This paper deals with the HEV real-time energy management problem using deep reinforcement learning with connected technologies such as Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I). In the HEV energy management problem, it is important to run the engine efficiently in order to minimize its total energy cost. This research proposes a policy model that takes into account road congestion and aims to learn the optimal system mode selection and power distribution when considering the far future by policy-based reinforcement learning. In the simulation, a traffic environment is generated in a virtual space by IPG CarMaker and a HEV model is prepared in MATLAB/Simulink to calculate the energy cost while driving on the road environment. The simulation validation shows the versatility of the proposed method for the test data, and in addition, it shows that considering road congestion reduces the total cost and improves the learning speed. Furthermore, we compare the proposed method with model predictive control (MPC) under the same conditions and show that the proposed method obtains more global optimal solutions.

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A hybrid electric vehicle (HEV) that uses multiple planetary gear units with clutches as transmission system is advanced for the powertrain performance, because the operation of the clutches can lead to distinct operating modes, and the induced possible operating modes provide additional freedom to deal with the energy optimal control problem. Under each operating mode, the powertrain mechanical system has specific dynamical behavior. In order to develop model-based optimization schemes that can tackle the transient operations of the vehicle, exact dynamical modeling is investigated focusing on a hybrid powertrain system that uses a two-planetary-gear transmission box with two clutches. It shows that according to the states of the two clutches, the powertrain system has the power-split mode, parallel mode and the electric vehicle (EV) mode. Finally, an analysis for the calculation of the desired driving torque and its application to the dynamic programming (DP)-based energy management indicate the significance of the developed exact dynamical models. Citation Zhang J Y, Inuzuka S, Kojima T, et al. Dynamical model of HEV with two planetary gear units and its application to optimization of energy consumption. Sci China Inf Sci, 2019, 62(12): 222203, https://doi.org/10.

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We solve a optimization problem for HEV energy management. In this study, HEV system has three driving modes(EV, parallel, series parallel) with two motors, one engine and two clutches. We change this problem into a combination optimization problem by discretizing each torque as a variable. It is difficult to solve this problem because the combinations of variable are so many. To this hard problem, we solved it efficiently by using dynamic programming and applying steady-state approximation. It does not require much computation time, and a global approximate optimal solution can be obtained.