Review of Various Machine Learning Approaches for Predicting Parameters of Lithium-Ion Batteries in Electric Vehicles

• Published in: Batteries (Basel) Vol. 10; no. 6; p. 181
• Main Authors: Shan, Chunlai, Chin, Cheng Siong, Mohan, Venkateshkumar, Zhang, Caizhi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2024
• Subjects: Aging; Algorithms; Artificial intelligence; Automobiles, Electric; Batteries; battery management systems; Efficiency; Electric vehicles; Equivalent circuits; Estimation; Fuzzy logic; Genetic algorithms; K-nearest neighbors algorithm; Lithium; Lithium cells; Lithium-ion batteries; Machine learning; Management systems; Power management; Product life cycle; R&D; Rechargeable batteries; Research & development; State of charge; state of health; Support vector machines; China
• ISSN: 2313-0105; 2313-0105
• DOI: 10.3390/batteries10060181

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.