Joint Estimation of State-of-Health and State-of-Charge for Lithium-Ion Battery Based on Electrochemical Model Optimized by Neural Network

• Published in: IEEE journal of emerging and selected topics in industrial electronics (Print) Vol. 4; no. 1; pp. 168 - 177
• Main Authors: Sun, Xiaodong, Chen, Qi, Zheng, Linfeng, Yang, Jufeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Back propagation; Back propagation networks; Batteries; Battery electrochemical model; Electric charge; Electric vehicles; Estimation; Integrated circuit modeling; Ions; Lithium; Lithium-ion batteries; lithium-ion battery; lithium-ion concentration; Mathematical models; Model accuracy; neural network; Neural networks; Optimization; Optimization algorithms; Parameter identification; Rechargeable batteries; Solid phases; State of charge; state-of-charge (SOC); state-of-health (SOH)
• ISSN: 2687-9735; 2687-9743
• DOI: 10.1109/JESTIE.2022.3148031

Accurate estimation of battery state including state-of-charge (SOC) and state-of-health (SOH) plays an important role in improving the performance and lifespan of battery packs in electric vehicles. From the perspective of electrochemical mechanisms, the battery SOC and SOH strongly depends on the status of battery electrochemical reaction processes and associated variables, especially the number of lithium-ion in solid phases. In this article, a method based on a simplified battery electrochemical model, which is optimized by back propagation neural network, was proposed to estimate the battery SOC and SOH. The simplified battery electrochemical model called single particle model is employed to obtain the number of recyclable lithium-ions at different aging degrees and the lithium-ion changes of solid particles with different loaded currents. The parameters of the electrochemical model are identified by an algorithm hybridizing Coyote Optimization Algorithm and Grey Wolf Optimizer, abbreviated to Hybrid Coyote Optimization Algorithm and Grey Wolf Optimizer for its great balance in operational speed and optimization accuracy. To improving the speed and accuracy of model parameter identification, the rang of the number of recyclable lithium-ions is narrowed by battery capacity, which is estimated by the back propagation neural network. The accuracy and robustness of the proposed method are systematically evaluated, and with the mean absolute percentage errors of promising battery capacity and SOC prediction results are less than 2% and 1.8%, respectively. The operation time of the battery SOC prediction during federal urban driving schedule cycles are less than 2 s.