State Estimation and Efficient Charging Strategies for Lithium-Ion Batteries in Electric Vehicles

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Guest Editor
School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Interests: lithium compounds; secondary cells; frequency control; power engineering computing; power grids; battery management systems
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E-Mail Website
Guest Editor
School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Interests: energy storage optimal configuration and control technology; power grids; battery management systems; data mining; distributed power generation; power apparatus; power distribution reliability
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Energy and Electrical Engineering, Chang'an University, Xi'an 710018, China
Interests: lithium-ion battery energy storage and applications; electric vehicle power conversion technology; transportation and energy integration technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electric vehicles have achieved rapid development and popularity in the passenger car market due to their superior driving performance and green environmental characteristics. Lithium-ion battery packs are the power core of electric vehicles, though the degradation mechanism of lithium-ion batteries is complex, making it difficult to estimate the state of health and safety of such batteries. Improper charging and discharging control can easily lead to safety issues with such batteries. Conducting research on state estimation and charging and discharging optimization strategies for batteries is vital for enhancing the competitiveness and safety of new energy vehicles.

This Special Issue will provide an outlet for novel and original research on all aspects of prognostics and health management of lithium-ion batteries for electric vehicles, as well as electric vehicle charging circuit topology and electric vehicle charging strategy optimization, including experiments, characterization, mechanisms, modeling, algorithms, systems, etc.

Dr. Jichang Peng
Dr. Jinhao Meng
Prof. Dr. Haitao Liu
Dr. Xinrong Huang
Guest Editors

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Keywords

  • electric vehicle
  • state of health estimation
  • prognostics and health management
  • lithium-ion batteries
  • lifetime test and analysis
  • aging mechanism
  • thermal management
  • electro-thermal coupling model
  • big data
  • empirical degradation model
  • fast charging optimizationearly failure warning
  • V2G

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Published Papers (1 paper)

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Research

19 pages, 4505 KiB  
Article
State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine
by Jichang Peng, Ya Gao, Lei Cai, Ming Zhang, Chenghao Sun and Haitao Liu
World Electr. Veh. J. 2025, 16(4), 224; https://doi.org/10.3390/wevj16040224 - 9 Apr 2025
Viewed by 433
Abstract
An accurate state of health (SOH) estimation for lithium-ion batteries (LIBs) is crucial for reliable operations and extending service life. While electrochemical impedance spectroscopy (EIS) effectively characterizes LIBs degradation patterns, the high dimensionality of EIS data poses challenges for an efficient analysis. This [...] Read more.
An accurate state of health (SOH) estimation for lithium-ion batteries (LIBs) is crucial for reliable operations and extending service life. While electrochemical impedance spectroscopy (EIS) effectively characterizes LIBs degradation patterns, the high dimensionality of EIS data poses challenges for an efficient analysis. This study proposes a novel method that combines EIS with an equivalent circuit model (ECM) and distribution of relaxation time (DRT) analysis to extract low-dimensional health features from high-dimensional EIS data. A multi-scale kernel extreme learning machine (MS-KELM), optimized by the Sparrow Search Algorithm (SSA), estimates battery SOH with an average mean absolute error (MAE) of 1.37% and a root mean square error (RMSE) of 1.76%. In addition, compared with support vector regression (SVR) and Gaussian process regression (GPR), the proposed method reduces computational time by factors of 4 to 30 and lowers memory usage by approximately 18%. Full article
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