A multi-objective optimization energy management strategy for power split HEV based on velocity prediction
A multi-objective optimization energy management strategy for power split HEV based on velocity prediction
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基于速度预测的动力分流混合动力汽车多目标优化能量管理策略
DOI：
10.1016/j.energy.2021.121714
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发表时间：
2022
期刊：
影响因子：
9
通讯作者：
Changle Xiang
中科院分区：
文献类型：
--
作者：
Weida Wang;Xinghua Guo;Chao Yang;Yuanbo Zhang;Yulong Zhao;Dengguo Huang;Changle Xiang
在复杂的行驶工况下，混合动力汽车（HEV）的急加速和急减速动作会引起电驱动系统的高倍率充放电电流，严重影响电池的使用寿命。混合储能系统（HESS）结合电池和超级电容器（UC），将是一个可能的解决方案。对于HESS混合动力汽车，除了提高燃油经济性外，实现对蓄电池的保护也是一个重要的目标。然而，提高一个方面的性能可能会牺牲另一个方面的性能。多个优化目标之间的权衡仍然是能源管理设计的一个挑战。针对这一问题，提出了一种基于速度预测的双工况功率分流混合动力汽车多目标优化能量管理策略。首先，为了得到精确的预测输入序列，采用广义回归神经网络（GRNN）对未来速度进行预测。其次，将具有混合储能系统的双模式功率分配混合动力汽车的功率分配问题描述为模型预测控制（MPC）预测时域内的滚动优化问题。提出了一种新的考虑燃料消耗和电池保护的成本函数，并利用庞特里亚金最小值原理（PMP）求解优化问题。此外，Powell-Modified算法被引入到PMP的求解过程中。最后，通过与其他四种策略在四种不同工况下的对比，验证了该策略的有效性。与基于规则的策略相比，所提出的策略使电池电流和燃料消耗的均方根（RMS）分别降低了18.5%和18.9%。
Under the complicated driving conditions, the sharp acceleration and deceleration actions would cause the high-rate charge and discharge current of electric driving system in hybrid electric vehicle (HEV), which brings about a serious impact on the battery lifetime. The hybrid energy storage system (HESS) combined with battery and ultracapacitor (UC), would be a possible solution to this problem. For HEV with HESS, in addition to improving fuel economy, realizing the protection of battery is also an important objective. However, improving one aspect performance may sacrifice another aspect performance. The tradeoff between multiple optimization objectives remains a challenge for energy management design. Aiming at this problem, a multi-objective optimization energy management strategy based on velocity prediction for a dual-mode power split HEV with HESS is proposed in this paper. Firstly, to get the precise predictive input sequence, generalized regression neural network (GRNN) is used to predict future velocity. Secondly, the power distribution of dual-mode power spilt HEV with HESS is described as a rolling optimization problem in the prediction horizon of model predictive control (MPC). A new cost function considering the fuel consumption and the protection of the battery is brought forward, and the optimization problem is solved using Pontryagin's minimum principle (PMP). Moreover, the Powell-Modified algorithm is introduced to execute the solving process of PMP. Finally, the proposed strategy is verified by comparing it with four other strategies under four different driving cycles. Compared to the rule-based strategy, the proposed strategy reduces root mean square (RMS) of battery current and fuel consumption by up to 18.5 % and 18.9 %, respectively.
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