Supercapacitor is being widely used in electric transportation applications, particularly in electric vehicles (EVs). In the EV, the supercapacitor mainly serves as energy storage system for accelerating the vehicle and/or capturing the braking energy due to its prominent features such as high power density and long life cycle. However, during the vehicle braking and/or accelerating, deep and fast supercapacitor charging-discharging cycles increase the electric and thermal stresses on the supercapacitor bank. Under such circumstances, condition monitoring of the supercapacitor bank is essential in EVs for safe operation of the storage system. In this paper, an effective method for condition monitoring and fault diagnosis of the supercapacitor banks is proposed. First, it is shown that the equivalent series resistance (ESR) and double-layer capacitance (CDL) can be used as the key signatures for indicating the state-of-health. Subsequently, the recursive extended least-square algorithm (RELS) is used for online estimation of the ESR and capacitance of the supercapacitor bank. Based on a residual signal which is defined as the difference between the voltages of the supercapacitor bank and RELS algorithm, the abnormal conditions of the supercapacitor bank are distinguished. Experimental results are presented to confirm the feasibility and precision of the proposed method.

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Online condition monitoring and fault detection of large supercapacitor banks in electric vehicle applications

References

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