Lithium-Ion Batteries for Electric Vehicle

Special Issue Editors

Department of Automation, University of Science and Technology of China, Hefei, China
Interests: power systems of new energy vehicles; modelling, simulation, and control of hybrid energy system; management and optimization control of fuel cell systems
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Guest Editor
Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
Interests: lithium batteries; energy storage; battery; kalman filtering; electrical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Featuring high energy and power density, a long lifespan, and a continuously decreasing cost, Li-ion batteries are regarded as the key energy storage components for electric vehicles. As is the case with most electrochemical systems, Li-ion batteries are highly nonlinear systems with complicated physical and chemical reactions. They are fragile to external factors, such as voltage, current, temperature, vibration, and humidity. The internal states of the batteries are mostly unmeasurable with the existing commercial sensors. Issues such as ultrafast charging, lifespan, second-life utilization, and reliability under extreme temperatures remain unsolved. Therefore, the intelligent control and management of these batteries are critical to the safe, fluent, and efficient use of these batteries.

This Special Issue will highlight recent studies related to Li-ion batteries that could potentially advance their use in electric vehicles. Topics of interest include but are not limited to:

  • Battery materials, design, and manufacturing;
  • Battery analysis, testing, and modeling;
  • Battery sorting, grouping, and grading;
  • Battery control, monitoring, charging, and maintenance;
  • Battery state estimation and life cycle assessment;
  • Battery thermal management and safety control;
  • Battery control in hybrid systems and V2X systems;
  • Battery infrastructure for electric vehicles;
  • Energy policy for batteries in electric vehicles.

Dr. Yujie Wang
Dr. Xiaopeng Tang
Guest Editors

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Keywords

  • battery management system
  • lithium-ion batteries
  • electric vehicles

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Published Papers (13 papers)

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Research

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17 pages, 2655 KiB  
Article
Optimizing Electric Racing Car Performance through Telemetry-Integrated Battery Charging: A Response Surface Analysis Approach
by A. F. Villa-Salazar, I. N. Gomez-Miranda, A. F. Romero-Maya, J. D. Velásquez-Gómez and K. Lemmel-Vélez
World Electr. Veh. J. 2024, 15(7), 317; https://doi.org/10.3390/wevj15070317 - 18 Jul 2024
Viewed by 2667
Abstract
The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The objective of [...] Read more.
The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The objective of this research was to integrate telemetry into the battery bank of an electric racing car in order to find the optimal values of current and voltage that optimize the charging process and thus improve the performance of the vehicle in competition using Response Surface Analysis. Specifically, the telemetry system consisted of an Arduino Mega, a digital wattmeter, and temperature sensors, all installed in the vehicle. Once the telemetry data were obtained, a response surface design was fitted with current, voltage, and temperature as factors varying from low to high values, with the objective function being to minimize the battery charging time. Using the response surface methodology and the steepest descent algorithm, it was found that all factors significantly affect the charging time, with the minimum charging time being 6961 s, obtained with a current of 2.4 amps and voltages of 50.5 volts and 43.6 volts. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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26 pages, 2373 KiB  
Article
Advancements in Battery Management Systems for Electric Vehicles: A MATLAB-Based Simulation of 4S3P Lithium-Ion Battery Packs
by Rakesh P. Tapaskar, Prashant P. Revankar and Sharanabasava V. Ganachari
World Electr. Veh. J. 2024, 15(6), 222; https://doi.org/10.3390/wevj15060222 - 21 May 2024
Cited by 1 | Viewed by 1773
Abstract
As electric vehicles (EVs) gain momentum in the shift towards sustainable transportation, the efficiency and reliability of energy storage systems become paramount. Lithium-ion batteries stand at the forefront of this transition, necessitating sophisticated battery management systems (BMS) to enhance their performance and lifespan. [...] Read more.
As electric vehicles (EVs) gain momentum in the shift towards sustainable transportation, the efficiency and reliability of energy storage systems become paramount. Lithium-ion batteries stand at the forefront of this transition, necessitating sophisticated battery management systems (BMS) to enhance their performance and lifespan. This research presents an innovative simulation of a 4S3P lithium-ion battery pack using MATLAB R2023b, designed to refine BMS capabilities by employing advanced mathematical modelling and computational intelligence. The simulation meticulously analyses critical operational metrics such as state of charge (SOC), state of health (SOH), temperature variations, and electrical behaviour under diverse load scenarios, offering deep insights into the intricate dynamics of lithium-ion batteries in EV applications. The results corroborate the simulation model’s accuracy in reflecting actual battery pack performance and underscore significant improvements in BMS strategies, especially concerning predictive maintenance and adaptive charging techniques. By seamlessly integrating computational intelligence into BMS, this study lays the groundwork for more durable, efficient, and intelligent energy storage systems in electric vehicles, marking a significant stride in e-mobility technology. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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21 pages, 7171 KiB  
Article
A Lithium-Ion Battery Remaining Useful Life Prediction Model Based on CEEMDAN Data Preprocessing and HSSA-LSTM-TCN
by Shaoming Qiu, Bo Zhang, Yana Lv, Jie Zhang and Chao Zhang
World Electr. Veh. J. 2024, 15(5), 177; https://doi.org/10.3390/wevj15050177 - 24 Apr 2024
Cited by 1 | Viewed by 1149
Abstract
Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for reducing battery usage risks and ensuring the safe operation of systems. Addressing the impact of noise and capacity regeneration-induced nonlinear features on RUL prediction accuracy, this paper proposes a [...] Read more.
Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for reducing battery usage risks and ensuring the safe operation of systems. Addressing the impact of noise and capacity regeneration-induced nonlinear features on RUL prediction accuracy, this paper proposes a predictive model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) data preprocessing and IHSSA-LSTM-TCN. Firstly, CEEMDAN is used to decompose lithium-ion battery capacity data into high-frequency and low-frequency components. Subsequently, for the high-frequency component, a Temporal Convolutional Network (TCN) prediction model is employed. For the low-frequency component, an Improved Sparrow Search Algorithm (IHSSA) is utilized, which incorporates iterative chaotic mapping and a variable spiral coefficient to optimize the hyperparameters of Long Short-Term Memory (LSTM). The IHSSA-LSTM prediction model is obtained and used for prediction. Finally, the predicted values of the sub-models are combined to obtain the final RUL result. The proposed model is validated using the publicly available NASA dataset and CALCE dataset. The results demonstrate that this model outperforms other models, indicating good predictive performance and robustness. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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20 pages, 9302 KiB  
Article
Research on Thermal Runaway Characteristics of High-Capacity Lithium Iron Phosphate Batteries for Electric Vehicles
by Qing Zhu, Kunfeng Liang and Xun Zhou
World Electr. Veh. J. 2024, 15(4), 147; https://doi.org/10.3390/wevj15040147 - 3 Apr 2024
Viewed by 1556
Abstract
With the rapid development of the electric vehicle industry, the widespread utilization of lithium-ion batteries has made it imperative to address their safety issues. This paper focuses on the thermal safety concerns associated with lithium-ion batteries during usage by specifically investigating high-capacity lithium [...] Read more.
With the rapid development of the electric vehicle industry, the widespread utilization of lithium-ion batteries has made it imperative to address their safety issues. This paper focuses on the thermal safety concerns associated with lithium-ion batteries during usage by specifically investigating high-capacity lithium iron phosphate batteries. To this end, thermal runaway (TR) experiments were conducted to investigate the temperature characteristics on the battery surface during TR, as well as the changes in battery mass and expansion rate before and after TR. Meanwhile, by constructing a TR simulation model tailored to lithium iron phosphate batteries, an analysis was performed to explore the variations in internal material content, the proportion of heat generation from each exothermic reaction, and the influence of the heat transfer coefficient during the TR process. The results indicate that as the heating power increases, the response time of lithium-ion batteries to TR advances. Furthermore, the heat released from the negative electrode–electrolyte reaction emerges as the primary heat source throughout the entire TR process, contributing to 63.1% of the total heat generation. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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14 pages, 2804 KiB  
Article
A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery
by Wenming Dai, Yong Xiang, Wenyi Zhou and Qiao Peng
World Electr. Veh. J. 2024, 15(2), 72; https://doi.org/10.3390/wevj15020072 - 18 Feb 2024
Viewed by 1449
Abstract
Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by [...] Read more.
Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R2 while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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18 pages, 5587 KiB  
Article
Thermal Performance Enhancement of Lithium-Ion Batteries Using Phase Change Material and Fin Geometry Modification
by Sarmad Ali, Muhammad Mahabat Khan and Muhammad Irfan
World Electr. Veh. J. 2024, 15(2), 42; https://doi.org/10.3390/wevj15020042 - 30 Jan 2024
Cited by 1 | Viewed by 1771
Abstract
The rapid increase in emissions and the depletion of fossil fuels have led to a rapid rise in the electric vehicle (EV) industry. Electric vehicles predominantly rely on lithium-ion batteries (LIBs) to power their electric motors. However, the charging and discharging processes of [...] Read more.
The rapid increase in emissions and the depletion of fossil fuels have led to a rapid rise in the electric vehicle (EV) industry. Electric vehicles predominantly rely on lithium-ion batteries (LIBs) to power their electric motors. However, the charging and discharging processes of LIB packs generate heat, resulting in a significant decline in the battery performance of EVs. Consequently, there is a pressing need for effective battery thermal management systems (BTMSs) for lithium-ion batteries in EVs. In the current study, a novel experimental BTMS was developed for the thermal performance enhancement of an LIB pack comprising 2 × 2 cells. Three distinct fin configurations (circular, rectangular, and tapered) were integrated for the outer wall of the lithium-ion cells. Additionally, the cells were fully submerged in phase change material (PCM). The study considered 1C, 2C, and 3C cell discharge rates, affiliated with their corresponding volumetric heat generation rates. The combination of rectangular fins and PCM manifested superior performance, reducing the mean cell temperature by 29.71% and 28.36% compared to unfinned lithium-ion cells under ambient conditions at the 1C and 2C discharge rates. Furthermore, at the 3C discharge rate, lithium-ion cells equipped with rectangular fins demonstrated a delay of 40 min in reaching the maximum surface temperature of 40 °C compared to the unfinned ambient case. After 60 min of battery discharge at the 3C rate, the cell surface temperature of the rectangular fin case only reached 42.7 °C. Furthermore, numerical simulations showed that the Nusselt numbers for lithium-ion cells with rectangular fins improved by 9.72% compared to unfinned configurations at the 3C discharge rate. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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19 pages, 6757 KiB  
Article
Numerical Investigation of Heat Production in the Two-Wheeler Electric Vehicle Battery via Torque Load Variation Test
by Hariyotejo Pujowidodo, Bambang Teguh Prasetyo, Respatya Teguh Soewono, Himawan Sutriyanto, Achmad Maswan, Muhammad Penta Helios, Kanon Prabandaru Sumarah, Bhakti Nuryadin, Andhy Muhammad Fathoni, Dwi Handoko Arthanto, Riki Jaka Komara, Agus Prasetyo Nuryadi, Fitrianto, Chairunnisa and I.G.A. Uttariyani
World Electr. Veh. J. 2024, 15(1), 13; https://doi.org/10.3390/wevj15010013 - 2 Jan 2024
Cited by 1 | Viewed by 1655
Abstract
Experimental studies were conducted to investigate the effect of varying torque loads on the temperature distribution on the surface of lithium-ion batteries (72 volts–20 Ah) in real commercial two-wheeler electric vehicles as part of our previous research. An electric vehicle engine was installed [...] Read more.
Experimental studies were conducted to investigate the effect of varying torque loads on the temperature distribution on the surface of lithium-ion batteries (72 volts–20 Ah) in real commercial two-wheeler electric vehicles as part of our previous research. An electric vehicle engine was installed in a dyno testing laboratory and used as the main load for the battery. Ambient temperature and relative humidity were controlled using an air conditioning system. The test results are presented as surface temperature distributions on each side of the battery at various torque loads. The highest temperature on the battery’s surface was found to be approximately 40 °C at a torque load of 100%. Unfortunately, the heat generated by the battery during testing could not be measured for further research. This paper presents a numerical study of battery heat generation at 100% torque load using Ansys Fluent 2020 R1©. This tool is employed to calculate the heat flux from the battery surface to the ambient air. The CFD tool was initially validated against available experimental data and commonly used correlations for natural convection along a vertically heated wall. Good agreements between the current predictions and experimental data were observed for laminar flow regimes. Convective heat transfer between the battery surface and ambient air was simulated. The results indicate that the commonly used heat transfer correlation for vertical plates accurately predicts the heat transfer rate on the battery surface, and it was found that the heat generation rate is 1199 W/m3. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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10 pages, 1494 KiB  
Article
High-Speed Laser Drying of Lithium-Ion Battery Anodes: Challenges and Opportunities
by Samuel Fink, Delil Demir, Markus Börner, Vinzenz Göken and Christian Vedder
World Electr. Veh. J. 2023, 14(9), 255; https://doi.org/10.3390/wevj14090255 - 9 Sep 2023
Cited by 4 | Viewed by 2956
Abstract
In modern electrode manufacturing for lithium-ion batteries, the drying of the electrode pastes consumes a considerable amount of space and energy. To increase the efficiency of the drying process and reduce the footprint of the drying equipment, a laser-based drying process is investigated. [...] Read more.
In modern electrode manufacturing for lithium-ion batteries, the drying of the electrode pastes consumes a considerable amount of space and energy. To increase the efficiency of the drying process and reduce the footprint of the drying equipment, a laser-based drying process is investigated. Evaporation rates of up to 318 g m−2 s−1 can be measured, which is orders of magnitude higher than the evaporation rates in conventional furnace drying processes. Optical measurements of the slurry components in the visible and near-infrared spectrum are conducted. Thermal analyses the of laser-dried samples reveal that the commonly used binders carboxymethyl-cellulose (CMC) and styrene–butadiene rubber (SBR) are not affected by the laser drying process within the investigated process window. The results indicated that with the combination of a fast laser drying step and a subsequent convection drying step, high evaporation rates can be achieved while maintaining the integrity and adhesion of the anode. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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13 pages, 2544 KiB  
Article
Research on Calendar Aging for Lithium-Ion Batteries Used in Uninterruptible Power Supply System Based on Particle Filtering
by Wei Xu and Hongzhi Tan
World Electr. Veh. J. 2023, 14(8), 209; https://doi.org/10.3390/wevj14080209 - 8 Aug 2023
Cited by 2 | Viewed by 1746
Abstract
The aging process of lithium-ion batteries is an extremely complex process, and the prediction of the calendar life of the lithium-ion battery is important to further guide battery maintenance, extend the battery life and reduce the risk of battery use. In the uninterruptible [...] Read more.
The aging process of lithium-ion batteries is an extremely complex process, and the prediction of the calendar life of the lithium-ion battery is important to further guide battery maintenance, extend the battery life and reduce the risk of battery use. In the uninterruptible power supply (UPS) system, the battery is in a floating state for a long time, so the aging of the battery is approximated by calendar aging, and its decay rate is slow and difficult to estimate accurately. This paper proposes a particle filtering-based algorithm for battery state-of-health (SOH) and remaining useful life (RUL) predictions. First, the calendar aging modeling for the batteries used in the UPS system for the Shanghai rail transportation energy storage power station is presented. Then, the particle filtering algorithm is employed for the SOH estimation and RUL prediction for the single-cell battery calendar aging model. Finally, the single-cell SOH and RUL estimation algorithm is expanded to the pack and group scales estimation. The experimental results indicate that the proposed method can achieve accurate SOH estimation and RUL prediction results. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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15 pages, 4722 KiB  
Article
State of Health Assessment for Lithium-Ion Batteries Using Incremental Energy Analysis and Bidirectional Long Short-Term Memory
by Yanmei Li, Laijin Luo, Chaolong Zhang and Huihan Liu
World Electr. Veh. J. 2023, 14(7), 188; https://doi.org/10.3390/wevj14070188 - 14 Jul 2023
Cited by 14 | Viewed by 2950
Abstract
The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this [...] Read more.
The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this difficulty, in this paper we propose a method for estimating battery SOH based on incremental energy analysis (IEA) and bidirectional long short-term memory (BiLSTM). First, the IE curve that effectively describes the complex chemical characteristics of the battery is obtained according to the energy data calculated from the constant current (CC) charging phase. Then, the relationship between the IE curve and battery SOH degradation characteristics is analyzed and the peak height of the IE curve is extracted as the aging characteristic of the battery. Further, Pearson correlation analysis is utilized to determine the linear correlation between the proposed aging characteristics and the battery SOH. Finally, BiLSTM is employed to capture the underlying mapping relationship between peak characteristics and SOH, and a battery SOH estimation model is developed. The results demonstrate that the proposed method is able to estimate battery SOH under two different charging conditions with a root mean square error less than 0.5% and coefficient of determination above 98%. Additionally, the method is combined with Pearson correlation analysis to select an aging characteristic with high correlation, reducing the required data input and computational burden. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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13 pages, 2318 KiB  
Article
An Improved Gaussian Process Regression Based Aging Prediction Method for Lithium-Ion Battery
by Weiwei Qu, Hu Deng, Yi Pang and Zhanfeng Li
World Electr. Veh. J. 2023, 14(6), 153; https://doi.org/10.3390/wevj14060153 - 9 Jun 2023
Viewed by 1764
Abstract
A reliable aging-prediction method is significant for lithium-ion batteries (LIBs) to prolong the service life and increase the efficiency of operation. In this paper, an improved Gaussian-process regression (GPR) is proposed to predict the degradation rate of LIBs under coupled aging stress to [...] Read more.
A reliable aging-prediction method is significant for lithium-ion batteries (LIBs) to prolong the service life and increase the efficiency of operation. In this paper, an improved Gaussian-process regression (GPR) is proposed to predict the degradation rate of LIBs under coupled aging stress to simulate working conditions. The complicated degradation processes at different ranges of the state of charge (SOC) under different discharge rates were analyzed. A composed kernel function was conducted to optimize the hyperparameter. The inputs for the kernel function of GPR were improved by coupling the constant and variant characteristics. Moreover, previous aging information was employed as a characteristic to improve the reliability of the prediction. Experiments were conducted on a lithium–cobalt battery at three different SOC ranges under three discharge rates to verify the performance of the proposed method. Some tips to slow the aging process based on the coupled stress were discovered. Results show that the proposed method accurately estimated the degradation rate with a maximum estimation root-mean-square error of 0.14% and regression coefficient of 0.9851. Because of the proposed method’s superiority to the exponential equation and GPR by fitting all cells under a different operating mode, it is better for reflecting the true degradation in actual EV. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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15 pages, 5049 KiB  
Article
An Energy Management Strategy for Hybrid Energy Storage System Based on Reinforcement Learning
by Yujie Wang, Wenhuan Li, Zeyan Liu and Ling Li
World Electr. Veh. J. 2023, 14(3), 57; https://doi.org/10.3390/wevj14030057 - 24 Feb 2023
Cited by 5 | Viewed by 2698
Abstract
Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and [...] Read more.
Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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Review

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25 pages, 1882 KiB  
Review
A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning
by Feng Zhao, Yun Guo and Baoming Chen
World Electr. Veh. J. 2024, 15(4), 131; https://doi.org/10.3390/wevj15040131 - 25 Mar 2024
Cited by 5 | Viewed by 2525
Abstract
With the advancement of machine-learning and deep-learning technologies, the estimation of the state of charge (SOC) of lithium-ion batteries is gradually shifting from traditional methodologies to a new generation of digital and AI-driven data-centric approaches. This paper provides a comprehensive review of the [...] Read more.
With the advancement of machine-learning and deep-learning technologies, the estimation of the state of charge (SOC) of lithium-ion batteries is gradually shifting from traditional methodologies to a new generation of digital and AI-driven data-centric approaches. This paper provides a comprehensive review of the three main steps involved in various machine-learning-based SOC estimation methods. It delves into the aspects of data collection and preparation, model selection and training, as well as model evaluation and optimization, offering a thorough analysis, synthesis, and summary. The aim is to lower the research barrier for professionals in the field and contribute to the advancement of intelligent SOC estimation in the battery domain. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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