Data-driven Comprehensive Evaluation of Lithium-ion Battery State of Health and Abnormal Battery Screening

doi:10.3901/JME.2021.14.141

Abstract

Abstract: Lithium-ion batteries are the most important part of electric vehicles and energy storage systems, and their health management and fault identification are critical to operation and maintenance. The data-driven method is more suitable for large-scale engineering applications than the model-based method. Aiming at scenarios with complex working conditions and poor data quality in practical applications, a data-driven comprehensive evaluation of lithium-ion battery state of health and abnormal battery screening algorithm are proposed. First, a novel feature extraction scheme is proposed for the actual operating conditions of batteries, which can be applied to unstable working conditions with non-constant current. A comprehensive state of health scoring system based on multi-dimensional features and hybrid clustering algorithms is developed. This scheme is an algorithm framework for unsupervised learning, which does not require high quantity and quality of extractable features, without prior model training and complicated hyper parameter adjustment. Then, the algorithm is verified at the public data set of Massachusetts Institute of Technology and Stanford. Based on the feature set of each stage of the battery life cycle, the health level prediction can be achieved, and the accuracy is more than 92% when applied to classify the health level. Finally, the proposed algorithm is implemented in a user-side energy storage power station. Early operation data can be used to quickly screen abnormal batteries, which is beneficial to early maintenance, and improve the safety and economy of the battery system.

Key words: lithium-ion battery, feature extraction, state of health, abnormal battery screening, prognostics and health management

CLC Number:

TM912

JIA Jun, HU Xiaosong, DENG Zhongwei, XU Huachi, XIAO Wei, HAN Feng. Data-driven Comprehensive Evaluation of Lithium-ion Battery State of Health and Abnormal Battery Screening[J]. Journal of Mechanical Engineering, 2021, 57(14): 141-149,159.

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