TY - GEN
T1 - Energy Management Strategy for Heavy-Duty Commercial Vehicles with Hybrid Electric System in Cold and High-Altitude Environment
AU - Cheng, Jiawei
AU - Ma, Yue
AU - Zhang, Qixiang
AU - Zhang, Chongbing
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This Paper proposes a global optimization energy management strategy based on dynamic programming for a series hybrid heavy-duty commercial vehicle powered by micro gas turbines in high-altitude environments. It considers the impact of high altitudes on the characteristics of gas turbines and batteries, integrating these factors into the strategy. Simulation modeling based on experimental data from hybrid commercial trucks which is conducted to validate the energy management strategy. Utilizing dynamic programming, the strategy divides time and control variables into grids, systematically exploring and optimizing the optimal control sequences for SOC at each time step until the final one, thereby generating a series of optimal SOC control sequences. Simulation results indicate that, with the modeling of real vehicle data, adoption of the proposed energy management strategy reduces hybrid system fuel consumption by 25% for series hybrid heavy-duty commercial vehicles, effectively minimizing energy losses during driving conditions beyond standard operations.
AB - This Paper proposes a global optimization energy management strategy based on dynamic programming for a series hybrid heavy-duty commercial vehicle powered by micro gas turbines in high-altitude environments. It considers the impact of high altitudes on the characteristics of gas turbines and batteries, integrating these factors into the strategy. Simulation modeling based on experimental data from hybrid commercial trucks which is conducted to validate the energy management strategy. Utilizing dynamic programming, the strategy divides time and control variables into grids, systematically exploring and optimizing the optimal control sequences for SOC at each time step until the final one, thereby generating a series of optimal SOC control sequences. Simulation results indicate that, with the modeling of real vehicle data, adoption of the proposed energy management strategy reduces hybrid system fuel consumption by 25% for series hybrid heavy-duty commercial vehicles, effectively minimizing energy losses during driving conditions beyond standard operations.
KW - Heavy-duty commercial vehicle
KW - energy management strategy
KW - fuel economy
KW - global optimization algorithm
KW - high-altitude cold environment
KW - micro gas turbine
UR - https://www.scopus.com/pages/publications/85217245014
U2 - 10.1109/CVCI63518.2024.10830069
DO - 10.1109/CVCI63518.2024.10830069
M3 - Conference contribution
AN - SCOPUS:85217245014
T3 - Proceedings of the 2024 8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024
BT - Proceedings of the 2024 8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024
Y2 - 25 October 2024 through 27 October 2024
ER -