Towards a smarter battery management system: A critical review on battery state of health monitoring methods
By Rui Xiong, Linlin Li, Jinpeng Tian. Journal of Power Sources, 2018
868 citations recorded by the source corpus.
Abstract To ensure the driving safety and avoid potential failures for electric vehicles, evaluating the health state of the battery properly is of significant importance. This study aims to serve as a useful support for researchers and practitioners by systematically reviewing the available literature on state of health estimation methods. These methods can be divided into two types: experimental and model-based estimation methods. Experimental methods are conducted in a laboratory environment to analyze battery aging process and provide theoretical support for model-based methods. Based on a battery model, model-based estimation methods identify the parameters, which have certain relationships with battery aging level, to realize state of health estimation. On the basis of reading extensive literature, methods for determining the health state of the battery are explained in a deeper way, while their corresponding strengths and weaknesses of these methods are analyzed in this paper. At the end of the paper, conclusions for these methods and prospects for the development trend of health state estimation are made.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/fbe6c0f413f7da98dda19dec169bf03b5bb69262
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Citations in the corpus
- Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “preprocessing are the foundation and key to the accuracy of SOH estimation [18].”
- Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “(2018) [18] General review on SOH estimation methods Berecibar et al.”
- Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries (2020, methodology): “model-based methods could achieve good robustness and are easily applied for online battery energy storage with different chemistries [9].”