Evaluation of electrochemical models based battery state-of-charge estimation approaches for electric vehicles. (1st December 2017)

• Record Type: Journal Article
• Authors: Lin, Cheng; Tang, Aihua; Xing, Jilei
• Is Part Of: Applied energy. Volume 207(2017)
• Page Start: 394
• Page End: 404
• Publication Date: 2017-12-01
• Subjects: Electric vehicles (EVs) -- Electrochemical model -- Extended Kalman filter (EKF) -- SoC estimation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.05.109
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Two order-reduced electrochemical models of lithium-ion batteries are derived. The reduced models are verified and evaluated experimentally. A SPM-based SoC estimation approach combined with EKF is proposed. The performances of five model-based approaches are compared under a UDDS test. Abstract: Real-time and accurate state-of-charge (SoC) estimation of lithium-ion batteries is a critical issue for efficient monitoring, control and utilization of advanced battery management systems (BMS) in electric vehicles (EVs). The electrochemical mechanism model can accurately describe the spatially distributed behavior of the internal states of the battery, but the model is complex and computationally huge, which is difficult to simulation in vehicle BMS. To solve these problems, it is necessary to simplify the battery mechanism model and study the model-based SoC estimation approaches. In this paper, two order-reduced models including an average-electrode model (AEM) and a single particle model (SPM) are first proposed. Additionally, the reduced-models combined with algorithms, including an extended Kalman filter (EKF), a sliding-mode observer (SMO) with a uniform reaching law (URL) and an SMO with an exponential reaching law (ERL), are employed to design battery SoC observers. To achieve an optimal trade-off between the tracking accuracy and convergence ability, the performances of these approaches are compared under an Urban Dynamometer Driving Schedule (UDDS) test. The comparison results indicate that the SPM-EKF approach can obtain a reliable battery voltage response and a more accurate SoC estimation than other approaches.