Accurate estimation of the state of charge (SOC) of a lithium-ion battery in electric vehicles closely depends on the health status of the current and voltage acquisition sensors. In this paper, a simple and effective model-based sensor fault detection method for lithium-ion batteries is presented. In this method, the SOC of the battery is estimated in real time by the unscented Kalman filter (UKF), and the ampere-time integration method can calculate the cumulative discharge/charge amount for a certain period. Therefore, the ratio of the amount of SOC change to the amount of charge/discharge during a certain period is the estimated capacity. The difference between the capacity used for SOC estimation and the estimated capacity is defined as the residual used to detect whether a sensor failure has occurred. The proposed sensor fault detection method was verified by the dynamic stress test.


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    Title :

    Model-Based Sensor Fault Detection for Lithium-Ion Batteries in Electric Vehicles


    Contributors:
    Yu, Quanqing (author) / Xiong, Rui (author) / Lin, Cheng (author)


    Publication date :

    2019-04-01


    Size :

    380317 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English






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