An Indirect State-of-Health Estimation Method Based on Improved Genetic and Back Propagation for Online Lithium-Ion Battery Used in Electric Vehicles



Li, Ning, He, Fuxing, Ma, Wentao, Wang, Ruotong, Jiang, Lin ORCID: 0000-0001-6531-2791 and Zhang, Xiaoping
(2022) An Indirect State-of-Health Estimation Method Based on Improved Genetic and Back Propagation for Online Lithium-Ion Battery Used in Electric Vehicles. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 71 (12). pp. 12682-12690.

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Abstract

Lithium-ion battery state of health (SOH) estimation technology is an important part of the design of a battery monitoring system (BMS) for electric vehicles. People often use battery capacity and internal resistance as SOH estimation indicators. However, due to actual working conditions, it is difficult for electric vehicles to achieve complete charge and discharge, so the battery capacity and internal resistance cannot be monitored online. In view of the above questions, this article proposes an indirect SOH estimation method for online EV lithium-ion batteries based on arctangent function adaptive genetic algorithm combination with back propagation neural network (ATAGA-BP). Firstly, constant current drop time (CCDT), constant current drop capacity (CCDC) and maximum constant current drop rate (MCCDR) in constant voltage charging stage are used as health indicator (HI) to evaluate battery SOH in order to indirectly quantify the degradation process of lithium-ion batteries. Error point optimization and correlation verification are also carried out. Secondly, an ATAGA-BP algorithm is proposed to establish the relationship between HI and available battery capacity, and SOH estimate is made for lithium-ion batteries according to the proposed algorithm. Finally, simulation results with NASA data show the correlation between the proposed HI and lithium-ion battery capacity is above 85%, the error of SOH estimation method proposed is 3.7%, and the iteration efficiency is increased by 17.8%.

Item Type: Article
Uncontrolled Keywords: Artan function adaptive genetic algorithm, electric vehicles, health indicator, lithium-ion battery, state of health estimation
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 08 Aug 2022 14:40
Last Modified: 06 Feb 2023 01:50
DOI: 10.1109/TVT.2022.3196225
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3160718