Data driven battery modeling and management method with aging phenomenon considered

Shuangqi Li, Hongwen He, Chang Su and Pengfei Zhao

Applied Energy, 2020, vol. 275, issue C, No S0306261920308527

Abstract: The battery is one of the most important parts of electric vehicles (EVs), and the establishment of an accurate battery state estimation model is of great significance to improve the management strategy of EVs. However, the battery degrades with the operation of EVs, which brings great difficulties for the battery modeling issue. This paper proposes a novel aging phenomenon considered vehicle battery modeling method by utilizing the cloud battery data. First of all, based on the Rain-flow cycle counting (RCC) algorithm, a battery aging trajectory extraction method is developed to quantify the battery degradation phenomenon and generate the aging index for the cloud battery data. Then, the deep learning algorithm is employed to mine the aging features of the battery, and based on the mined aging features, an aging phenomenon considered battery model is established. The actual operation data of electric buses in Zhengzhou is used to validate the practical performance of the proposed methodologies. The results show that the proposed modeling method can simulate the characteristic of the battery accurately. The terminal voltage and SoC estimation error can be limited within 2.17% and 1.08%, respectively.

Keywords: Electric vehicles; Battery energy storage; Battery management system; Deep learning; Battery degradation quantification; Aging-considered battery model (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (21)

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DOI: 10.1016/j.apenergy.2020.115340

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