Review on Techniques for Power Battery State of Health Estimation Driven by Big Data Methods

Review on Techniques for Power Battery State of Health Estimation Driven by Big Data Methods

Battery management systems

Big data

Data acquisition

Energy utilization

Extraction

Parameter estimation

Petroleum reservoir evaluation

Risk perception

Secondary batteries

State of health estimation of power batteries is one of the key algorithms of the battery management systems, which is of great significance for improving power battery energy utilization efficiency, reducing thermal runaway risk, as well as power battery maintenance and residual value evaluation. Comparative analysis has been done on experimental-based, model-based and data-driven methods, and data-driven methods are elaborated from three aspects: dataset construction, health indicators extraction, model establishment. The big data collection methods and data preprocessing methods are summarized. The health indicators extraction methods are compared by their pros and cons and applicable scenarios. The basic principles of different health state estimation models are discussed. The conclusion that model fusion is the direction of future technology development is proposed. Finally, facing the future application scenarios of big data in electric vehicles, the current issue and prospective are depicted. 2023 Editorial Office of Chinese Journal of Mechanical Engineering. All rights reserved.

151-168

2

59

Wang, Zhenpo

Wang, Qiushi

Liu, Peng

Zhang, Zhaosheng

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering

2023

journalArticle

5776686

10.3901/JME.2023.02.151

http://dx.doi.org/10.3901/JME.2023.02.151