An overview of data-driven battery health estimation technology for battery management system

Battery degradation, caused by multiple coupled degradation mechanisms, severely affects the safety and sustainability of a battery management system (BMS). The battery state of health (SOH) is a commonly-adopted metric to evaluate a battery's degradation condition, which should be carefully modeled to facil-itate the safety and reliability of a BMS. Recently, owing to the rapid progress of data science-related techniques, data-driven models for battery SOH estimation have attracted great attentions from both aca-demia and industry communities. This paper aims to provide the scientists and engineers with a general overview of data-driven battery SOH estimation technology for BMSs. State-of-the-art models published during 2018-2022 are reviewed with care, including a) feature extraction and selection methods; b) benchmarks, variants and extensions of data-driven SOH estimation models; and c) publicly-available battery SOH datasets. Afterwards, experiments are conducted and analyzed on the Toyota & Stanford-MIT battery SOH datasets for benchmark study. Finally, existing challenges and feature trends are summarized.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Battery degradation has a significant impact on the safety and sustainability of battery management systems. This paper provides an overview of data-driven battery state of health (SOH) estimation technology for BMSs. It reviews state-of-the-art models, feature extraction methods, benchmarks, and publicly-available battery SOH datasets. The study includes experiments and analysis on Toyota & Stanford-MIT battery SOH datasets, highlighting existing challenges and feature trends.