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LiFePO4 batteries are increasingly utilized in electric vehicles due to their superior safety. Accurate state estimation is the basis for the safe and reliable application of LiFePO4 batteries. However, the flat voltage characteristics of LiFePO4 batteries lead to state estimation closed-loop correction as its inherent contradiction. To address this challenge, a model-based SOC estimation method combining proportional-integral-differential (PID) observer and improved extended Kalman filter (EKF) is developed according to different open-circuit-voltage (OCV) ranges, specific processes include: First, an exponentially weighted moving average algorithm with a temperature compensation factor is presented to compensate for the errors in the identified OCV. Secondly, the combination of the PID observer and EKF is chosen adaptively to update SOC within distinct OCV ranges, differentiated by the identified OCV. To achieve optimization of the PID parameters and temperature compensation factors across varying temperatures, an enhanced whale optimization algorithm is developed. To validate the developed method, a series of experiments are performed across a range of temperatures and with multiple driving profiles. The results show that the developed method not only guarantees maximum absolute error of <3 %, but also can converge quickly in the early stage. • A temperature compensation factor (T c) is used to reduce the error of identified OCV based on EWMA-FFRLS. • A PIDO combined with an improved AEKF is presented to accelerate the SOC convergence speed. • An AEKF-PIDO-integrated US-EKF framework is developed to enhance SOC estimation of LiFePO4 battery. • An improved WOA is introduced to adjust the PIDO parameters and T c at different temperatures. [ABSTRACT FROM AUTHOR]

This paper presents an effective model-based sensor fault detection and isolation (FDI) scheme for a series battery pack with low computational effort. The large number of current and voltage sensors in the battery pack, make it of high computational complexity. The major purpose of sensor FDI is to guarantee the healthy operations of the battery management system (BMS), and thus to prevent the battery from over-charge and over-discharge. In the voltage sensors fault scenarios, the most possibly being over-charged and over-discharged cells are these two cells with the maximum and minimum voltage respectively. Within the proposed scheme, these two cells are monitored in real time to diagnose the pack current sensor fault, or a voltage sensor fault of these two cells, while the rest cells are monitored offline with a long time interval, guaranteeing other voltage sensors working normally. For the scheme implementation, adaptive extended Kalman filter (AEKF) is used to estimate the battery states of each individual cell, and the estimated output voltage is compared with the measured voltage to generate a residual. Then the residuals are evaluated by a statistical inference method that determines the presence of the fault. Finally, the effectiveness of the proposed sensor FDI scheme is experimentally validated with a series battery pack under the UDDS driving cycles. [ABSTRACT FROM AUTHOR]

Highlights: [•] A lumped parameter battery model against different battery aging levels is proposed. [•] The RLS based method is used to identify the parameter of battery model in real-time. [•] A data-driven based adaptive SoC estimator is developed by RLS and AEKF algorithm. [•] The robustness of the SoC estimator against varying loading profiles is evaluated. [•] The robustness of the SoC estimator against different aging levels is evaluated. [ABSTRACT FROM AUTHOR]

Abstract: The accurate estimation of internal parameters and state-of-charge (SoC) of battery, which greatly depends on proper models and corresponding high-efficiency, high-accuracy algorithms, is one of the critical issues for the battery management system. A model-based online estimation method of a LiFePO4 battery is presented for application in electric vehicles (EVs) by using an adaptive extended Kalman filter (AEKF) algorithm. The Thevenin equivalent circuit model is selected to model the LiFePO4 battery and its mathematics equations are deduced to some extent. Additionally, an implementation of the AEKF algorithm is elaborated and employed for the online parameters’ estimation of the LiFePO4 battery model. To illustrate advantages of the online parameters’ estimation, a comparison analysis is performed on the terminal voltages between the online estimation and the offline calculation under the Hybrid pulse power characteristic (HPPC) test and the Urban Dynamometer Driving Schedule (UDDS) test. Furthermore, an efficient online SoC estimation approach based on the online estimation result of open-circuit voltage (OCV) is proposed. The experimental results show that the online SoC estimation based on OCV–SoC can efficiently limit the error below 0.041. [Copyright &y& Elsevier]