An observer-based fault diagnosis in battery systems of hybrid vehicles

Hybrid electric vehicles (HEVs) currently use Nickel-Metal Hydride (Ni-MH) batteries which have advantages of design flexibility, superior power, environmental acceptability and recyclability, long life, wide-range operating temperature and low cost. No matter how good a battery is, a failure can always occur in a battery leading to serious inconvenience, performance deterioration and costly replacement. Thus, it is desirable to be able to detect the underlying degradation and to predict level of unsatisfactory performance. By using current, voltage and temperature measurements of Ni-MH batteries, they can be modeled so that the internal dynamics of the batteries can be estimated and state of health of the batteries can be predicted for secure and long-life operations. An observer-based fault diagnosis approach is designed to analyze the state of health of the Ni-MH battery system of HEVs in this study. Real-world input data is used to assess the efficiency of the approach in the existence of uncertainties. The possible sensor faults and unexpected parameter deviations are diagnosed efficiently with statistical evaluation of the generated residuals.