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Pontryagin's Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus

机译：基于庞特里亚金最小原理的插电式混合动力客车能量管理模型预测控制

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摘要

To improve computational efficiency of energy management strategies for plug-in hybrid electric vehicles (PHEVs), this paper proposes a stochastic model predictive controller (MPC) based on Pontryagin's Minimum Principle (PMP), which differs from widely used dynamic programming (DP)-based predictive methods. First, short-time speed forecasting is achieved using a Markov chain model, based on real-world driving cycles. The PMP- and DP-based MPCs are compared under four preview horizons (5 s, 10 s, 15 s and 20 s), and the results show that the computational time of the DP-MPC is almost four times of that in the PMP-MPC. Moreover, the influence of predication horizon length on computational time and energy consumption is examined. Given a preview horizon of 5 s, the PMP-MPC holds a total energy consumption cost of 7.80 USD and computational time per second of 0.0130 s. When the preview horizon increases to 20 s, the total cost is 7.77 USD with the computational time per second increasing to 0.0502 s. Finally, DP, PMP, and rule -based strategies are contrasted to the PMP-MPC method, further demonstrating the promising performance and computational efficiency of the proposed methodology.
机译：为了提高插电式混合动力汽车（PHEV）的能源管理策略的计算效率，本文提出了一种基于庞特里亚金最小原理（PMP）的随机模型预测控制器（MPC），该模型不同于广泛使用的动态规划（DP）-基于预测的方法。首先，基于实际驾驶周期，使用马尔可夫链模型实现了短时速度预测。在四个预览阶段（5 s，10 s，15 s和20 s）下比较了基于PMP和DP的MPC，结果表明DP-MPC的计算时间几乎是PMP的四倍。 -MPC。此外，研究了预测水平长度对计算时间和能耗的影响。假设预览时间为5 s，则PMP-MPC的总能耗成本为7.80 USD，每秒计算时间为0.0130 s。当预览范围增加到20 s时，总成本为7.77 USD，而每秒的计算时间增加到0.0502 s。最后，将DP，PMP和基于规则的策略与PMP-MPC方法进行了对比，进一步证明了所提出方法的有希望的性能和计算效率。

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