TY - GEN
T1 - Integrated optimization of kinematic and air-conditioning states for eco-driving and eco-cooling
AU - Zhang, Chuntao
AU - Ning, Changjiu
AU - Ma, Heyang
AU - Sun, Chao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The powertrain and air-conditioning (AC) system constitute the primary electricity consumption components in electric vehicles (EVs). Hence, to enhance the energy optimality of an ego vehicle that has to travel across consecutive traffic lights following a preceding vehicle with stochastic driving behavior, as presented in the benchmark challenge of CVCI 2023, a model predictive control (MPC) -based optimization method is proposed to parallel conduct kinematic optimization and AC regulation. Furthermore, a hierarchical framework is adopted in kinematic optimization for speed planning and powertrain control. To incorporate the road terrain information and enhance the feasibility of the formulated optimization problem, speed planning with MPC is formulated in the space domain with an adaptive planning horizon. Validation results exhibit that the proposed method reduces the overall electricity consumption by 12.82% to 24.84% among the five provided traffic scenarios compared with the benchmark method, demonstrating superior energy-saving potential of the proposed method.
AB - The powertrain and air-conditioning (AC) system constitute the primary electricity consumption components in electric vehicles (EVs). Hence, to enhance the energy optimality of an ego vehicle that has to travel across consecutive traffic lights following a preceding vehicle with stochastic driving behavior, as presented in the benchmark challenge of CVCI 2023, a model predictive control (MPC) -based optimization method is proposed to parallel conduct kinematic optimization and AC regulation. Furthermore, a hierarchical framework is adopted in kinematic optimization for speed planning and powertrain control. To incorporate the road terrain information and enhance the feasibility of the formulated optimization problem, speed planning with MPC is formulated in the space domain with an adaptive planning horizon. Validation results exhibit that the proposed method reduces the overall electricity consumption by 12.82% to 24.84% among the five provided traffic scenarios compared with the benchmark method, demonstrating superior energy-saving potential of the proposed method.
KW - eco-driving
KW - electric vehicles
KW - energy management strategy
KW - model predictive control
UR - http://www.scopus.com/inward/record.url?scp=85185388528&partnerID=8YFLogxK
U2 - 10.1109/CVCI59596.2023.10397336
DO - 10.1109/CVCI59596.2023.10397336
M3 - Conference contribution
AN - SCOPUS:85185388528
T3 - Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023
BT - Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023
Y2 - 27 October 2023 through 29 October 2023
ER -