Online condition monitoring of battery systems with a nonlinear estimator

The performance of batteries as uninterruptable power sources in any industry cannot be taken for granted. The failures in battery systems of safety-related electric systems can lead to performance deterioration, costly replacement, and, more importantly, serious hazards. The possible failures in battery systems are currently determined through periodic maintenance activities. However, it is desirable to be able to detect the underlying degradation and to predict the level of unsatisfactory performance by an online real-time monitoring system to prevent unexpected failures through early fault diagnosis. Such an online fault diagnosis method can also contribute to better maintenance and optimal battery replacement programs. A robust nonlinear estimator-based online condition monitoring method is proposed to determine the state of health of the battery systems online in industry. Real-world experimental data of a modern battery system are used to assess the efficiency of the proposed approach in the existence of parameter uncertainties.