A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks

Jichao Hong, Kerui Li, Fengwei Liang, Haixu Yang, Chi Zhang, Qianqian Yang and Jiegang Wang

Energy, 2024, vol. 289, issue C

Abstract: State of health (SOH) is crucial to battery management system. However, SOH accuracy and reliability is difficult to be guaranteed owing to complex ageing mechanisms and driving conditions. This paper proposes a novel battery SOH prediction method based on gated recurrent unit (GRU) neural network for real-world vehicles. Firstly, real-vehicle operating data is extracted as well as cleaned and sliced to improve data reliability, then the Kalman filter and recursive least squares method are used to identify the ohmic internal resistance (OIR). The GRU neural network is established to realize the battery SOH prediction, and the average relative error of final validation set is less than 0.65 %. To further validate the reliability and applicability of the prediction method, four other vehicles data are set as input, and the SOH results indicate that all average relative errors are still within 4 %. This study demonstrates the feasibility of using OIR as a health factor for real-world vehicles, and also provides a new solution for accurately predicting battery SOH for real-world vehicles.

Keywords: Electric vehicles; Battery system; State of health; Ohmic internal resistance; Gated recurrent unit (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:289:y:2024:i:c:s0360544223033121

DOI: 10.1016/j.energy.2023.129918

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