A Data Selection Strategy for Real-time Estimation of Battery Parameters

Real-time estimation of battery internal states and parameters is a key task of battery management and has been studied extensively in literature. Estimation is usually performed by an algorithm using a model and the measured input-output data, such as current, voltage, and temperature. Traditionally, the estimation algorithms do not differentiate the data points and use all of them equally for estimating each variable. However, not all data points, but often only a small fraction of them, are sensitive to the target variables under estimation. Using insensitive data will induce significant estimation errors due to the commonly presented unknown disturbances such as measurement noise and model uncertainty. This paper studies the data selection mechanism to optimize and guarantee the accuracy of battery state and parameter estimation. A sensitivity-based data selection strategy is proposed, which automatically identifies the sensitive data in real-time and passes them to the observer for estimation. It is shown that the data selection strategy could improve the quality of estimation results significantly.