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Energy and Emission Management of Hybrid Electric Vehicles using Reinforcement Learning

机译：利用强化学习的混合动力电动汽车的能量和排放管理

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The electrification of drivetrains of conventional vehicles plays a decisive role in reducing fuel consumption. At the same time decreasing pollutant emission limits must be met also under real driving conditions. This trade-off between fuel consumption and pollutant emissions needs to be optimized, which results in powertrains with increasing complexity. A holistic energy and emission management is needed to control such systems in a way that the fuel consumption is minimized while emission limits are respected.Mathematical optimization methods are difficult to apply in real-time applications due to high computational and calibration demands. Self-learning algorithms, on the other hand, seem to be a suitable solution for such optimization problems.In this paper a control strategy for a hybrid electrical vehicle is presented, consisting of a decision-making agent, trained on different test drives with Reinforcement Learning. For these, the Proximal Policy Optimization method was applied. The strategy controls the torque-split between the combustion engine and electric motor, the power of an electrically heated catalyst and internal engine measures. The method is demonstrated in a simulation framework based on a Diesel P0-HEV with a SCR exhaust gas aftertreatment system. In comparison to a reference strategy a fuel reduction of 3.1 % averaged over the test data set was achieved.
机译：传统车辆的动力学的电气化在降低燃料消耗方面发挥着决定性作用。同时也必须在实际驾驶条件下达到污染物排放限制。需要优化燃料消耗和污染物排放之间的这种权衡，从而导致有动力随着复杂性的增加。需要整体能量和排放管理来控制这些系统，以使燃料消耗最小化，而由于高计算和校准需求，难以在实时应用中难以应用。另一方面，自学习算法似乎是这种优化问题的合适解决方案。本文提出了一种混合动力电动车辆的控制策略，由决策剂组成，在不同的测试驱动器上培训，具有加强件学习。为此，应用了近端策略优化方法。该策略控制内燃机和电动机之间的扭矩分配，电加热催化剂的功率和内部发动机措施。该方法在基于柴油P0-HEV的仿真框架中进行说明，该套件具有SCR废气后处理系统。与参考策略相比，实现了在测试数据集上平均的3.1％的燃油减少。

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