To improve a parallel hybrid electric vehicle's (HEV's) fuel economy, this study develops a real-time optimisation strategy with a learning-based method that predicts the driver's power demand under the connected environment. This demand is strongly constrained by the total power generated by the energy sources. Therefore, a key issue of solving the energy management problem in real time by model-based predictive optimisation is to predict the power demand of each receding horizon. The proposed optimisation strategy consists of two layers. The upper layer provides the prediction of the driver's torque demand. Gaussian process regression (GPR) is used to predict the driver's demand with the uncertain and stochastic estimation between the traffic environment and torque demand. Vehicle-to-vehicle and vehicle-to-infrastructure data are used as the inputs of the GPR model. The lower layer performs finite-horizon optimisation based on the cost function of energy consumption. A receding horizon control (RHC) problem is formulated, and optimisation is achieved by a sequential quadratic programming algorithm. To validate the proposed optimisation strategy, a powertrain control co-simulation platform with a traffic-in-the-loop environment is constructed, and results validation with the platform is demonstrated. The comparisons with the dynamic programming and no-prediction RHC results show that the proposed strategy can improve fuel economy.

中文翻译：

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通过对驾驶员功率需求的车载预测，使混合动力汽车的后视最优控制

为了提高并行混合动力电动汽车（HEV）的燃油经济性，本研究开发了一种基于学习的方法的实时优化策略，该方法可预测互联环境下驾驶员的动力需求。这种需求受到能源产生的总功率的强烈限制。因此，通过基于模型的预测优化来实时解决能源管理问题的关键问题是预测每个后退层的电力需求。所提出的优化策略包括两层。上层提供了驾驶员扭矩需求的预测。高斯过程回归（GPR）用于通过交通环境和扭矩需求之间的不确定性和随机性估算来预测驾驶员的需求。车辆对车辆和车辆对基础设施的数据用作GPR模型的输入。下层根据能耗的成本函数执行有限水平优化。提出了后退水平控制（RHC）问题，并通过顺序二次编程算法实现了优化。为了验证所提出的优化策略，构建了具有环行交通环境的动力总成控制协同仿真平台，并演示了该平台的结果验证。与动态规划和无预测RHC结果的比较表明，所提出的策略可以提高燃油经济性。提出了后退水平控制（RHC）问题，并通过顺序二次编程算法实现了优化。为了验证所提出的优化策略，构建了具有环行交通环境的动力总成控制协同仿真平台，并演示了该平台的结果验证。与动态规划和无预测RHC结果的比较表明，所提出的策略可以提高燃油经济性。提出了后退水平控制（RHC）问题，并通过顺序二次编程算法实现了优化。为了验证所提出的优化策略，构建了具有环行交通环境的动力总成控制协同仿真平台，并演示了该平台的结果验证。与动态规划和无预测RHC结果的比较表明，所提出的策略可以提高燃油经济性。