Battery Parameter Identification Using Recursive Least Squares with Variable Directional Forgetting

An accurate battery model is important to perform various tasks of battery management. Battery model parameters change with working conditions, thus requiring continuous update for parameter estimation. The recursive least-squares algorithm with a forgetting strategy is widely used to estimate time-varying parameters online. However, the online data vary with battery operations, so the data may fail to contain sufficient excitation. When the online data is not persistently exciting, most existing forgetting strategies in battery literature suffer from covariance blowup, which means the algorithm cannot identify parameters reliably. In order to cope with non-persistent excitation, a recursive least-squares algorithm with variable direction forgetting is presented in this paper for battery parameter estimation. Instead of discounting all old information, it performs forgetting only in the direction of sufficient excitation. The forgetting factor can adaptively adjust based on excitation, which enhances trade-off between stability and tracking capability of the algorithm. A battery simulation model with time-varying parameters is used to illustrate the effectiveness of our proposed algorithm.