A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles

• Published in: Energy (Oxford) Vol. 175; pp. 378 - 392
• Main Authors: Jinquan, Guo, Hongwen, He, Jiankun, Peng, Nana, Zhou
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.05.2019; Elsevier BV
• Subjects: Adaptive control; AEMS; Artificial neural networks; Computer simulation; DNN; Driving; Dynamic programming; EDPS; Electric vehicles; Energy consumption; Energy distribution; Energy management; Fuel economy; Hybrid electric vehicles; Neural networks; PHEV; Predictive control; Renewable energy; SOC reference constraint; State of charge; Traffic; Traffic information; EDPS; AEMS; PHEV; SOC reference constraint; DNN
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2019.03.083

In this paper, an adaptive energy management strategy (AEMS) under model predictive control (MPC) framework is proposed. The main advantage of the AEMS is that it fully integrates the economy driving pro system (EDPS), which can provide the renewable energy consumption trajectory considering dynamic traffic information of target driving task, namely the state of charge (SOC) reference constraint for the MPC optimal calculation at each control step. Moreover, based on the dynamically updated traffic information, the SOC reference constraint will be re-planned with correction, which will further reflect the ideal energy consumption trend over the actual driving cycle. For the MPC prediction aspect, the deep neural network (DNN) is applied in this paper to predict the future short-term velocity with 5s, 10s and 15s horizon, respectively. Meanwhile, the dynamic programming (DP) is applied to calculate the optimal energy distribution at each MPC control step. Simulation results show that under the test driving cycle, the optimal MPC predictive horizon with the assistance of EDPS is 10s, and the fuel economy rate can improve up to 6.48% compared with energy management without the assistance of EDPS. Moreover, the HIL test indicates the AEMS has well real-time performance as well. •A method of MPC-based SOC reference constraint for each control step.•A dynamic updating SOC reference constraint method based on the EDPS.•A deep neural network-based velocity prediction method with the different horizon.•The novel AEMS fuel economy performance in different control horizon.