Infinite Horizon Model Predictive Control With Terminal Cost Learning for Hybrid Electric Vehicle Energy Management

Abstract

This article presents an infinite horizon model predictive control (infinite MPC) framework with terminal cost learning for energy management of hybrid electric vehicles. The proposed framework integrates the model-based predictive capability of MPC with the learning potential of a neural network-based terminal cost function. The Monte Carlo method is employed to estimate long-term consumption, aiming to minimize the equivalent fuel consumption of the vehicle. To enhance the generalization of the terminal cost network, a vehicle speed Markov chain is utilized to create a stochastic environment. Leveraging historical data to train the terminal cost network extends the optimization horizon of MPC to infinity, enabling more energy-efficient energy management decisions. Additionally, a knowledge-guided cross-entropy method is adopted for rolling optimization. Simulation results demonstrate the effectiveness of the proposed approach in reducing equivalent fuel consumption. Compared to adaptive equivalent consumption minimization strategy (AECMS), the proposed method increases the proportion of operating points in the high-efficiency zone by 12% and reduces the equivalent fuel consumption by 11.3%. Using three different vehicle weights to test the robustness of the algorithm, the simulation results indicate that infinite MPC still shows better fuel economy than AECMS when facing parameter variations. Furthermore, engine-generator-set-in-the-loop experimental results reveal a 10% reduction in equivalent fuel consumption between AECMS and infinite MPC outcomes, validating the practical applicability of the approach.