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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. 

ABSTRACT Model predictive control (MPC) is advantageous for autonomous vehicle path tracking but suffers from high computational complexity for real‐time implementation. Event‐triggered MPC aims to reduce this burden by optimizing the control inputs only when needed instead of every time step. Existing works in literature have been focused on algorithmic development and simulation validation for very specific scenarios. Therefore, event‐triggered MPC in real‐world full‐size vehicle has not been thoroughly investigated. This work develops event‐triggered MPC with switching model for autonomous vehicle lateral motion control, and implements it on a production vehicle for real‐world validation. Experiments are conducted under both closed road and open road environments, with both low speed and high speed maneuvers, as well as stop‐and‐go scenarios. The efficacy of the proposed event‐triggered MPC, in terms of computational load saving without sacrificing control performance, is clearly demonstrated. It is also demonstrated that event‐triggered MPC can sometimes improve the control performance, even with less number of optimizations, thus contradicting to existing conclusions drawn from simulation. 

This paper presents a practical experiment for estimating the state-of-charge (SOC) of individual cells in a series-connected heterogeneous lithium-ion battery pack, where only the terminal voltage of the battery pack is measured. To deal with real-time computation constraints, the dense extended Kalman filter (DEKF) algorithm has been proposed in the literature, which has a significantly lower computational complexity compared to the regular extended Kalman filter for this specific estimation problem. This work supplements the existing work by conducting a real-world experiment to validate the performance of the DEKF. Specifically, experiments involving a battery pack of three cells connected in series were conducted, where the battery pack was discharged under a constant current load. A genetic algorithm was applied to identify missing model parameters, as well as tuning the DEKF for optimal convergence and accurate SOC estimation. Our experimental results confirm that the proposed DEKF accurately estimates the SOC of each cell regardless of the hardware limitations and uncertainty, making it suitable for low-cost, real-time battery management systems. In particular, the SOC estimation error can be kept well under 1% even if the initial estimate is far from the true SOC. 

This paper presents and compares two model predictive control (MPC) approaches for battery cell state-of-charge (SOC) balancing. In both approaches, a linearized discrete-time model that takes into account individual cell capacities is used. The first approach is a linear MPC controller that effectively regulates multiple cells to track a target SOC level while satisfying physical constraints. The second approach is based on explicit MPC implementation to reduce online computation while achieving a comparable performance. The simulation results suggest that explicit MPC can deliver the same balancing performance as linear MPC, while achieving faster online execution. Specifically, explicit MPC reduces the computation time by 37.3% in a five-cell battery example, with the cost of higher offline computation. However, simulation results also reveal a significant limitation for explicit MPC for battery systems with a larger number of cells. As the number of cells increases and/or the prediction horizon increases, the computational requirements grow exponentially, making its application to SOC balancing for large battery systems impractical. To the best of the authors’ knowledge, this is the first study that compares MPC and explicit MPC algorithms in the context of battery cell balancing.