Enhanced multi-state estimation methods for lithium-ion batteries considering temperature uncertainties.

Takyi-Aninakwa, Paul; Wang, Shunli; Zhang, Hongying; Xiao, Yang; Fernandez, Carlos

Abstract

Due to their high energy density and minimal emissions, lithium-ion batteries are frequently employed in electric vehicles (EVs). Accurate estimation of the micro-parameters, state of charge (SOC), and state of health (SOH) are a few primary monitoring functions of the battery management system (BMS) to increase the battery's efficiency and safety under various operating conditions. This paper proposes a SOC and SOH co-estimation method by adopting an ensemble empirical mode decomposition method with adaptive noise and an autoencoder (EEMDA) to extract, decompose, and reconstruct the full-scale charging voltage and current data for a dual extended Kalman filter (DEKF) with multi-parameter and time-scale updates for accurate estimation based on a variable forgetting factor limited memory recursive least squares (VFF-LMRLS) method. The VFF-LMRLS method is used to solve the data saturation phenomenon and identify the battery's characteristic micro-parameters based on a proposed dynamic migration second-order resistor-capacitor equivalent circuit model under different operating states. Battery tests are conducted at temperatures ranging from −10 to 50 °C under complex working conditions. Using the VFF-LMRLS method, the effects of different temperatures on the micro-parameters are discussed. The SOC and SOH results of the proposed EEMDA-DEKF method based on the dynamic migration battery model show that the mean absolute error and root mean square error metrics have the least values of 0.0233% and 0.0252%, which signify an optimal performance improvement of 93.26% and 93.66%, respectively, compared to the conventional DEKF method. Based on the experimental results and analyses, the proposed method has a high degree of accuracy and robustness, which makes it feasible for battery monitoring and prognostic BMS applications.

Citation

TAKYI-ANINAKWA, P., WANG, S., ZHANG, H., XIAO, Y. and FERNANDEZ, C. 2023. Enhanced multi-state estimation methods for lithium-ion batteries considering temperature uncertainties. Journal of energy storage [online], 66, article number 107495. Available from: https://doi.org/10.1016/j.est.2023.107495

• Journal Article Type: Article
• Acceptance Date: Apr 20, 2023
• Online Publication Date: May 1, 2023
• Publication Date: Aug 30, 2023
• Deposit Date: May 15, 2023
• Publicly Available Date: May 2, 2024
• Journal: Journal of energy storage
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 66
• Article Number: 107495
• DOI: https://doi.org/10.1016/j.est.2023.107495
• Keywords: State of charge; State of health; Dynamic migration second-order resistor-capacitor equivalent circuit model; Variable forgetting factor limited memory recursive least squares method; Ensemble empirical mode decomposition autoencoding method
• Public URL: https://rgu-repository.worktribe.com/output/1952939