A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles

TLDR: A simple and effective model-based sensor fault diagnosis scheme is developed to detect and isolate the fault of a current or voltage sensor for a series-connected lithium-ion battery pack and the experimental and simulation results validate the effectiveness of the proposed sensor fault diagnosed scheme.

Abstract: In electric vehicles, a battery management system highly relies on the measured current, voltage, and temperature to accurately estimate state of charge (SOC) and state of health. Thus, the normal operation of current, voltage, and temperature sensors is of great importance to protect batteries from running outside their safe operating area. In this paper, a simple and effective model-based sensor fault diagnosis scheme is developed to detect and isolate the fault of a current or voltage sensor for a series-connected lithium-ion battery pack. The difference between the true SOC and estimated SOC of each cell in the pack is defined as a residual to determine the occurrence of the fault. The true SOC is calculated by the coulomb counting method and the estimated SOC is obtained by the recursive least squares and unscented Kalman filter joint estimation method. In addition, the difference between the capacity used in SOC estimation and the estimated capacity based on the ratio of the accumulated charge to the SOC difference at two nonadjacent sampling times can also be defined as a residual for fault diagnosis. The temperature sensor which is assumed to be fault-free is used to distinguish the fault of a current or voltage sensor from the fault of a battery cell. Then, the faulty current or voltage sensor can be isolated by comparing the residual and the predefined threshold of each cell in the pack. The experimental and simulation results validate the effectiveness of the proposed sensor fault diagnosis scheme.