Data-driven energy management and velocity prediction for four-wheel-independent-driving electric vehicles
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 e...
Saved in:
Published in | eTransportation (Amsterdam) Vol. 9; p. 100119 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.08.2021
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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. |
---|---|
ISSN: | 2590-1168 2590-1168 |
DOI: | 10.1016/j.etran.2021.100119 |