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Multi-objective energy management for fuel cell electric vehicles using online-learning enhanced Markov speed predictor

机译：使用在线学习增强的Markov速度预测因子的燃料电池电动汽车多目标能源管理

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摘要

As one of promising solutions towards future cleaner transportation, fuel cell electric vehicles have been widely regarded as an attractive technology in both academia and industry. To enhance the vehicle's operation efficiency, this paper proposes a multi-criteria power allocation strategy for a fuel cell/battery-based plug-in hybrid electric vehicle. Firstly, an adaptive online-learning enhanced Markov velocity-forecast approach is proposed. Its predictive behaviors can be adjusted accordingly under various driving scenarios through the real-time-identified transition probability matrices. Subsequently, based only on the previewed trip duration information and the speed prediction results, a state-of-charge (SOC) reference planning approach is designed to guide the allocation of battery energy. Combining with the velocity-forecast results and the reference SoC, model predictive control derives the optimal power-allocation decision through minimizing the multi-purpose objective function in a finite time horizon. It has been verified that (1) the presented power allocation strategy can reduce over 12.05% H2 consumption and over 94.40% fuel cell power spikes against the commonly used Charge-Depleting/Charge-Sustaining strategy; (2) despite the existence of mission time estimation errors, the presented control strategy could still bring performance enhancement over the benchmark strategy, thus demonstrating its feasibility for real-world implementations.
机译：作为未来清洁运输的有希望的解决方案之一，燃料电池电动汽车被广泛认为是学术界和工业中的一种有吸引力的技术。为了提高车辆的运行效率，本文提出了一种用于燃料电池/电池的插入式混合动力电动车辆的多标准功率分配策略。首先，提出了一种自适应在线学习增强的马尔可夫速度预测方法。可以通过实时识别的转换概率矩阵在各种驾驶场景下相应地调整其预测行为。随后，仅基于预览的跳闸持续时间信息和速度预测结果，设计了充电状态（SOC）参考计划方法以引导电池能量的分配。结合速度预测结果和参考SOC，模型预测控制通过最小化有限时间范围内的多功能目标函数来导出最佳功率分配决策。已经验证了（1）所示的功率分配策略可以减少12.05％的H2消耗量，超过94.40％的燃料电池电力针对常用的电荷耗尽/充电策略; （2）尽管存在任务时间估计错误，但呈现的控制策略仍然可以通过基准战略带来性能提升，从而证明了其对现实世界实施的可行性。

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