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Title: Research on residual life prediction method of lithium ion battery for pure electric vehicle

Authors: Zhiwen An

Addresses: College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan 523083, China

Abstract: To overcome the complexity of the lithium-ion battery inside the chemical reaction resulting in a low battery life remaining prediction accuracy, the paper proposes a new electric vehicle lithium ion battery remaining life prediction method based on a correlation vector machine. According to the operating characteristics of lithium-ion batteries in electric vehicles, this method selects health factors that affect battery life, and selects related factors. According to the marginal likelihood function, the factor weights are integrated to obtain the health factor sequence target. Relevance vector machine is used to optimise and evaluate the characteristics of health factors, and complete the prediction of electric vehicle lithium-ion battery capacity and remaining battery life. Comparative experiments show that the prediction effect and stability of the method in this paper are better, and the minimum prediction error is only 0.013.

Keywords: correlation vector machine; lithium ion battery; pure electric vehicle; residual life; prediction effect.

DOI: 10.1504/IJMPT.2021.10039891

International Journal of Materials and Product Technology, 2021 Vol.63 No.1/2, pp.86 - 95

Received: 27 Nov 2020
Accepted: 04 Feb 2021

Published online: 02 Aug 2021 *

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