State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches

Abstract

Precise estimation of both state-of-charge (SoC) and state-of-health (SoH) is crucial for optimizing electric vehicle (EV) performance and enhancing the battery lifetime, safety, and reliability, where machine learning (ML) plays a vital role in this regard. While existing surveys explore ML applications in EVs, they often need to address ML approaches for SoC and SoH estimation. This paper bridges this gap by comprehensively reviewing how ML is utilized for SoC and SoH estimation, analyzing their strengths and weaknesses across different battery chemistries. Our review offers a systematic breakdown of critical areas: fundamental concepts and functionalities of prominent ML techniques for estimating SoC and SoH, a comparative evaluation of ML techniques applied to diverse EV battery types, an exploration of SoC and SoH estimation using modeling approaches within EV battery systems, and the critical role of dataset quality and model evaluation criteria. Moreover, this paper addresses ML tools developed for lithium-ion batteries (LiBs), image processing applications in EV batteries, and an in-depth investigation of the system model for ML-based SoH and SoC estimation. Furthermore, we present key concepts and methods for SoH and SoC estimation utilizing ML, compare input features, metrics, hyperparameters, and datasets, and demonstrate ML-based system models for EV battery estimation. By conducting this thorough analysis, we aim to close the existing gap and stimulate future progress in ML for SoH and SoC estimation, primarily focusing on LiBs across different EV applications.

Keywords

• Machine learning
• electric vehicles
• state-of-health
• state-of-charge
• lithium-ion batteries