Active Battery Cell Balancing by Real-Time Model Predictive Control for Extending Electric Vehicle Driving Range

Electrical vehicles (EV) have been considered to be an effective way to combat global climate change. To extend the driving range of EV, this paper studies the active battery cell balancing control based on linear parametric varying model predictive control (MPC). Specifically, an equivalent circuit model is used to predict cell terminal voltage, and three different MPC-based battery cell balancing control strategies are proposed to dynamically transport electricity from cell to cell to reduce the imbalance. In particular, for the first control strategy, MPC is set up to be a tracking controller with the primary control objective of forcing all cells' terminal voltage to follow the same trajectory generated by a nominal cell model; for the second control strategy, MPC maximizes the lowest cell voltage, so that the battery operating range can be extended; for the third and last strategy, MPC minimizes the maximum variation among cell terminal voltages. To assess the effectiveness of the proposed battery cell balancing control strategies, simulations are performed on all three MPC formulations, using both steady-state and transient conditions. Numerical results show that the proposed battery cell balancing control can achieve a driving range extension of 9% for dynamic driving cycle and 7% for steady-state condition, based on our simulation setup. Compared to the existing work, our approaches do not require the over-restrictive assumption that the trip duration is known in advance, while at the same time achieve similar driving range extension. Furthermore, it is also shown that different driving condition favors different cell balancing control strategy, indicating a need for a hybrid approach. Finally, real time implementability is demonstrated via throughput analysis. Note to Practitioners-Improving the efficiency of electric vehicles is of paramount importance to combat the global climate challenge. This paper contributes by proposing effective cell level balancing control methodologies to extend the driving range of electric vehicles to improve their energy efficiency and public acceptance. The control methods, which are based on model predictive control, are analytically derived with details for embedded implementation. Simulation results demonstrate the effectiveness of the proposed methodologies, with future work to investigate the applicability of nonlinear model predictive control with large number of cells.