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Abstract To extend the operation window of batteries, active cell balancing has been studied in the literature. However, such an advancement presents significant computational challenges on real-time optimal control, especially when the number of cells in a battery increases. This article investigates the use of reinforcement learning (RL) and model predictive control (MPC) to effectively balance battery cells while at the same time keeping the computational load at a minimum. Specifically, event-triggered MPC is introduced as a way to reduce real-time computation. Different from the existing literature where rule-based or threshold-based event-trigger policies are used to determine the event instances, deep RL is explored to learn and optimize the event-trigger policy. Simulation results demonstrate that the proposed framework can keep the cell state-of-charge variation under 1% while using less than 1% computational resources compared to conventional MPC. 

Optimal control techniques such as model predictive control (MPC) have been widely studied and successfully applied across a diverse field of applications. However, the large computational requirements for these methods result in a significant challenge for embedded applications. While event-triggered MPC (eMPC) is one solution that could address this issue by taking advantage of the prediction horizon, one obstacle that arises with this approach is that the event-trigger policy is complex to design to fulfill both throughput and control performance requirements. To address this challenge, this paper proposes to design the event trigger by training a deep Q-network reinforcement learning agent (RLeMPC) to learn the optimal event-trigger policy. This control technique was applied to an active-cell-balancing controller for the range extension of an electric vehicle battery. Simulation results with MPC, eMPC, and RLeMPC control policies are presented along with a discussion of the challenges of implementing RLeMPC.