Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles
Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles
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DOI：
10.1016/j.apenergy.2019.113617
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发表时间：
2019-11
期刊：
影响因子：
11.2
通讯作者：
Ji Li;Quan Zhou;Yinglong He;B. Shuai;Ziyang Li;Huw Williams;Hongming Xu
中科院分区：
文献类型：
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作者：
Ji Li;Quan Zhou;Yinglong He;B. Shuai;Ziyang Li;Huw Williams;Hongming Xu
本文研究了插电式混联混合动力汽车（phev）的在线预测控制策略，提出了一种新的在线优化方法——双环在线智能规划（DOIP），用于速度预测和能量流控制。该方法通过重新考虑每步预瞄时驾驶行为的变化，保证了最优控制序列在在线预测能源管理节能效率中的有效性。设计过程从使用系统控制模型和成本函数定义的串并联插电式混合动力汽车的仿真开始。受模糊粒化技术的启发，建立了一种深度模糊预测器来实现面向驾驶员的速度预测，并利用有限状态马尔可夫链来学习车速和加速度之间的过渡概率。为了确定最优控制行为和两个能量源之间的功率分配，对DOIP算法进行了混沌增强加速群优化。在基于wltp的驾驶循环中，通过与现有的两种预测器进行比较，评价了深度模糊预测器的预测能力。将该控制策略与基于短视和动态规划的控制策略进行了对比，并通过环内驱动测试进行了验证。结果表明，与基于马尔可夫链的预测器相比，深度模糊预测器可以有效地识别驾驶行为，并减少至少19%的误差。采用DOIP算法的在线预测控制策略，油耗较基线显著降低9.37%，计算时间缩短。
This paper investigates an online predictive control strategy for series-parallel plug-in hybrid electric vehicles (PHEVs), resulting in a novel online optimization methodology named the dual-loop online intelligent programming (DOIP) that is proposed for velocity prediction and energy-flow control. By reconsidering the change of driving behaviours at each look-ahead step, this methodology guarantees the effectiveness of optimal control sequence in the energy-saving efficiency of online predictive energy management. The design procedure starts with the simulation of a series-parallel PHEV using a systematic control-oriented model and the definition of a cost function. Inspired by fuzzy granulation technology, a deep fuzzy predictor is created to achieve driver-oriented velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities between vehicle speed and acceleration. To determine the optimal control behaviours and power distribution between two energy sources, chaos-enhanced accelerated swarm optimization is developed for the DOIP algorithm. The prediction capability of the deep fuzzy predictor is evaluated by comparing with two existing predictors over the WLTP-based driving cycle. The proposed control strategy is contrasted with short-sighted and dynamic programming based counterparts, and validated by a driver-in-the-loop test. The results demonstrate that the deep fuzzy predictor can effectively recognize driving behaviour and reduce at least 19% errors compared to involved Markov chain based predictors. Online predictive control strategy using the DOIP algorithm is able to significantly reduce 9.37% fuel consumption from the baseline and shorten computational time.