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A High-Efficiency Bidirectional Active Balance for Electric Vehicle Battery Packs Based on Model Predictive Control

机译:基于模型预测控制的电动汽车电池组高效双向活动平衡

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摘要

This study designs an active equilibrium control strategy based on model predictive control (MPC) for series battery packs. To shorten equalisation time and reduce unnecessary energy consumption, bidirectional active equalisation is modelled and analysed, and the model predictive control algorithm is then applied to the established state space equation. The optimisation problem that minimises the equilibrium time is transformed to a linear programming form in each cycle. By solving the linear programming problem online, a group of control optimal solutions is found and the series equalisation problem is decoupled. The equalisation time is shortened by dynamically adjusting the equalisation current. Simulation results show that the MPC algorithm can avoid unnecessary energy transfer and shorten equalisation time. The bench experimental result shows that the equilibrium time is reduced by 31%, verifying the rationality of the MPC strategy.
机译:本研究基于模型预测控制(MPC)的串联电池组设计了主动均衡控制策略。为了缩短均衡时间并降低不必要的能量消耗,建模和分析双向激活均衡,然后将模型预测控制算法应用于建立的状态空间方程。最小化平衡时间的优化问题在每个周期中转换为线性编程形式。通过在线解决线性编程问题,发现了一组控制最佳解决方案,并分离了串联均衡问题。通过动态调整均衡电流来缩短均衡时间。仿真结果表明,MPC算法可以避免不必要的能量传递和缩短均衡时间。台面实验结果表明,均衡时间减少了31%,验证了MPC策略的合理性。

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