A novel approach of remaining discharge energy prediction for large format lithium-ion battery pack

Accurate estimation of battery pack remaining discharge energy is a crucial challenge to the battery energy storage systems. In this paper, a new method of battery pack remaining discharge energy estimation is proposed using the recursive least square-unscented Kalman filter. To predict the remaining discharge energy precisely, the inconsistency of the battery pack caused by different working temperatures is taken into consideration and the degree of battery inconsistency is quantified based on mathematical methods of statistics. In addition, the recursive least square is applied to identify the parameters of the battery pack model on-line and the unscented Kalman filter is employed in battery pack remaining discharge energy and energy utilization ratio estimation. The experimental results in terms of battery states estimation under the new European driving cycle and real driven profiles, with the root mean square error less than 0.01, further verify that the proposed method can estimate the battery pack remaining discharge energy with high accuracy. What's more, the relationship between the pack energy utilization ratio and the degree of battery inconsistency is summarized in the paper.