Energy Management Strategy for Plug-in Hybrid Vehicles Based on Model Predictive Control and Local Encryption Dynamic Programming School of Automotive Studies, Tongji

Format:

Book

Conference/Event

Author/Creator:

Wu, Changkun, author.

Contributor:

Ni, Jimin

Shi, Xiuyong

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

A model predictive control (MPC) energy management strategy (EMS) coupled with offline dynamic programming (ODP) based on historical average vehicle speed, ODP-MPC, is proposed in this paper. The effectiveness of ODP-MPC is verified using historical traffic flow datasets from the open literature. The simulation results show that ODP-MPC can reduce fuel consumption by 1.1% to 7.3% compared to MPC. Moreover, at the prediction area Hp=3(3s), the fuel consumption of ODP-MPC is only 2.1% higher than that of the DP algorithm. This indicates that ODP-MPC can approximate the theoretical fuel economy. As for the computational effort, the online computation time of ODP-MPC is improved by 6.3%~22.9% compared to MPC, but still less than the 1s time step. Reducing the number of grid cells (m) or increasing the distance step (distf) in offline DP reduces the offline computational cost and the fuel economy of ODP-MPC. The coupled locally encrypted meshing strategy (LEMS) in ODP-MPC resolves the trade-off between computational cost and fuel-saving performance. Compared to ODP-MPC with m=1001 and distf=10, when m=21 and distf=30, the fuel consumption of ODP-MPC with LEMS remains nearly unchanged, while the computational cost is reduced by 99.1%

Notes:

Vendor supplied data

Publisher Number:

2024-01-2781

Access Restriction:

Restricted for use by site license