Heuristic Dynamic Programming Based Online Energy Management Strategy for Plug-In Hybrid Electric Vehicles

• Published in: IEEE transactions on vehicular technology 2019-05, Vol.68 (5), p.4479-4493
• Main Authors: Liu, Jichao, Chen, Yangzhou, Zhan, Jingyuan, Shang, Fei
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Back propagation networks; back propagation neural network; Computer simulation; Controllers; Dynamic models; Dynamic programming; Electric vehicles; Electronic countermeasures; Energy conservation; Energy consumption; Energy management; Engines; Fuel consumption; Fuels; Global optimization; heuristic dynamic programming; Hybrid electric vehicles; Neural networks; Nonlinear dynamics; on-line energy optimization; Optimization; Plug-in hybrid electric vehicle; Real-time systems; Strategy; Tracking; Vehicle dynamics
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2019.2903119

For the online energy optimization problem of plug-in hybrid electric vehicles (P-HEVs), this paper proposes a heuristic dynamic programming (HDP) based online energy management strategy, to minimize the fuel consumption of the P-HEV. First of all, considering the uncertain nonlinear dynamic process of a vehicle in the actual traffic environment, we adopt the back propagation neural network (BPNN) to construct the dynamic model of the P-HEV. Then, on this basis, we utilize the HDP to establish an energy management controller with the aim of minimizing energy consumption of the P-HEV. Moreover, the energy management controller is implemented by an online energy management strategy algorithm. To verify the effect of the controller, we employ a practical route in Beijing road network to simulate the BPNN model of the P-HEV and the proposed energy management strategy. The experimental results show several advantages of our strategy. First, compared to the analytic model, the BPNN model can reflect the real dynamic process of the P-HEV with a higher precision. Second, the assigned torques by the strategy can effectively make the vehicle track the desired vehicle-speeds, and the tracking accuracy of the vehicle-speed is higher than 98%. Besides, on the premise of ensuring the real-time performance, the proposed strategy can further reduce the fuel consumption and emissions of the P-HEV when compared with the existing online energy management strategies, although its fuel consumption is more than that of the offline global optimization energy management strategy by 4% approximately.