Overview of Research on Degradation Mechanism and State of Health Estimation for Traction Battery in New Energy Vehicles

doi:10.3901/JME.2024.22.241

Abstract

Abstract: Lithium-ion batteries as the core component of new energy vehicles(NEVs), accurate and efficient degradation mechanism identification and state of health(SOH) estimation are of great significance for improving the operational reliability of traction battery systems, reducing safety risks and evaluating residual values. With the increasing degree of intelligent network connections for NEVs and the rapid development of big data analysis technology, data-driven based SOH estimation has gained widespread attention. In order to systematically sort out the latest progress in research on the decline mechanism and health state estimation of lithium-ion batteries, the following two aspects are summarized. Regarding the ageing mechanism, the effects of different internal side reactions on lithium-ion battery degradation are discussed based on the anode, cathode and other battery structures, and combined with the actual operation scenario of NEVs to analyze the dominant role of strongly associated external factors on battery degradation. As for the SOH diagnosis, an overview of existing research is categorized according to the characteristics and focus of different data-driven algorithms, their advantages, limitations and application scenarios are analyzed and compared, and further discussed the feasibility of typical methods in the current stage of real vehicle application. Finally, the challenges and development directions in the field of SOH estimation research are summarized and prospected for the actual operation requirements of NEVs.

Key words: new energy vehicle, traction battery, degradation mechanism, state of health, data-driven

CLC Number:

TM912

ZHANG Dayu, WANG Zhenpo, LIU Peng, LIN Ni, ZHANG Zhaosheng. Overview of Research on Degradation Mechanism and State of Health Estimation for Traction Battery in New Energy Vehicles[J]. Journal of Mechanical Engineering, 2024, 60(22): 241-256.

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