Application-Oriented Stochastic Energy Management for Plug-in Hybrid Electric Bus With AMT
Application-Oriented Stochastic Energy Management for Plug-in Hybrid Electric Bus With AMT
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DOI：
10.1109/tvt.2015.2496975
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发表时间：
2016-06
影响因子：
6.8
通讯作者：
Liang Li;Bingjie Yan;Chao Yang;Yuanbo Zhang;Zheng Chen;Guirong Jiang
中科院分区：
文献类型：
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作者：
Liang Li;Bingjie Yan;Chao Yang;Yuanbo Zhang;Zheng Chen;Guirong Jiang
考虑到公交车程序复杂但有规律的特点，随机动态规划（SDP）可能是一种更有潜力优化插电式混合动力电动公交车（PHEB）能量管理的方法。然而，离散传动系统和连续动力系统使其成为复杂的多维优化问题，特别是对于带有自动机械变速箱（AMT）的PHEB，基于历史数据获得的最优决策可能并不总是能很好地满足驾驶员在各种驾驶条件下对车辆操纵性的期望。为了解决这些问题，本文提出了一种基于SDP的自适应方法。彻底地将SDP推进到变速箱的输入中，仅在特殊的换档逻辑下优化扭矩分配，从而减少了优化的维度并获得更适用的优化序列。然后，针对复杂客车驾驶循环的变化，开发了一种自适应因子，通过动态调整换档点和扭矩分配来实时权衡车辆燃油经济性和驾驶性能。仿真结果表明，所提出的方法可以很好地响应驾驶条件（例如道路坡度和车辆负载）的变化。此外，通过与不同控制策略的比较，详细讨论了所提出方法的性能。更重要的是，所提出的方法在实践中具有巨大的应用潜力。
Taking the complex but regular characteristics of bus routines into account, the stochastic dynamic programming (SDP) might be a method with more potential to optimize the energy management of a plug-in hybrid electric bus (PHEB). However, the discrete transmission system and the continuous power system make it a complicated multidimensional optimal problem, particularly for PHEB with automated mechanical transmission (AMT), and the optimal decisions, which are obtained based on historical data, might not always well satisfy the driver's expectation to vehicle maneuverability under various driving conditions. To solve these problems, an adaptive approach based on the SDP is proposed in this paper. Exhaustively, the SDP is propelled into the input of the transmission to only optimize the torque split under the special gearshift logic, which reduces the dimensions of optimization and obtains more applicable optimal sequences. Then, an adaptive factor, which trades off the vehicle fuel economy and drivability in real time by dynamically adjusting the gearshift points and the torque split, is developed for the variation of the complicated bus driving cycles. The simulation results demonstrate that the proposed method could well respond to the variations of the driving conditions (e.g., road grade and vehicle load). Furthermore, the performance of the proposed method is discussed in detail by comparisons with different control strategies. More importantly, the proposed approach has great potential to be applied in practice.