Data-driven energy management and velocity prediction for four-wheel-independent-driving electric vehicles

Abstract

This paper proposes an online energy management and optimization method for four-wheel-independent-driving electric vehicles via stochastic model predictive control (SMPC), where velocity is predicted as the foundation to ensure feasibility and efficiency. By utilizing operating data of real-world electric vehicles from a big data platform, a data-driven Markov chain method is adopted to achieve vehicle velocity prediction in an accurate and reliable way. On top of the proposed method, real-time updates of the sample space and online substitution of the velocity-acceleration (V-A) state space can be realized, which mitigates problems of prediction interruption resulting from deficiency of sample state. Simulation results based on a constructed Hardware-in-Loop system indicate effectiveness of velocity prediction with root-mean-square error under 1.3 km/h. In the perspective of the energy conservation, the SMPC method can decrease energy consumption by 7.92% compared with traditional Rule-based methods, which is close to the optimization result of a conventional dynamic programming method. Further simulation and test results demonstrate that the proposed data-driven method is capable of realizing online accurate velocity prediction and energy management for real-world vehicles. • An energy management optimization method for four-wheel-independent-driving vehicles. • A data-driven Markov chain with is adopted for vehicle velocity prediction. • The stochastic model prediction control method is employed for torque allocation. • The proposed energy optimization method is verified through the Hardware-in-Loop test.