Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications

• Published in: PloS one 2023-11, Vol.18 (11), p.e0293753-e0293753
• Main Authors: El Marghichi, Mouncef, Dangoury, Soufiane, zahrou, Younes, Loulijat, Azeddine, Chojaa, Hamid, Banakhr, Fahd A, Mosaad, Mohamed I
• Format: Article
• Language: English
• Subjects: Accuracy; Aging; Algorithms; Automobiles, Electric; Biology and Life Sciences; Case studies; Computer and Information Sciences; Datasets; Electric vehicles; Engineering and Technology; Estimates; Evaluation; Hybrid electric vehicles; Hybrid vehicles; Kalman filters; Least squares; Lithium; Lithium batteries; Lithium cells; Lithium-ion batteries; Mathematical optimization; Methods; Neural networks; Optimization; Particle swarm optimization; Performance prediction; Physical Sciences; Properties; Rechargeable batteries; Regression analysis; Research and Analysis Methods; Root-mean-square errors; State of charge; Support vector machines
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0293753

Significant improvements in battery performance, cost reduction, and energy density have been made since the advancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). The safety and effectiveness of EVs depend on accurate measurement and prediction of the state of health (SOH) of lithium-ion batteries; however, this process is uncertain. In this study, our primary goal is to enhance the accuracy of SOH estimation by reducing uncertainties in state of charge (SOC) estimation and measurements. To achieve this, we propose a novel method that utilizes the gradient-based optimizer (GBO) to evaluate the SOH of lithium batteries. The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. We evaluated our method against four robust algorithms, namely particle swarm optimization-least square support vector regression (PSO-LSSV), BCRLS-multiple weighted dual extended Kalman filtering (BCRLS-MWDEKF), Total least square (TLS), and approximate weighted total least squares (AWTLS) in hybrid electric vehicle (HEV) and electric vehicle (EV) applications. Our method consistently outperformed the alternatives, with the GBO achieving the lowest maximum error. In EV scenarios, GBO exhibited maximum errors ranging from 0.65% to 1.57% and mean errors ranging from 0.21% to 0.57%. Similarly, in HEV scenarios, GBO demonstrated maximum errors ranging from 0.81% to 3.21% and mean errors ranging from 0.39% to 1.03%. Furthermore, our method showcased superior predictive performance, with low values for mean squared error (MSE) (