control strategies to achieve the purpose of saving energy. This paper gives a literature survey on the categories and mathematical methods of SOC estimation

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5

Li, Shuo, Song Li, Haifeng Zhao, and Yuan An. "Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system." International Journal of Distributed Sensor Networks 15, no. 12 (2019): 155014771989452. http://dx.doi.org/10.1177/1550147719894526.

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In this article, a method for estimating the state of charge of lithium battery based on back-propagation neural network is proposed and implemented for uninterruptible power system. First, back-propagation neural network model is established with voltage, temperature, and charge–discharge current as input parameters, and state of charge of lithium battery as output parameter. Then, the back-propagation neural network is trained by Levenberg–Marquardt algorithm and gradient descent method; and the state of charge of batteries in uninterruptible power system is estimated by the trained back-pro

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6

Lai, Xin, Ming Yuan, Xiaopeng Tang, et al. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing." Energies 15, no. 19 (2022): 7416. http://dx.doi.org/10.3390/en15197416.

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State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is the basis of other state estimations. However, its accuracy can be affected by many factors, such as temperature and ageing. To handle this bottleneck issue, we here propose a joint SOC-SOH estimation method considering the influence of the temperature. It combines the Forgetting Factor Recursive Least Squares (FFRLS) algorithm, Total Least Squares (TLS) algorithm, and Unscented Kalman Filter (UKF) algorithm. First, the FFRLS algorithm is used to identify and update the parameters of the equivalent circuit model in real time u

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7

Asghar, Furqan, Muhammad Talha, Sung Ho Kim, and In-Ho Ra. "Simulation Study on Battery State of Charge Estimation Using Kalman Filter." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 6 (2016): 861–66. http://dx.doi.org/10.20965/jaciii.2016.p0861.

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Low power dissipation and maximum battery run-time are crucial in portable electronics and EV’s. Battery characteristics and performance varied at different operating conditions. By using accurate, efficient circuit and battery models, designers can predict and optimize battery runtime, current state of charge (SOC) and circuit performance. A great factor in determining the stability of battery system lies within the state of charge estimation. Failing to predict SOC will cause overcharge or over discharge which potentially will bring permanent damage to the battery cells. Open circuit voltage

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8

Figueroa-Santos, Miriam A., Jason B. Siegel, and Anna G. Stefanopoulou. "Leveraging Cell Expansion Sensing in State of Charge Estimation: Practical Considerations." Energies 13, no. 10 (2020): 2653. http://dx.doi.org/10.3390/en13102653.

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Measurements such as current and terminal voltage that are typically used to determine the battery’s state of charge (SOC) are augmented with measured force associated with electrode expansion as the lithium intercalates in its structure. The combination of the sensed behavior is shown to improve SOC estimation even for the lithium ion iron phosphate (LFP) chemistry, where the voltage–SOC relation is flat (low slope) making SOC estimation using measured voltage difficult. For the LFP cells, the measured force has a non-monotonic F–SOC relationship. This presents a challenge for estimation as m

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9

Martí-Florences, Miquel, Piñol Andreu Cecilia, Alejandro Clemente, and Ramon Costa-Castelló. "SoC Estimation in Lithium-Ion Batteries with Noisy Measurements and Absence of Excitation." Batteries 9, no. 12 (2023): 578. https://doi.org/10.3390/batteries9120578.

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Accurate State-of-Charge estimation is crucial for applications that utilise lithium-ion batteries. In real-time scenarios, battery models tend to present significant uncertainty, making it desirable to jointly estimate both the State of Charge and relevant unknown model parameters. However, parameter estimation typically necessitates that the battery input signals induce a persistence of excitation property, a need which is often not met in practical operations. This document introduces a joint state of charge/parameter estimator that relaxes this stringent requirement. This estimator is base

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10

El Maliki, Anas, Abdessamad Benlafkih, Kamal Anoune, and Abdelkader Hadjoudja. "Reduce state of charge estimation errors with an extended Kalman filter algorithm." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 57. http://dx.doi.org/10.11591/ijece.v14i1.pp57-65.

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Li-ion batteries (LiBs) are accurately estimated under varying operating conditions and external influences using extended Kalman filtering (EKF). Estimating the state of charge (SOC) is essential for enhancing battery efficiency, though complexities and unpredictability present obstacles. To address this issue, the paper proposes a second-order resistance-capacitance (RC) battery model and derives the EKF algorithm from it. The EKF approach is chosen for its ability to handle complex battery behaviors. Through extensive evaluation using a Simulink MATLAB program, the proposed EKF algorithm de

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11

Turkmanović, Haris, Ivan Popović, and Vladimir Rajović. "Toward Energy Efficient Battery State of Charge Estimation on Embedded Platforms." Electronics 13, no. 21 (2024): 4256. http://dx.doi.org/10.3390/electronics13214256.

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Recent studies have focused on accuracy as the key state of charge (SoC) estimation algorithms’ performance metrics, whereas just a few of them compare algorithms in terms of energy efficiency. Such a comparison is important when selecting an algorithm that should be implemented on a resource-constrained, low-power embedded system. In this paper, recursive model-based SoC estimation algorithms, such as the extended Kalman filter, have been identified as well-suited solutions for implementation on an embedded platform, providing a good compromise between estimation accuracy and computational co

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12

Feng, Juqiang, Long Wu, Kaifeng Huang, Xing Zhang, and Jun Lu. "State-of-charge Estimation of Lithium-ion Battery Based Online Parameter Identification." E3S Web of Conferences 194 (2020): 02023. http://dx.doi.org/10.1051/e3sconf/202019402023.

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Accurately estimating the state of charge (SOC) of lithium-ion is very important to improving the dynamic performance and energy utilization efficiency. In order to reduce the influence of model parameters and system coloured noise on SOC estimation accuracy, this paper proposes the SOC estimation based on online identification. Based on the mixed simplified electrochemical model, the forgetting factor recursive least squares (FFRLS) method was used to identify the parameters online, and the SOC estimation was carried out in combination with Unscented Kalman Filter (UKF). Finally, the accuracy

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13

Martí-Florences, Miquel, Andreu Cecilia Piñol, Alejandro Clemente, and Ramon Costa-Castelló. "SoC Estimation in Lithium-Ion Batteries with Noisy Measurements and Absence of Excitation." Batteries 9, no. 12 (2023): 578. http://dx.doi.org/10.3390/batteries9120578.

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Accurate State-of-Charge estimation is crucial for applications that utilise lithium-ion batteries. In real-time scenarios, battery models tend to present significant uncertainty, making it desirable to jointly estimate both the State of Charge and relevant unknown model parameters. However, parameter estimation typically necessitates that the battery input signals induce a persistence of excitation property, a need which is often not met in practical operations. This document introduces a joint state of charge/parameter estimator that relaxes this stringent requirement. This estimator is base

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14

Xu, Peipei, Junqiu Li, Chao Sun, Guodong Yang, and Fengchun Sun. "Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation." Electronics 10, no. 2 (2021): 122. http://dx.doi.org/10.3390/electronics10020122.

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The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this pa

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15

Xu, Peipei, Junqiu Li, Chao Sun, Guodong Yang, and Fengchun Sun. "Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation." Electronics 10, no. 2 (2021): 122. http://dx.doi.org/10.3390/electronics10020122.

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The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this pa

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16

Kamenev, Yurii Borisovich, Georgii Alekseevich Shtompel', and Yurii Vasil'evich Skachkov. "Accelerated charge method of the lead-acid batteries. 2. Constant current charge." Electrochemical Energetics 13, no. 2 (2013): 70–76. http://dx.doi.org/10.18500/1608-4039-2013-13-2-70-76.

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Strategy of the accelerated mode of the charge of the lead-acid batteries, including constant current charge to 80% state-of-charge and a pulse charge to 100% is considered. Various modes one — and two-stage constant current charges are studied in this work, and the estimation of influence of these modes on effectiveness ratio of a charge and a heat-up of batteries is yielded. The data, which allows reasonably choose the mode of the first stage of the accelerated charge to 80% state-of-charge, are presented.

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17

Li, Hongzhao, Hongsheng Jia, Ping Xiao, Haojie Jiang, and Yang Chen. "Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles." Energies 18, no. 9 (2025): 2144. https://doi.org/10.3390/en18092144.

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Accurately estimating the State of Charge (SOC) of power batteries is crucial for the Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, energy management efficiency, and the safety and lifespan of the battery. However, SOC cannot be measured directly with instruments; it needs to be estimated using external parameters such as current, voltage, and internal resistance. Moreover, power batteries represent complex nonlinear time-varying systems, and various uncertainties—like battery aging, fluctuations in ambient temperature, and

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18

Gruosso, Giambattista, Giancarlo Storti Gajani, Fredy Ruiz, Juan Diego Valladolid, and Diego Patino. "A Virtual Sensor for Electric Vehicles’ State of Charge Estimation." Electronics 9, no. 2 (2020): 278. http://dx.doi.org/10.3390/electronics9020278.

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The estimation of the state of charge is a critical function in the operation of electric vehicles. The battery management system must provide accurate information about the battery state, even in the presence of failures in the vehicle sensors. This article presents a new methodology for the state of charge estimation (SOC) in electric vehicles without the use of a battery current sensor, relying on a virtual sensor, based on other available vehicle measurements, such as speed, battery voltage and acceleration pedal position. The estimator was derived from experimental data, employing support

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19

Li, Boyu, Yueyue Ma, Ren Zhu, and Heng Li. "State-of-Charge Estimation of Reconfigurable Supercapacitor." Journal of Physics: Conference Series 2774, no. 1 (2024): 012089. http://dx.doi.org/10.1088/1742-6596/2774/1/012089.

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Abstract A large body of research has been done on state-of-charge (SOC) estimate of supercapacitors in the literature; most of the works have been on SOC estimation of individual supercapacitor units. Nonetheless, supercapacitors are usually linked to balancing circuits in real-world applications in order to remove inter-cell imbalances. The cells’ system dynamics shift to a new mode when the balancing circuit is turned on, making the SOC calculation techniques that are currently in use inappropriate. Our proposal in this research is a Kalman filtering-based reconfigurable supercapacitor bala

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20

Zhang, Tao, Ningyuan Guo, Xiaoxia Sun, et al. "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 (2021): 5166. http://dx.doi.org/10.3390/su13095166.

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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

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21

Chen, Dong Zhao, and Li Jun Jia. "Estimation of Lead-Acid Battery SOC Based on Kalman Filtering Algorithm." Applied Mechanics and Materials 651-653 (September 2014): 1064–67. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1064.

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The key technology of electric vehicle battery management lies in the battery state of charge (SOC) estimation, accurate and efficient estimation of SOC can provide the reference data for the control system of the electric vehicle in time. On the basis of the Ah measurement method, Calman filtering method, open circuit voltage method to estimate method, the estimation procedure to estimate the charged battery optimized Calman filtering method, effectively shorten the calculation process, improve the estimation accuracy, in the normal operation of electric vehicles under the influence of compre

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22

Arahal, Manuel R., Alfredo Pérez Vega-Leal, Manuel G. Satué, and Sergio Esteban. "Assessing SOC Estimations via Reverse-Time Kalman for Small Unmanned Aircraft." Energies 17, no. 20 (2024): 5161. http://dx.doi.org/10.3390/en17205161.

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This paper presents a method to validate state of charge (SOC) estimations in batteries for their use in remotely manned aerial vehicles (UAVs). The SOC estimation must provide the mission control with a measure of the available range of the aircraft, which is critical for extended missions such as search and rescue operations. However, the uncertainty about the initial state and depth of discharge during the mission makes the estimation challenging. In order to assess the estimation provided to mission control, an a posteriori re-estimation is performed. This allows for the assessment of esti

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23

Zang, Weihong, Facheng Wang, Zhonghua Li, and Wei Zhou. "Battery State Estimation based on Dual Extended Kalman Filtering with Fixed Step." Journal of Physics: Conference Series 2200, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2200/1/012023.

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Abstract With the rapid popularization of new energy vehicles, users pursue reliable mileage as well as stable and efficient power battery charging and discharging performance, which puts forward higher requirements for on-board battery management system (BMS). Realizing online update of battery model parameters and accurate estimation of charged state has also become one of the key technical problems in the field of new energy vehicles at present. In this paper, based on the second-order resistor-capacitor (RC) equivalent circuit model, multiple parameters in the voltage relaxation stage were

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24

Muhammad Adib Kamali and Wansu Lim. "ANN-based State of Charge Estimation of Li-ion Batteries for Embedded Applications." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 12, no. 2 (2023): 85–92. http://dx.doi.org/10.22146/jnteti.v12i2.6632.

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The conventional state of charge (SOC) estimation model has several concerns, such as accuracy and reliability. In order to realize robust SOC estimation for embedded applications, this study focuses on three concerns of the existing SOC estimation model: accuracy, robustness, and practicality. In improving the estimation accuracy and robustness, this study took into account the dynamic of the actual SOC caused by the dynamic charging and discharging process. In practice, the charging and discharging processes have characteristics that must be considered to realize robust SOC estimation. The m

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Jung, Hyeonhong, and Seongjun Lee. "A Study on Capacity and State of Charge Estimation of VRFB Systems Using Cumulated Charge and Electrolyte Volume under Rebalancing Conditions." Energies 16, no. 5 (2023): 2478. http://dx.doi.org/10.3390/en16052478.

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Extensive research has been conducted on energy storage systems (ESSs) for efficient power use to mitigate the problems of environmental pollution and resource depletion. Various batteries such as lead-acid batteries, lithium batteries, and vanadium redox flow batteries (VRFBs), which have longer life spans and better fire safety, have been actively researched. However, VRFBs undergo capacity reduction due to electrolyte crossover. Additionally, research on the capacity and state of charge (SOC) estimation for efficient energy management, safety, and life span management of VRFBs has been perf

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26

Movassagh, Kiarash, Arif Raihan, Balakumar Balasingam, and Krishna Pattipati. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries." Energies 14, no. 14 (2021): 4074. http://dx.doi.org/10.3390/en14144074.

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In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge are known. The Coulomb counting approach, on the other hand, is prone to inaccuracies from a variety of sources, and the magnitude of these errors has not been explored in the literature. We formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources: (1) current

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27

Sun, Huan Huan, Jun Bi, and Sai Shao. "The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter." Advanced Materials Research 953-954 (June 2014): 796–99. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.796.

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Accurate estimation of battery state of charge (SOC) is important to ensure operation of electric vehicle. Since a nonlinear feature exists in battery system and extended kalman filter algorithm performs well in solving nonlinear problems, the paper proposes an EKF-based method for estimating SOC. In order to obtain the accurate estimation of SOC, this paper is based on composite battery model that is a combination of three battery models. The parameters are identified using the least square method. Then a state equation and an output equation are identified. All experimental data are collecte

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Kaleli, Ali Rıza, and Alptekin Türkkan. "ESTIMATION OF ELECTRIC VEHICLE MULTIPLE PACKAGE LITHIUM-ION BATTERY STATE OF CHARGE USING AN EXTENDED MACHINE LEARNING." Uludağ University Journal of The Faculty of Engineering 30, no. 1 (2025): 231–44. https://doi.org/10.17482/uumfd.1595646.

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The estimation of battery state-of-charge (SOC) in electric or hybrid vehicle has vital importance in the designing process of battery management systems. The state-of-charge estimation is implemented using different modelling approaches, model-based estimators such as Kalman filtering and Luenberger observer and data-driven based modelling techniques like artificial neural network and machine learning methods. This study aimed to develop a battery state-of-charge estimation method and proposed a novel architecture for multiple battery back SOC estimation using an extended learning machine (EL

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29

Omiloli, Koto, Ayokunle Awelewa, Isaac Samuel, Oghorchukwuyem Obiazi, and James Katende. "State of charge estimation based on a modified extended Kalman filter." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5054. http://dx.doi.org/10.11591/ijece.v13i5.pp5054-5065.

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<p><span lang="EN-US">The global transition from fossil-based automobile systems to their <br /> electric-driven counterparts has made the use of a storage device inevitable. Owing to its high energy density, lower self-discharge, and higher cycle lifetime the lithium-ion battery is of significant consideration and usage in electric vehicles. Nevertheless, the state of charge (SOC) of the battery, which cannot be measured directly, must be calculated using an estimator. This paper proposes, by means of a modiﬁed priori estimate and a compensating proportional gain, an improve

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Wang, Shunli, Xianyi Jia, Paul Takyi-Aninakwa, Daniel-Ioan Stroe, and Carlos Fernandez. "Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs." Journal of The Electrochemical Society 170, no. 5 (2023): 050514. http://dx.doi.org/10.1149/1945-7111/acd148.

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Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. State of charge (SOC) estimation is one of the key functions of the battery management system (BMS). Accurate SOC estimation helps to determine the driving range and effective energy management of electric vehicles (EVs). However, due to complex electrochemical reactions and nonlinear battery characteristics, accurate SOC estimation is challenging. Therefore, this review examines the existing methods for estimating the SOC of LIBs and analyzes their respective advantages and disadvantages. Subsequentl

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Trilla, Lluís, Lluc Canals Casals, Jordi Jacas, and Pol Paradell. "Dual Extended Kalman Filter for State of Charge Estimation of Lithium–Sulfur Batteries." Energies 15, no. 19 (2022): 6989. http://dx.doi.org/10.3390/en15196989.

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Lithium-Sulfur is a promising technology for the next generation of batteries and research efforts for early-stage prototype implementation increased in recent years. For the development of a suitable Battery Management System, a state estimator is required; however, lithium-sulfur behavior presents a large non-observable region that may difficult the convergence of the state estimation algorithm leading to large errors or even instability. A dual Extended Kalman Filter is proposed to circumvent the non-observability region. This objective is achieved by combining a parameter estimation algori

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Sun, Gang, Jin Ding Lu, Qing Ye, Li Ren, Jing Shi, and Yue Jin Tang. "State of Charge Estimation Using EKF Method for VRB." Advanced Materials Research 512-515 (May 2012): 986–94. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.986.

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Vanadium redox battery (VRB) system is a new type of energy storage system, which can be used to shave power grid peak, improve power quality, and smooth fluctuations of power and Voltage in photovoltaic and wind power systems. The state of charge (SOC) of VRB is an indication of how much energy is stored in the battery. Estimating the VRB SOC accurately in real time is very important when the battery is used to power system. In consideration of deficiencies of existing methods, a new approach is introduced to use Extended Kalman Filter (EKF) method. In this paper, state space model of VRB bas

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Liu, Han, Xinyu Cao, Fengdao Zhou, and Gang Li. "Online fusion estimation method for state of charge and state of health in lithium battery storage systems." AIP Advances 13, no. 4 (2023): 045217. http://dx.doi.org/10.1063/5.0142507.

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To ensure the safe and reliable operation of Li-ion battery energy storage systems, it is important to diagnose the operational status and aging degree of the batteries. In this study, an online fusion estimation method based on back propagation neural network and genetic algorithm (BP-GA) is used for estimating the state of charge (SoC) and state of health (SoH) of Li-ion batteries. First, the effective features of SoC and SoH of Li-ion batteries during charging and discharging are analyzed, and the relevant features are extracted. Subsequently, a conventional back propagation neural network

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Jafari, Sadiqa, Zeinab Shahbazi, Yung-Cheol Byun, and Sang-Joon Lee. "Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach." Mathematics 10, no. 6 (2022): 888. http://dx.doi.org/10.3390/math10060888.

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The battery management system in an electric vehicle must be reliable and durable to forecast the state of charge. Considering that battery degradation is generally nonlinear, state of charge (SOC) estimation with lower degradation can be challenging. Lithium-ion batteries are highly dependent on the knowledge of aging, which is usually costly or not available online. In this paper, we suggest the state of charge estimation of lithium-ion battery systems by using an extreme gradient boosting algorithm for electric vehicles application, which acquires the nonlinear relationship model can with o

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Zhang, Mingyue, and Xiaobin Fan. "Review on the State of Charge Estimation Methods for Electric Vehicle Battery." World Electric Vehicle Journal 11, no. 1 (2020): 23. http://dx.doi.org/10.3390/wevj11010023.

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Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time-varying and non-linear properties, which are extremely complex. Therefore, accurately estimating the state of charge is a challenging task. This paper reviews various representative patents and papers related to the state of charge estimation methods for an electric vehicle battery. According to their theoretical and experimental characteri

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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.

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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; tr

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Cao, Yu, Xin Wen, and Hongyu Liang. "Multi-Temperature State-of-Charge Estimation of Lithium-Ion Batteries Based on Spatial Transformer Network." Energies 17, no. 20 (2024): 5029. http://dx.doi.org/10.3390/en17205029.

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Accurately estimating the state of charge of a lithium-ion battery plays an important role in managing the health of a battery and estimating its charging state. Traditional state-of-charge estimation methods encounter difficulties in processing the diverse temporal data sequences and predicting adaptive results. To address these problems, we propose a spatial transformer network (STN) for multi-temperature state-of-charge estimation of lithium-ion batteries. The proposed STN consists of a convolutional neural network with a temporal–spatial module and a long short-term memory transformer netw

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Neupert, Steven, and Julia Kowal. "Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes." Batteries 9, no. 7 (2023): 364. http://dx.doi.org/10.3390/batteries9070364.

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State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery dataset with dynamic validation data over degradation and, on the other hand, a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman-filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The p

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Ismail. "The Estimation of Battery State of Charge using Corny Network." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 6 (2024): 5–11. https://doi.org/10.35940/ijrte.F7999.12060324.

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<strong>Abstract:</strong> State of charge (SOC) estimation of lithium-ion batteries has been extensively studied and the estimation accuracy was mainly investigated through the development of various battery models and dynamic estimation algorithms. All battery models, however, contain inherent model bias due to the simplifications and assumptions, which cannot be effectively addressed through the development of various conventional computation and intelligent computation. Consequently, some existing methods performed battery SOC estimation using conventional and intelligent computation have

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Margal, Ali, Soukaina El Daoudi, Abdelmounaim Khallouq, and Asma Karama. "Comparative Analysis of Battery State of Charge Estimation Methods." E3S Web of Conferences 601 (2025): 00033. https://doi.org/10.1051/e3sconf/202560100033.

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Lithium-ion batteries are widely used in electric vehicles, buses, etc., due to their high-power density, long lifespan, and high energy density. To efficiently manage energy in these vehicles, a Battery Management System (BMS) is crucial. A critical parameter for the BMS is the State of Charge (SoC), which indicates the available charge in the battery and ensures its operational range. This paper presents three methods for estimating SoC: the extended Kalman filter (EKF), the adaptive Luenberger observer (ALO), and a neural network model employing nonlinear auto-regressive with eXogenous inpu

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Camargo-Trigueros, Eugenio, Nancy Visairo-Cruz, Ciro-Alberto Núñez-Gutiérrez, and Juan Segundo-Ramírez. "Partial Discharge Method for State-of-Health Estimation Validated by Real-Time Simulation." Processes 12, no. 11 (2024): 2389. http://dx.doi.org/10.3390/pr12112389.

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Accurate estimation of the state of health (SOH) of batteries for automotive applications, particularly in electric vehicle battery management systems (EV-BMS), remains a critical study area to ensure battery system availability. This paper proposes a comprehensive SOH estimation method that transcends traditional approaches based on estimating the available capacity using the integral of the battery current or estimating the increase in internal resistance. The SOH estimator employs a partial discharge method (PDM) and a linear state-of-charge (SOC) observer based on an equivalent electrical

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Balkan, Ayca, Min Gao, Paulo Tabuada, and Lei He. "A Behavioral Algorithm for State of Charge Estimation." World Electric Vehicle Journal 5, no. 2 (2012): 412–17. http://dx.doi.org/10.3390/wevj5020412.

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Li, Mingheng. "Li-ion dynamics and state of charge estimation." Renewable Energy 100 (January 2017): 44–52. http://dx.doi.org/10.1016/j.renene.2016.06.009.

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Hogan, Jason M., Roger Higdon, Natali Kolker, and Eugene Kolker. "Charge State Estimation for Tandem Mass Spectrometry Proteomics." OMICS: A Journal of Integrative Biology 9, no. 3 (2005): 233–50. http://dx.doi.org/10.1089/omi.2005.9.233.

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Danko, Matúš, Juraj Adamec, Michal Taraba, and Peter Drgona. "Overview of batteries State of Charge estimation methods." Transportation Research Procedia 40 (2019): 186–92. http://dx.doi.org/10.1016/j.trpro.2019.07.029.

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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 (2017): 508. http://dx.doi.org/10.11591/ijeecs.v5.i3.pp508-514.

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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 bu

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Wei, Ying. "State-of-charge estimation for lithium-ion batteries based on dual extended Kalman filter." Journal of Physics: Conference Series 2369, no. 1 (2022): 012048. http://dx.doi.org/10.1088/1742-6596/2369/1/012048.

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The strategy of estimating SOC by model is highly dependent on the accuracy of model. An SOC estimation method based on a dual extended Kalman fliter (DEKF) is proposed. One of the dual filters is employed to estimate the battery SOC, and the other is used to online identify the model parameters. The SOC estimation results by DEKF are compared with those by single EKF under the US06 Highway Driving Schedule test and Dynamic Stress Test (DST). The comparison results show that DEKF has higher SOC estimation and voltage prediction accuracy. Under the US06 and DST tests, the SOC mean absolute erro

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Kalk, Alexis, Lea Leuthner, Christian Kupper, and Marc Hiller. "An Aging-Optimized State-of-Charge-Controlled Multi-Stage Constant Current (MCC) Fast Charging Algorithm for Commercial Li-Ion Battery Based on Three-Electrode Measurements." Batteries 10, no. 8 (2024): 267. http://dx.doi.org/10.3390/batteries10080267.

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This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and Li-plating is experimentally analyzed with the help of three-electrode measurements. Therefore, a SOC-dependent charging algorithm is proposed. Secondly, a SOC estimation algorithm based on an Extended Kalman Filter is developed in MATLAB/Simulink to conduct high accuracy SOC estim

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Yu, Yuan Bin, Zhou Cai, Kai Peng, and Wen Qiang Lv. "SOC Estimation Strategy and its Accuracy Analysis." Advanced Materials Research 953-954 (June 2014): 790–95. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.790.

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Accurate battery state of charge is the prerequisite and precondition for optimal control of hybrid vehicles. This article will be based on the established dynamic model of battery, estimate the battery state of charge in real time. Firstly, analysis the application limitations of Kalman filtering algorithm estimates battery state of charge. Secondly, for some uncertain parameters contained in the model of battery system, paper proposes a parameter line identification extended Kalman filter algorithm to estimate the battery state of charge. Finally, experimental verification algorithm dynamic

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Antonucci, Artale, Brunaccini, et al. "Li-ion Battery Modeling and State of Charge Estimation Method Including the Hysteresis Effect." Electronics 8, no. 11 (2019): 1324. http://dx.doi.org/10.3390/electronics8111324.

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In this paper, a new approach to modeling the hysteresis phenomenon of the open circuit voltage (OCV) of lithium-ion batteries and estimating the battery state of charge (SoC) is presented. A characterization procedure is proposed to identify the battery model parameters, in particular, those related to the hysteresis phenomenon and the transition between charging and discharging conditions. A linearization method is used to obtain a suitable trade-off between the model accuracy and a low computational cost, in order to allow the implementation of SoC estimation on common hardware platforms. T

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