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Journal articles on the topic 'State of Charge (SoC) Monitoring'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

M., Surendar, and Pradeepa P. "Future Challenges in State of Charge Estimation for Lithium-Ion Batteries." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 1 (2020): 215–23. https://doi.org/10.35940/ijeat.A1789.1010120.

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Energy storage system is an Emerging technology in past few decades. The Energy storage system is an important technology for Electric Vehicles, Hybrid Electric Vehicles (EV) and (HVE) and Micro grid system. The Battery Management System (BMS) is need to be control and monitor the various parameter of the battery such as SOC , SOH, C-Rate, E-Rate ,Temperature , RVL , EOL and so on. However, the (SOC) State of Charge is an important estimation for the online control and BMS monitoring. The SOC is the challenging task when online control and BMS monitoring. This various technique or methods avai

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2

Dou, Zhaoliang, Jiaxin Li, Hongjuan Yan, Chunlin Zhang, and Fengbin Liu. "Real-Time Online Estimation Technology and Implementation of State of Charge State of Uncrewed Aerial Vehicle Lithium Battery." Energies 17, no. 4 (2024): 803. http://dx.doi.org/10.3390/en17040803.

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The SOC estimation of UAV lithium batteries plays a crucial role in the mission planning and safe flight of UAVs. Aiming at existing UAV lithium battery SOC estimation problems, such as low estimation accuracy and poor real-time performance, a real-time online estimation scheme for UAV lithium battery SOC is proposed. A model-based approach is adopted to establish an SOC estimation model on the basis of the Thevenin equivalent circuit model, and a UAV lithium battery online monitoring device is developed to monitor the current and voltage of the UAV lithium battery in real time and import the

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3

Bawango, Kesya Maria Magdalena, Hesky S. Kolibu, and Seni H. J. Tongkukut. "Estimasi State of Charge Pada Baterai Li-ion Dengan Menggunakan Metode Coulomb Counting." JURNAL LPPM BIDANG SAINS DAN TEKNOLOGI 9, no. 2 (2024): 78–84. https://doi.org/10.35801/jlppmsains.9.2.2024.51598.

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Baterai berfungsi sebagai tempat penyimpanan energi yang dihasilkan dari sumber energi dan dapat digunakan untuk menyuplai energi saat sumber energi utama tidak tersedia. Dengan demikian, baterai memungkinkan penyimpanan energi dan penggunaannya dalam waktu yang fleksibel. Monitoring baterai diperlukan agar performa baterai dapat mencapai maksimal. Salah satu aspek monitoring baterai adalah mengestimasi state of charge (SOC). Penelitian ini melibatkan empat merek baterai Li-ion yang berbeda, dengan pengambilan data arus dan tegangan selama proses pengosongan baterai dilakukan selama 60 menit d

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Rybalchenko, Maria, Nathan Quill, D. Noel Buckley, and Robert P. Lynch. "(Invited) State of Charge Monitoring of Vanadium Flow Batteries Using Spectroscopic and Electrochemical Methods." ECS Meeting Abstracts MA2022-02, no. 30 (2022): 1101. http://dx.doi.org/10.1149/ma2022-02301101mtgabs.

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Vanadium Flow Batteries (VFBs) are a promising energy storage technology, particularly for large and medium scale applications. In a VFB, energy is stored in two vanadium electrolytes separated by a nafion membrane. The use of vanadium electrolytes in both half-cells minimises cross contamination issues which have plagued other flow battery chemistries. The electrodes, electrolytes and membrane are key areas of research for VFBs with the aim of improving efficiency, energy density and costs. Accurately monitoring state-of-charge (SoC) is vital for any rechargeable battery system. It is importa

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5

Dyartanti, Endah Retno, Anif Jamaluddin, Muhammad Farrel Akshya, et al. "The State of Charge Estimation of LiFePO4 Batteries Performance Using Feed Forward Neural Network Model." Applied Mechanics and Materials 918 (January 9, 2024): 85–94. http://dx.doi.org/10.4028/p-iidzs6.

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Lithium-ion batteries like LiFePO4 become a new choice for electrical energy sources in the world and can be used on electric vehicles. Battery packs monitoring by Battery Management System in electric vehicles require accurate monitoring. The inaccuracy of monitoring such property can lead to low safety, low efficiency and battery’s life reduction. Estimating state of charge (SoC) to prevent battery damage from overcharging and over discharging. Some of the methods used to estimate SoC such as Coulomb Counting have errors during the charge and discharge process. This research proposes a count

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6

Kurzweil, Peter, Bernhard Frenzel, and Wolfgang Scheuerpflug. "A Novel Evaluation Criterion for the Rapid Estimation of the Overcharge and Deep Discharge of Lithium-Ion Batteries Using Differential Capacity." Batteries 8, no. 8 (2022): 86. http://dx.doi.org/10.3390/batteries8080086.

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Differential capacity dQ/dU (capacitance) can be used for the instant diagnosis of battery performance in common constant current applications. A novel criterion allows state-of-charge (SOC) and state-of-health (SOH) monitoring of lithium-ion batteries during cycling. Peak values indicate impeding overcharge or deep discharge, while dSOC/dU = dU/dSOC = 1 is close to “full charge” or “empty” and can be used as a marker for SOC = 1 (and SOC = 0) at the instantaneous SOH of the aging battery. Instructions for simple state-of-charge control and fault diagnosis are given.

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7

Ma, Jinkai, Haodong Yan, Yitian Sun, et al. "Comparison and Evaluation of State-of-charge and Health Monitoring Methods for Lithium-sulfur Batteries." International Journal of Energy 5, no. 1 (2024): 5–14. http://dx.doi.org/10.54097/mhpg6x76.

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State-of-charge (SOC) estimation and state-of-health (SOH) prediction of lithium-sulfur batteries is an extremely important technology for battery management systems (BMS), which is affected by factors such as internal chemical reactions and external temperature changes of lithium-sulfur batteries, which makes it difficult to predict the state of charge and state of health of lithium-sulfur batteries. Firstly, the retrieval status of battery SOC estimation and SOH prediction is introduced, then the main methods and advantages and disadvantages of various methods are introduced, and finally the

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8

Afrida, Yenni, Jeckson J, and Ubaidah U. "Studi Penentuan State Of Charge (SOC) pada Baterai Valve Regulated Lead Acid NP7-12 Menggunakan MATLAB." Electrician : Jurnal Rekayasa dan Teknologi Elektro 17, no. 2 (2023): 146–50. http://dx.doi.org/10.23960/elc.v17n2.2481.

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Abstract — To maintain battery performance, it is necessary to have a battery monitoring system that functions to determine several parameters in the battery, such as voltage, current, capacitance and battery capacity. The technology for knowing battery capacity is often known as state of charge (SOC). State of charge (SOC) is the ratio of the remaining energy to the maximum energy capacity of the battery. State of charge values are often expressed in percentage form, 0 percent -100 percent. Estimating the value of the state of charge is quite important in battery monitoring technology. The

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9

Syafii, Syafii, Irfan El Fakhri, Thoriq Kurnia Agung, and Farah Azizah. "Design of battery state of charge monitoring and control system using coulomb counting method based." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 736. http://dx.doi.org/10.11591/ijeecs.v33.i2.pp736-745.

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<span>Lead-acid batteries are commonly used in photovoltaic systems to store solar energy for continuous use. However, lead-acid batteries have a relatively short lifespan due to frequent over-charging and over-discharging. A battery management system (BMS) is essential for accurately predicting the battery state of charge (SoC) value in order to extend the battery lifespan. In this research, a BMS is developed using the coulomb counting method to estimate the SoC value of a lead-acid battery. The coulomb counting algorithm provides a reliable estimation of the battery’s SoC value by cal

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Syafii, Irfan El Fakhri, Thoriq Kurnia Agung, and Farah Azizah. "Design of battery state of charge monitoring and control system using coulomb counting method based." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 736–45. https://doi.org/10.11591/ijeecs.v33.i2.pp736-745.

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Lead-acid batteries are commonly used in photovoltaic systems to store solar energy for continuous use. However, lead-acid batteries have a relatively short lifespan due to frequent over-charging and over-discharging. A battery management system (BMS) is essential for accurately predicting the battery state of charge (SoC) value in order to extend the battery lifespan. In this research, a BMS is developed using the coulomb counting method to estimate the SoC value of a lead-acid battery. The coulomb counting algorithm provides a reliable estimation of the battery’s SoC value by calculati

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11

Kurzweil, Peter, and Wolfgang Scheuerpflug. "State-of-Charge Monitoring and Battery Diagnosis of Different Lithium Ion Chemistries Using Impedance Spectroscopy." Batteries 7, no. 1 (2021): 17. http://dx.doi.org/10.3390/batteries7010017.

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For lithium iron phosphate batteries (LFP) in aerospace applications, impedance spectroscopy is applicable in the flat region of the voltage-charge curve. The frequency-dependent pseudocapacitance at 0.15 Hz is presented as useful state-of-charge (SOC) and state-of-health (SOH) indicator. For the same battery type, the prediction error of pseudocapacitance is better than 1% for a quadratic calibration curve, and less than 36% for a linear model. An approximately linear correlation between pseudocapacitance and Ah battery capacity is observed as long as overcharge and deep discharge are avoided

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12

Ma, Chao-Tsung. "A Novel State of Charge Estimating Scheme Based on an Air-Gap Fiber Interferometer Sensor for the Vanadium Redox Flow Battery." Energies 13, no. 2 (2020): 291. http://dx.doi.org/10.3390/en13020291.

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Real-time and remote monitoring of the state of charge (SOC) of a vanadium redox flow battery (VRFB) is technically desirable for achieving advanced compensation functions of VRFB systems. This paper, for the first time, proposes a novel SOC monitoring scheme based on an air-gap fiber Fabry–Perot interferometer (AGFFPI) sensor for the VRFB. The proposed sensing concept is based on real-time sensing of the refractive index (RI) of the positive electrolyte, which is found closely correlated to the VRFB’s SOC. The proposed SOC estimating scheme using fiber sensor has a number of merits, e.g., bei

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13

Kim, Jonghyeon, and Julia Kowal. "A Method for Monitoring State-of-Charge of Lithium-Ion Cells Using Multi-Sine Signal Excitation." Batteries 7, no. 4 (2021): 76. http://dx.doi.org/10.3390/batteries7040076.

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In this paper, a method for monitoring SoC of a lithium-ion battery cell through continuous impedance measurement during cell operation is introduced. A multi-sine signal is applied to the cell operating current, and the cell SoH and SoC can be simultaneously monitored via impedance at each frequency. Unlike existing studies in which cell impedance measurement is performed ex situ through EIS equipment, cell state estimation is performed in situ. The measured impedance takes into account cell temperature and cell SoH, enabling accurate SoC estimation. The measurement system configured for the

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14

Meng, Xiong, and Lim. "Model-Based Condition Monitoring of a Vanadium Redox Flow Battery." Energies 12, no. 15 (2019): 3005. http://dx.doi.org/10.3390/en12153005.

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The safe, efficient and durable utilization of a vanadium redox flow battery (VRB) requires accurate monitoring of its state of charge (SOC) and capacity decay. This paper focuses on the unbiased model parameter identification and model-based monitoring of both the SOC and capacity decay of a VRB. Specifically, a first-order resistor-capacitance (RC) model was used to simulate the dynamics of the VRB. A recursive total least squares (RTLS) method was exploited to attenuate the impact of external disturbances and accurately track the change of model parameters in realtime. The RTLS-based identi

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Kurzweil, Peter, and Wolfgang Scheuerpflug. "State-of-Charge Monitoring and Battery Diagnosis of NiCd Cells Using Impedance Spectroscopy." Batteries 6, no. 1 (2020): 4. http://dx.doi.org/10.3390/batteries6010004.

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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 r

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16

Hu, Feifu, Hengyu Li, Cheng Lin, Dian Fang, and Yuan Lin. "Lithium-ion battery state-of-charge estimation based on long-short-term memory neural network and square-root cubature Kalman filter." Journal of Physics: Conference Series 2591, no. 1 (2023): 012031. http://dx.doi.org/10.1088/1742-6596/2591/1/012031.

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Abstract Due to the widespread use of Li-ion batteries in electric vehicles, battery management systems for monitoring the status and ensuring the safe operation of Li-ion batteries have been extensively studied. Online monitoring of the state of charge (SOC) is crucial for lithium-ion batteries, but achieving precise SOC estimation is a difficult task due to battery dynamics and the influence of factors such as current, temperature, and operating conditions on SOC variability. This paper introduces a novel approach that combines a Long Short-Term Memory (LSTM) network with a square-root cubat

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17

Saha, Pankaj, Abdul Ali, and Venkatasailanathan Ramadesigan. "Estimation of State-of-Charge and Energy Efficiency of Supercapacitors Using a 1-D Electrochemical Model." ECS Meeting Abstracts MA2023-02, no. 1 (2023): 4. http://dx.doi.org/10.1149/ma2023-0214mtgabs.

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Supercapacitors (SCs) have found a broad application spectrum due to their unique characteristics, including high power density and long cycle life [1]. Supercapacitors have been extensively studied to be used with Li-ion batteries for electric vehicle applications due to their complementary characteristics [1]. The energy management system of these applications relies on accurate information about the state-of-charge (SOC) and energy efficiency of the storage device at the cell level for effective monitoring and control. In the case of SCs, coulomb counting is the most commonly used SOC ident

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18

Sinha, Bhabya, Arunima Adhikary, P. Nandini, Venkatesh Chakravartula, R. Narayanamoorthi, and Samiappan Dhanalakshmi. "Monitoring State-of-Charge of Lithium-Ion Battery with Diverse Series-Parallel Configuration Using Particle Filter." ECS Transactions 107, no. 1 (2022): 8831–45. http://dx.doi.org/10.1149/10701.8831ecst.

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Lithium-ion battery packs are typically assembled into modules. These modules can be equipped with various series-parallel configurations. In this work a feasible method for monitoring SoC is demonstrated by designing a circuit model in Simulink of three different configurations 3s2p (three in series and two in parallel), 3s3p (three in series and three in parallel), and 4s2p (four in series and two in parallel) using Particle Filter. Initially, lithium-ion cells are connected in 3s2p configuration and then we compared our results by adding one cells in parallel and then in series with the est

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Du, Lu Lu, Bei Li, and Huai De Zhang. "Estimation on State of Charge of Power Battery Based on the Grey Neural Network Model." Applied Mechanics and Materials 427-429 (September 2013): 1158–62. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1158.

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The state of charge (SOC) of power battery is an important parameter of battery state, and it plays a vital role in real-time accurate estimation, condition monitoring, improving battery life, and ensuring the safety of power supply. This paper presents the grey neural network model of the relation between the battery SOC and rebound voltage, discharge current. Based on this model, a new on-line SOC detection method using rebound voltage and discharge current in the discharge process is proposed. From the testing results, the model and algorithm were proved to be feasible and effective, and th

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Guo, Gui Fang, Lin Shui, Xiao Lan Wu, and Bing Gang Cao. "SOC Estimation for Li-Ion Battery Using SVM Based on Particle Swarm Optimization." Advanced Materials Research 1051 (October 2014): 1004–8. http://dx.doi.org/10.4028/www.scientific.net/amr.1051.1004.

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State of charge (SOC) is very important parameter for monitoring the battery charge and discharge operation and estimating the drive distance of electric vehicle. Especially, with the cycle number increasing, the precision estimation of SOC for battery management system is still not well resolved. Therefore, in this study, aim at accurate sampling of voltage, current and temperature signals based on LTC6803-3 chip, the paper proposed a support vector machine (SVM) optimized by particle swarm optimization (PSO) to improve SOC estimation accuracy. The results demonstrate that the proposed PSO-SV

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Ningrum, Puspita, Novie Ayub Windarko, and Suhariningsih Suhariningsih. "Battery Management System (BMS) Dengan State Of Charge (SOC) Metode Modified Coulomb Counting." INOVTEK - Seri Elektro 1, no. 1 (2019): 1. http://dx.doi.org/10.35314/ise.v1i1.1022.

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Baterai merupakan media penyimpanan energi listrik dalam bentuk energi kimia yang dapat dikonversikan menjadi daya. Dalam kasus yang ditemukan baterai mudah mengalami kerusakan dan memiliki life time yang pendek. Kerusakan pada baterai disebabkan karena penggunaan yang tidak ideal dan baterai tidak dilengkapi sistem proteksi dan monitoring, sehingga baterai tetap beroperasi meskipun dalam kondisi over-voltage, over-current dan over-heat saat charging dan ditambah mengalami under-voltage pada saat discharging. Pada jurnal ini disampaikan perancangan sistem BMS (Battery Management System) untuk

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Zhao, Jingyu, Kexin Xing, Xinrong Jiang, Chi-Min Shu, and Xiangrong Sun. "Thermal Runaway Critical Threshold and Gas Release Safety Boundary of 18,650 Lithium-Ion Battery in State of Charge." Processes 13, no. 7 (2025): 2175. https://doi.org/10.3390/pr13072175.

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In this study, we systematically investigated the characteristic parameter evolution laws of thermal runaway with respect to 18,650 lithium-ion batteries (LIBs) under thermal abuse conditions at five state-of-charge (SOC) levels: 0%, 25%, 50%, 75%, and 100%. In our experiments, we combined infrared thermography, mass loss analysis, temperature monitoring, and gas composition detection to reveal the mechanisms by which SOC affects the trigger time, critical temperature, maximum temperature, mass loss, and gas release characteristics of thermal runaway. The results showed that as the SOC increas

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Chen, Bin-Hao, Chen-Hsiang Hsieh, Li-Tao Teng, and Chien-Chung Huang. "Experimental Study on Temperature Sensitivity of the State of Charge of Aluminum Battery Storage System." Energies 16, no. 11 (2023): 4270. http://dx.doi.org/10.3390/en16114270.

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The operating temperature of a battery energy storage system (BESS) has a significant impact on battery performance, such as safety, state of charge (SOC), and cycle life. For weather-resistant aluminum batteries (AlBs), the precision of the SOC is sensitive to temperature variation, and errors in the SOC of AlBs may occur. In this study, a combination of the experimental charge/discharge data and a 3D anisotropic homogeneous (Ani-hom) transient heat transfer simulation is performed to understand the thermal effect of a novel battery system, say an aluminum-ion battery. The study conducts a tu

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Wang, Qi, Tian Gao, and Xingcan Li. "SOC Estimation of Lithium-Ion Battery Based on Equivalent Circuit Model with Variable Parameters." Energies 15, no. 16 (2022): 5829. http://dx.doi.org/10.3390/en15165829.

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The state of charge (SOC) of the battery is an important basis for the battery management system to perform state monitoring and control decisions. In this paper, by identifying the internal parameters of the battery model at different temperatures and SOCs of the lithium-ion battery, the specific factors that affect the change of the parameters are analyzed, the segmentation basis of the model and the fitting method of related parameters are discussed, the second-order equivalent circuit model of the lithium-ion battery whose parameters vary with SOC and temperature is established, the unscen

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Chen, Mengying, Fengling Han, Long Shi, et al. "Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model." Energies 15, no. 7 (2022): 2658. http://dx.doi.org/10.3390/en15072658.

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Lithium-ion battery devices are essential for energy storage and supply in distributed energy generation systems. Robust battery management systems (BMSs) must guarantee that batteries work within a safe range and avoid the damage caused by overcharge and overdischarge. The state-of-charge (SoC) of Li-ion batteries is difficult to observe after batteries are manufactured. The hysteresis phenomenon influences the existing battery modeling and SoC estimation accuracy. This research applies a terminal sliding mode observer (TSMO) algorithm based on a hysteresis resistor-capacitor (RC) equivalent

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Rahman, Ashikur, Xianke Lin, and Chongming Wang. "Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer." Energies 15, no. 15 (2022): 5662. http://dx.doi.org/10.3390/en15155662.

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The anode state of charge (SOC) and degradation information pertaining to lithium-ion batteries (LIBs) is crucial for understanding battery degradation over time. This information about each cell in a battery pack can help prolong the battery pack’s life cycle. Because of the limited observability, estimating the anode state and capacity fade is difficult. This task is even more challenging for the cells in a battery pack, as the current through the individual cell is not constant when cells are connected in parallel. Considering these challenges, this paper presents a novel method to set up t

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Xiong, Ran, Shunli Wang, Fei Feng, et al. "Co-Estimation of State-of-Charge and State-of-Health for High-Capacity Lithium-Ion Batteries." Batteries 9, no. 10 (2023): 509. http://dx.doi.org/10.3390/batteries9100509.

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To address the challenges of efficient state monitoring of lithium-ion batteries in electric vehicles, a co-estimation algorithm of state-of-charge (SOC) and state-of-health (SOH) is developed. The algorithm integrates techniques of adaptive recursive least squares and dual adaptive extended Kalman filtering to enhance robustness, mitigate data saturation, and reduce the impact of colored noise. At 25 °C, the algorithm is tested and verified under dynamic stress test (DST) and Beijing bus DST conditions. Under the Beijing bus DST condition, the algorithm achieves a mean absolute error (MAE) of

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Kurzweil, Peter, and Mikhail Shamonin. "State-of-Charge Monitoring by Impedance Spectroscopy during Long-Term Self-Discharge of Supercapacitors and Lithium-Ion Batteries." Batteries 4, no. 3 (2018): 35. http://dx.doi.org/10.3390/batteries4030035.

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Frequency-dependent capacitance C(ω) is a rapid and reliable method for the determination of the state-of-charge (SoC) of electrochemical storage devices. The state-of-the-art of SoC monitoring using impedance spectroscopy is reviewed, and complemented by original 1.5-year long-term electrical impedance measurements of several commercially available supercapacitors. It is found that the kinetics of the self-discharge of supercapacitors comprises at least two characteristic time constants in the range of days and months. The curvature of the Nyquist curve at frequencies above 10 Hz (charge tran

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A., Guna, Sharmila Kumari S, and Sivapriya G. "State of Charge based Charging Controller with Temperature monitoring system for Lithium ion Battery in Electric Vehicle." E3S Web of Conferences 399 (2023): 01008. http://dx.doi.org/10.1051/e3sconf/202339901008.

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The battery management system plays a vital role in electric vehicles. Improper charging and discharging of the battery alters the chemical properties of the battery and thereby reduces its lifetime. Battery State of charge (SOC) is an essential parameter to be measured for designing a battery management system. Operating the electric vehicle battery above its nominal temperature leads to a blast of the battery, which may cause human loss. Hence the temperature of the battery has to be monitored properly. In order to reduce the depth of discharge of the battery, an SOC-based charging controlle

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Purohit, Kanishkavikram, Shivangi Srivastava, Varun Nookala, et al. "Soft Sensors for State of Charge, State of Energy, and Power Loss in Formula Student Electric Vehicle." Applied System Innovation 4, no. 4 (2021): 78. http://dx.doi.org/10.3390/asi4040078.

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The proliferation of electric vehicle (EV) technology is an important step towards a more sustainable future. In the current work, two-layer feed-forward artificial neural-network-based machine learning is applied to design soft sensors to estimate the state of charge (SOC), state of energy (SOE), and power loss (PL) of a formula student electric vehicle (FSEV) battery-pack system. The proposed soft sensors were designed to predict the SOC, SOE, and PL of the EV battery pack on the basis of the input current profile. The input current profile was derived on the basis of the designed vehicle pa

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Rajnarayanan, M., A. Balaji, S. Sumitha, and M. S. Sathish. "EV Battery Management System with Fire Safety and Dual Mode Charge Monitoring." Journal of Recent Trends in Electrical Power System 8, no. 2 (2025): 1–8. https://doi.org/10.5281/zenodo.15349562.

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<em>Battery storage forms the most crucial part of any Electric Vehicle (EV), as it stores the energy required for the operation of the vehicle. To ensure maximum output and safe functioning, an efficient Battery Management System (BMS) is essential. The BMS monitors critical battery parameters, determines the State of Charge (SoC), and provides protective functions to guarantee safe and optimal operation. It plays an integral role in safeguarding both the user and the battery by maintaining the cells within their safe operating limits. The proposed system not only monitors the battery and man

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Jianwang, Hong, Ricardo A. Ramirez-Mendoza, and Jorge de J. Lozoya-Santos. "Adjustable Scaling Parameters for State of Charge Estimation for Lithium-Ion Batteries Using Iterative Multiple UKFs." Mathematical Problems in Engineering 2020 (April 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/4037306.

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In this paper, one unscented Kalman filter with adjustable scaling parameters is proposed to estimate the state of charge (SOC) for lithium-ion batteries, as SOC is most important in monitoring the latter battery management system. After the equivalent circuit model is applied to describe the lithium-ion battery charging and discharging properties, a state space equation is constructed to regard SOC as its first state variable. Based on this state space model about SOC, one state estimation problem corresponding to the nonlinear system is established. In implementing the unscented Kalman filte

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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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Dannier, Adolfo, Gianluca Brando, Mattia Ribera, and Ivan Spina. "Li-Ion Batteries for Electric Vehicle Applications: An Overview of Accurate State of Charge/State of Health Estimation Methods." Energies 18, no. 4 (2025): 786. https://doi.org/10.3390/en18040786.

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Road transport significantly contributes to greenhouse gas emissions in all places where it is used and therefore also in Europe, prompting the EU to set ambitious objectives for CO2 reduction. In order to reach these objectives, the automotive industry is transitioning to electric vehicles, utilizing electric powertrains powered by battery packs. However, the longevity and reliability of these batteries are critical concerns. This review paper focuses on the advanced diagnostic techniques for effective battery State of Charge (SoC) and State of Health (SoH) monitoring. Accurate SoC/SoH estima

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Gao, Jie, Yan Lyu, and Cunfu He. "Estimating State of Charge of Lithium-ion Batteries by Using Ultrasonic Guided Waves Detection Technology." Journal of Physics: Conference Series 2198, no. 1 (2022): 012015. http://dx.doi.org/10.1088/1742-6596/2198/1/012015.

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Abstract As a light weight and high power density energy, Lithium-ion batteries have become widely used in electric vehicles, energy storage systems, etc. Thus, accurately capturing the internal battery dynamics and properly estimating the state of charge of a lithium-ion battery attract academic research interest. A reliable battery detection method is particularly important. The mechanical properties (elastic modulus and density) can be affected by the level of lithiation of the electrodes and the volume expansion during charge and discharge cycling. In this work, a theoretical model of ultr

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Zhang, Zhaowei, Junya Shao, Junfu Li, Yaxuan Wang, and Zhenbo Wang. "SOC Estimation Methods for Lithium-Ion Batteries without Current Monitoring." Batteries 9, no. 9 (2023): 442. http://dx.doi.org/10.3390/batteries9090442.

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State of charge (SOC) estimation is an important part of a battery management system (BMS). As for small portable devices powered by lithium-ion batteries, no current sensor will be configured in BMS, which presents a challenge to traditional current-based SOC estimation algorithms. In this work, an electrochemical model is developed for lithium batteries, and three methods, including the incremental seeking method, dichotomous method, and extended Kalman filter algorithm (EKF), are separately developed to establish the framework of current and SOC estimation simultaneously. The results show t

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Reuter, Lennart, Jonas L. S. Dickmanns, Simon Kücher, et al. "State-of-Charge Dependent Change in the Microporous Structure of Graphite Electrodes." ECS Meeting Abstracts MA2023-02, no. 8 (2023): 3392. http://dx.doi.org/10.1149/ma2023-0283392mtgabs.

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Regarding the negative electrode, the most used electrode material in commercially available lithium-ion batteries (LIBs) up to the present day is graphite.1 Upon battery operation, an irreversible volume change in the microporous structure of the graphite electrode is detected. It is associated with the formation of the solid-electrolyte interphase (SEI) within the first cycles and an irreversible thickness increase of the electrode. Consequently, one observes a change in the electrode’s porosity after the initial formation cycles.2,3 Next to irreversible microstructure changes, one observes

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Nurdiansyah, Rizal, Novie Ayub Windarko, Renny Rakhmawati, and Muhammad Abdul Haq. "State of charge estimation of ultracapacitor based on equivalent circuit model using adaptive neuro-fuzzy inference system." Journal of Mechatronics, Electrical Power, and Vehicular Technology 13, no. 1 (2022): 60–71. http://dx.doi.org/10.14203/j.mev.2022.v13.60-71.

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Ultracapacitors have been attracting interest to apply as energy storage devices with advantages of fast charging capability, high power density, and long lifecycle. As a storage device, accurate monitoring is required to ensure and operate safely during the charge/discharge process. Therefore, high accuracy estimation of the state of charge (SOC) is needed to keep the Ultracapacitor working properly. This paper proposed SOC estimation using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS is tested by comparing it to true SOC based on an equivalent circuit model. To find the best

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Kim, Jonghyeon, and Julia Kowal. "Development of a Matlab/Simulink Model for Monitoring Cell State-of-Health and State-of-Charge via Impedance of Lithium-Ion Battery Cells." Batteries 8, no. 2 (2022): 8. http://dx.doi.org/10.3390/batteries8020008.

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Lithium-ion battery cells not only show different behaviors depending on degradation and charging states, but also overcharge and overdischarge of cells shorten battery life and cause safety problems, thus studies aiming to provide an accurate state of a cell are required. Measurements of battery cell impedance are used for cell SoH and SoC estimation techniques, but it generally takes a long time for a cell in each state to be prepared and cell voltage response is measured when charging and discharging under each condition. This study introduces an electrical equivalent circuit model of lithi

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Widjaja, Ryo G., Muhammad Asrol, Iwan Agustono, et al. "State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems." Emerging Science Journal 7, no. 3 (2023): 691–703. http://dx.doi.org/10.28991/esj-2023-07-03-02.

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The Solar Dryer Dome (SDD), an independent energy system equipped with Artificial Intelligence to support the drying process, has been developed. However, inaccurate state-of-charge (SOC) predictions in each battery cell resulted in the vulnerability of the battery to over-charging and over-discharging, which accelerated the battery performance degradation. This research aims to develop an accurate neural network model for predicting the SOC of battery-cell level. The model aims to maintain the battery cell balance under dynamic load applications. It is accompanied by a developed dashboard to

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Xi, Haiwen, Taolin Lv, Jincheng Qin, et al. "Prediction of Lithium Battery Voltage and State of Charge Using Multi-Head Attention BiLSTM Neural Network." Applied Sciences 15, no. 6 (2025): 3011. https://doi.org/10.3390/app15063011.

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Predicting battery states such as the voltage and state of charge (SOC) can help us monitor lithium batteries more efficiently during usage. This study proposed a predictive model for the lithium battery voltage and SOC by combining a second-order RC equivalent circuit model with a multi-head attention Bidirectional Long Short-Term Memory (MHA-BiLSTM) neural network. The equivalent circuit model simulates long-term charge–discharge cycles in Simulink, providing essential data for model training. The BiLSTM model, enhanced by the multi-head attention mechanism, is used for accurate short-term p

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Xing, Jie, and Peng Wu. "State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter." Sustainability 13, no. 9 (2021): 5046. http://dx.doi.org/10.3390/su13095046.

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State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter (UKF) may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is not high. Therefore, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed to solve this problem. The IAUKF is composed of the improved unscented Kalman f

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Masmitjà Rusinyol, Ivan, Julián González, Gerard Masmitjà , Spartacus Gomáriz, and Joaquín Del-Río-Fernández. "Power system of the Guanay II AUV." ACTA IMEKO 4, no. 1 (2015): 35. http://dx.doi.org/10.21014/acta_imeko.v4i1.161.

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Guanay II is an autonomous underwater vehicle (AUV) designed to perform measurements in a water column. In this paper the aspects of the vehicle's power system are presented with particular focus on the power elements and the state of charge of the batteries. The system performs both measurement and monitoring tasks and also controls the state of charge (SoC) of the batteries. It allows simultaneous charging of all batteries from outside the vehicle and has a wireless connection/disconnection mode. Guanay II uses a NiCd battery and for this reason the current integration as a SoC methodology h

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Pan, Yuanyuan, Ke Xu, Ruiqiang Wang, Honghong Wang, Guodong Chen, and Kai Wang. "Lithium-Ion Battery Condition Monitoring: A Frontier in Acoustic Sensing Technology." Energies 18, no. 5 (2025): 1068. https://doi.org/10.3390/en18051068.

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Lithium-ion batteries (LIBs) are widely used in the fields of consumer electronics, new energy vehicles, and grid energy storage due to their high energy density and long cycle life. However, how to effectively evaluate the State of Charge (SOC), State of Health (SOH), and overcharging behavior of batteries has become a key issue in improving battery safety and lifespan. Acoustic sensing technology, as an advanced non-destructive monitoring method, achieves real-time monitoring of the internal state of batteries and accurate evaluation of key parameters through ultrasonic testing technology an

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Komal Mohan Garse and Dr. Kedar Narayan Bairwa. "Performance Evaluation of Model-Based Online Condition Monitoring Algorithms for Li-Ion Battery State Estimation." International Journal of Information Technology and Management 19, no. 1 (2024): 98–103. http://dx.doi.org/10.29070/amh0hc18.

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This discovery examines and tests four model-based charge-state (SOC) estimation methods for lithium-ion (Li-ion) batteries. This work evaluates some parts of the SOC estimation, such as error distribution evaluation, rise time estimation, consumption time estimation, etc., instead of the former probing. The battery comparison model is introduced and the state function of the model is inferred. The first step is to study four model-based SOC estimation strategies. The four systems are then tested using simulation and analysis. To mimic the driving conditions of an electric vehicle, the Urban D

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S, Priyashree, Murugesh H. M, Deepika A. B, Manjunath M, and Shahapuram Bharath. "Battery Monitoring and Protective System." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5237–45. http://dx.doi.org/10.22214/ijraset.2024.62630.

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Abstract: The growing demand for battery-powered devices and systems has led to an increased need for battery monitoring and protective systems. These systems play a critical role in ensuring the safe and reliable operation of battery-powered systems, such as electric vehicles, renewable energy systems, and mobile devices. This work emphasizes to monitor the state of charge (SOC) of the battery to ensure optimal performance and prevent premature failure. The battery is protected from damage due to overcharging, over-discharging, overheating, and short-circuiting. It also provides early warning

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Malik, Fajar, Sri Paryanto Mursid, and Sri Utami. "Pembuatan Alat Monitoring Daya Pada Baterai Panel Surya 50 Wp Berbasis Internet Of Things." Jurnal Teknik Energi 12, no. 2 (2024): 21–26. http://dx.doi.org/10.35313/energi.v12i2.5220.

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PLTS merupakan salah satu jenis pembangkit yang dapat dimanfaatkanpotensinya di Indonesia. PLTS dapat menghasilkan energi alternatif denganmemanfaatkan cahaya matahari. Energi yang diterima dari panel surya tersimpanpada baterai dan energi yang akan disalurkan dari baterai membutuhkanpemantauan agar kondisi dari baterai dapat terjaga dengan baik. Pemantauan padabeterai juga diperlukan untuk mengetahui nilai parameter seperti tegangan dan arusyang masuk agar tidak terjadi overcharge. Penelitian ini bertujuan untuk membuatalat monitoring daya baterai berbasis IoT agar dapat mempermudah memantauk

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Sylvester, Tirones. "Automatic 12-Volt Battery Charge Controller for Telecommunication Systems." International Journal of Inventive Engineering and Sciences (IJIES) 12, no. 5 (2025): 25–31. https://doi.org/10.35940/ijies.E1104.12050525.

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<strong>Abstract:</strong> Everyday electronic applications rely on electric energy to perform work. The growing use of electrical appliances has significantly increased electricity demand. In remote areas where electronic systems are deployed, DC power is essential, making batteries vital for energy storage. Rechargeable batteries are widely used as backup power sources in applications requiring continuous operation, but without appropriate chargers, they become ineffective. Telecommunication systems, crucial for transmitting and receiving information, depend on reliable power. A battery char

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Sylvester, Tirones. "Automatic 12-Volt Battery Charge Controller for Telecommunication Systems." International Journal of Inventive Engineering and Sciences (IJIES) 12, no. 5 (2025): 25–31. https://doi.org/10.35940/ijies.E1104.12050525/.

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<strong>Abstract: </strong>Every day, electronic applications rely on electric energy to perform work. The growing use of electrical appliances has significantly increased electricity demand. In remote areas where electronic systems are deployed, DC power is essential, making batteries vital for energy storage. Rechargeable batteries are widely used as backup power sources in applications requiring continuous operation, but without appropriate chargers, they become ineffective. Telecommunication systems depend on reliable power, which is crucial for transmitting and receiving information. A ch

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T, Girijaprasanna, and Dhanamjayulu C. "A Review on Different State of Battery Charge Estimation Techniques and Management Systems for EV Applications." Electronics 11, no. 11 (2022): 1795. http://dx.doi.org/10.3390/electronics11111795.

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Electric vehicles (EVs) have acquired significant popularity in recent decades due to their performance and efficiency. EVs are already largely acknowledged as the most promising solutions to global environmental challenges and CO2 emissions. Li-ion batteries are most frequently employed in EVs due to their various benefits. An effective Battery Management System (BMS) is essential to improve the battery performance, including charging–discharging control, precise monitoring, heat management, battery safety, and protection, and also an accurate estimation of the State of Charge (SOC). The SOC

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