2018/10/21 by Agus Hasan, Hasan, Agus, Martin Skriver +3
Engineering · Mathematics · #Advanced Battery Technologies Research #Advancements in Battery Materials #Battery (electricity) #Cascade #Computer science #Control theory (sociology) #Convergence (economics) #Electric Vehicles and Infrastructure #Electric and Hybrid Vehicle Technologies #Electrical engineering #Engineering #Estimator #Extended Kalman filter #FOS: Mathematics #Kalman filter #Lithium-ion battery #Mathematics #Nonlinear system #Observer (physics) #Optimization and Control (math.OC) #Physics #Power (physics) #Rate of convergence #Schedule #State of charge #Statistics #Voltage #math.OC
paper · pdf · doi:10.48550/arxiv.1810.09014
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/10/21 · openalex publication_date 2018/10/21 · arxiv updated 2018/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
This paper presents a novel framework for state-of-charge estimation of rechargeable batteries in electric vehicles using a two-stage nonlinear estimator called the eXogenous Kalman filter (XKF). The nonlinear estimator consists of a cascade of nonlinear observer (NLO) and linearized Kalman filter (LKF). The NLO is used to produce a globally convergent auxiliary state estimate that is used to generate a linearized model in the time-varying Kalman filter algorithm. To demonstrate the proposed approach, we present a model of a lithium-ion battery from an equivalent circuit model (ECM). The model has linear process equations and a nonlinear output voltage equation. The method is tested using experimental data of a lithium iron phosphate (LiFePO4) battery under dynamic stress test (DST) and federal urban driving schedule (FUDS). Effect on different ambient temperatures is also discussed. Compared with EKF and UKF, our proposed XKF achieve faster convergence rate, which can be attributed to the use of the NLO.