Research on Eco-driving Control Strategy of Connected Electric Vehicle Based on Learning-MPC

Eco-driving is an important way to achieve sustainable mobility and sustainable urban transport development. To improve the energy efficiency of connected electric vehicles, a two-stage non-conservative eco-driving control strategy combining learning-based model predictive control and fast interior point method is proposed for complex and variable urban signalized intersection scenarios, taking into full consideration constraints such as signal phase and timing information of real traffic and the limited predictive capability of vehicles for future information. Before vehicle departure, the energy-efficient optimal control problem is constructed based on passenger destination and road speed limit information, while the band-stop function is introduced to improve the computational efficiency to transform the speed constraint into a part of the objective function, and the interior point method coarse planning solves the vehicle energy-efficient optimal reference speed trajectory; At departure, the vehicle predicts the dynamic signal phase and timing information, and the Learning-MPC strategy learns the state prediction model of the vehicle online through Gaussian process to realize the tracking control of the vehicle energy-efficient optimal reference speed trajectory. The simulation shows that the proposed method can achieve 9.7% energy saving compared with the classical acceleration-uniformity-deceleration strategy, and indicates better energy saving potential as the length of the predicted field of view increases. It is further verified that the error accumulation problem caused by the discretization of the traditional MPC non-flexible conservative system state prediction model is solved by machine learning, and the optimal effect of vehicle eco-driving control is improved to a higher degree. © 2024 Chinese Mechanical Engineering Society. All rights reserved.