Research on Collaborative Estimation of SOC and SOH for Lithium‐Ion Batteries Based on BS‐SRCKF‐DEKF
Year: 2025
Type: article
Abstract: ABSTRACT The safe and reliable operation of lithium‐ion battery energy storage systems depends critically on effective monitoring by the battery management system (BMS). Accurate estimation of the state of charge (SOC) and state of health (SOH) is therefore essential. This paper proposes a new SOC and SOH cooperative estimation approach. First, a second‐order RC equivalent‐circuit model with hysteresis is established, and its parameters are identified using a bias‐compensated adaptive‐forgetting recursive least squares algorithm (BC‐AFFRLS). SOH is characterized by the maximum usable capacity and the ohmic resistance. Building on this model, we develop a multi‐time‐scale SOC–SOH cooperative estimator based on a backward‐smoothed square‐root cubature Kalman filter (BS‐SRCKF) and a dual extended Kalman filter (DEKF). To further improve accuracy, the initial covariance matrices of BS‐SRCKF‐DEKF are tuned via swarm‐intelligence optimization. Simulation results under intermittent constant‐current charge/discharge profiles validate the effectiveness of the proposed method. Moreover, tests under US06 and FUDS driving cycles show that the proposed algorithm outperforms four comparative methods; notably, the cumulative SOC estimation errors are only 1.18% and 1.71%, respectively. Additional comparisons across FUDS, US06, and DST at different temperatures against data‐driven baselines as well as BS‐SRCKF and DEKF further confirm consistently excellent SOC estimation. Overall, the BS‐SRCKF‐DEKF framework demonstrates strong robustness and superior performance.
Source: Energy Science & Engineering
DOI: 10.1002/ese3.70428
Authors: Meijin Lin, Haokun Lin, Jiehua Tan
Institution: Foshan University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,780
OpenAlex ID: W7117540997