State of Charge and State of Health Estimation in Electric Vehicles: Challenges, Approaches and Future Directions

被引:1
作者
Soyoye, Babatunde D. [1 ]
Bhattacharya, Indranil [1 ]
Dhason, Mary Vinolisha Anthony [1 ]
Banik, Trapa [1 ]
机构
[1] Tennessee Technol Univ, Elect & Comp Engn Dept, Cookeville, TN 38505 USA
来源
BATTERIES-BASEL | 2025年 / 11卷 / 01期
关键词
electric vehicles; state of charge; state of health; battery management systems; electrochemical models; data-driven methods; machine learning; LITHIUM-ION BATTERY; PARTICLE SWARM OPTIMIZATION; CAPACITY DEGRADATION MODEL; EQUIVALENT-CIRCUIT MODELS; RECURRENT NEURAL-NETWORK; UNSCENTED KALMAN FILTER; SUPPORT VECTOR MACHINE; SHORT-TERM-MEMORY; OF-CHARGE; ELECTROCHEMICAL MODEL;
D O I
10.3390/batteries11010032
中图分类号
O646 [电化学、电解、磁化学];
学科分类号
081704 ;
摘要
This critical review paper delves into the complex and evolving landscape of the state of health (SOH) and state of charge (SOC) in electric vehicles (EVs), highlighting the pressing need for accurate battery management to enhance safety, efficiency, and longevity. With the global shift towards EVs, understanding and improving battery performance has become crucial. The paper systematically explores various SOC estimation techniques, emphasizing their importance akin to that of a fuel gauge in traditional vehicles, and addresses the challenges in accurately determining SOC given the intricate electrochemical nature of batteries. It also discusses the imperative of SOH estimation, a less defined but critical parameter reflecting battery health and longevity. The review presents a comprehensive taxonomy of current SOC estimation methods in EVs, detailing the operation of each type and succinctly discussing the advantages and disadvantages of these methods. Furthermore, it scrutinizes the difficulties in applying different SOC techniques to battery packs, offering insights into the challenges posed by battery aging, temperature variations, and charge-discharge cycles. By examining an array of approaches-from traditional methods such as look-up tables and direct measurements to advanced model-based and data-driven techniques-the paper provides a holistic view of the current state and potential future of battery management systems (BMS) in EVs. It concludes with recommendations and future directions, aiming to bridge the gap for researchers, scientists, and automotive manufacturers in selecting optimal battery management and energy management strategies.
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页数:42
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