Review of Various Machine Learning Approaches for Predicting Parameters of Lithium-Ion Batteries in Electric Vehicles
Chunlai Shan,
Cheng Siong Chin,
Venkateshkumar Mohan,
Caizhi Zhang
Affiliations
Chunlai Shan
Northwest Institute of Mechanical and Electrical Engineering, Xianyang 712099, China
Cheng Siong Chin
Faculty of Science, Agriculture, and Engineering, Newcastle University in Singapore, Singapore 599493, Singapore
Venkateshkumar Mohan
Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India
Caizhi Zhang
The State Key Laboratory of Mechanical Transmissions, Chongqing Automotive Collaborative Innovation Center, School of Automotive Engineering, Chongqing University, Chongqing 400044, China
Battery management systems (BMSs) play a critical role in electric vehicles (EVs), relying heavily on two essential factors: the state of charge (SOC) and state of health (SOH). However, accurately estimating the SOC and SOH in lithium-ion (Li-ion) batteries remains a challenge. To address this, many researchers have turned to machine learning (ML) techniques. This study provides a comprehensive overview of both BMSs and ML, reviewing the latest research on popular ML methods for estimating the SOC and SOH. Additionally, it highlights the challenges involved. Beyond traditional models like equivalent circuit models (ECMs) and electrochemical battery models, this review emphasizes the prevalence of a support vector machine (SVM), fuzzy logic (FL), k-nearest neighbors (KNN) algorithm, genetic algorithm (GA), and transfer learning in SOC and SOH estimation.
