Active Cell Balancing by Model Predictive Control for Real Time Range Extension
Jun Chen, Aman Behal, Chong Li
Abstract
This paper studies the active cell balancing problem by using model predictive control (MPC) for real time range extension. Specifically, three MPC formulations are proposed and compared: the first one being a tracking controller to force all cells to follow the same trajectory generated by a nominal cell model, the second one trying to maximize the lowest cell SOC/voltage and the last one minimizing the difference between the highest and lowest cell SOC/voltages. Both steady state and transient conditions are simulated to assess the effectiveness of the proposed controllers, and a range extension of 4% is found for dynamic driving cycle and 7% for steady state condition. Comparing to the literature, our approaches achieve similar range extension, without making the restrictive assumption that the final battery state-of-charge is known in advance, making our approaches more applicable. Real time implementability is demonstrated via throughput analysis.