LAUSR

Abstract Vehicle to grid technology is no longer a fiction but rather a reality. Due to recent technological advances in bidirectional power transfer, EVs could serve not just as transportation tools but also as electric storage units for the grid. As a result, battery management is now more crucial than ever to control the energy transfer to and from the battery pack, while keeping all battery parameters within a safe and optimal region. In order to do so, accurate knowledge of SOC is of significant importance, since it reflects the inner state of the battery. This paper proposes, a multi-innovation theory to enhance the estimation accuracy of the unscented Kalman filter. By expanding a single innovation voltage value to multi-innovations consisting of the previous and current values of the battery's output, the accuracy of the UKF is drastically improved. All the design aspects of the proposed MI-UKF are detailed. Moreover, a hybrid Levenberg Marquardt approach is implemented for battery internal parameter identification. Finally, the experimental results indicate that the proposed MI-UKF is robust against unpredicted operational conditions, and it can enhance the accuracy of the UKF with more than 1%.