Title: Bayesian optimised route and SOH estimation effect for Li-ion battery management system of electric vehicles based on LSTM

Authors: Zhijun Xiao

Addresses: School of Electrical and Electronic Information Engineering, Hubei Polytechnic University, Huangshi, Hubei, China

Abstract: Lithium-ion batteries are widely used in electric vehicles, and accurate state of health (SOH) estimation is crucial for driving safety. This study applies a long short-term memory (LSTM) algorithm to model SOH based on health features correlated with standardised capacity. Since manual parameter tuning is inefficient and training is time-consuming with large datasets, a domain space design inspired by manual adjustment is combined with Bayesian optimisation for hyperparameter configuration. Experimental results show that the optimised LSTM improves estimation accuracy by 0.0235%. Compared with grid and random search, Bayesian optimisation reduces relative error by 50.63% on average and requires the least time, demonstrating both higher optimisation efficiency and near-optimal parameter selection.

Keywords: LSTM; lithium battery management system; Bayesian optimisation algorithm; SOH estimation; battery health characteristics.

DOI: 10.1504/IJVICS.2026.152933

International Journal of Vehicle Information and Communication Systems, 2026 Vol.11 No.2, pp.146 - 162

Received: 13 Sep 2024
Accepted: 20 Jan 2025
Published online: 15 Apr 2026 *

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