Title: SOC estimation of lithium-ion batteries for new energy vehicles based on improved AEKF

Authors: Pinjia Shi; Qiongqiong Liu; Jie Wen

Addresses: College of Automobile Engineering, Jiangsu Vocational College of Electronics and Information, Huai'an, 223003, China ' College of Automobile Engineering, Jiangsu Vocational College of Electronics and Information, Huai'an, 223003, China ' College of Automobile Engineering, Jiangsu Vocational College of Electronics and Information, Huai'an, 223003, China

Abstract: In order to address the issue of reduced safety of lithium-ion batteries due to their high energy density and long cycle life, a novel lithium-ion battery state estimation method based on an improved adaptive extended Kalman filter (AEKF) algorithm is proposed. The new method improves AEKF through a triple mechanism, using dual adaptive extension to optimise parameter tracking, differential adaptive noise suppression, and multi-scale updates to enhance temporal accuracy. Experimental results show that the proposed method reduces noise bias by 3.3%, 1.9%, and 5.9%, and lowers deviation in cycling tests by 0.7%, 3.7%, and 5.2%, respectively, compared to traditional approaches. Optimal performance is achieved at a time scale of 10 and temperature of 25°C. The method demonstrates superior accuracy and robustness in estimating battery states, making it valuable for improving the reliability and efficiency of lithium-ion batteries in new energy vehicles.

Keywords: adaptive extended Kalman filter; AEKF; new energy vehicles; lithium-ion battery; SOC estimation; SOCE; parameter identification.

DOI: 10.1504/IJPT.2026.154602

International Journal of Powertrains, 2026 Vol.15 No.2, pp.121 - 137

Received: 22 Apr 2025
Accepted: 21 Nov 2025
Published online: 07 Jul 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article