TY - GEN
T1 - Energy-Aware Driving Profile of Autonomous Electric Vehicles Considering Regenerative Braking Limitations
AU - Mohammadi, Masoud
AU - Heydari, Shoeib
AU - Fajri, Poria
AU - Harirchi, Farshad
AU - Yi, Zonggen
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper focuses on finding an optimal energy-aware speed trajectory of an Autonomous Electric Vehicle (AEV) considering regenerative braking capability and its limitations. A position-based Electric Vehicle (EV) energy consumption model is used to emulate vehicle-road operating conditions. It is assumed that the EV is driven in an urban area where the route is only constrained by maximum speed limits and traffic signs. The eco-driving problem is formulated as a Mixed Integer Linear Programming (MILP) problem and is solved for two different case studies to demonstrate the importance of considering regenerative braking in identifying optimal speed trajectory of AEVs. The MILP problem is coded in Python and CPLEX is used as a solver for the optimization problem. The results show a variation in the optimal speed trajectories and confirm that when regenerative braking limitations are considered in the calculations leading to an energy-aware speed trajectory, energy consumption can be reduced. This study sets forth a framework for optimizing the braking profile of an AEV by realistically taking into account the vehicle's regenerative braking limitations which ultimately yields an optimal speed trajectory.
AB - This paper focuses on finding an optimal energy-aware speed trajectory of an Autonomous Electric Vehicle (AEV) considering regenerative braking capability and its limitations. A position-based Electric Vehicle (EV) energy consumption model is used to emulate vehicle-road operating conditions. It is assumed that the EV is driven in an urban area where the route is only constrained by maximum speed limits and traffic signs. The eco-driving problem is formulated as a Mixed Integer Linear Programming (MILP) problem and is solved for two different case studies to demonstrate the importance of considering regenerative braking in identifying optimal speed trajectory of AEVs. The MILP problem is coded in Python and CPLEX is used as a solver for the optimization problem. The results show a variation in the optimal speed trajectories and confirm that when regenerative braking limitations are considered in the calculations leading to an energy-aware speed trajectory, energy consumption can be reduced. This study sets forth a framework for optimizing the braking profile of an AEV by realistically taking into account the vehicle's regenerative braking limitations which ultimately yields an optimal speed trajectory.
UR - https://www.scopus.com/pages/publications/85134660914
UR - https://www.mendeley.com/catalogue/a7471d41-f7be-32ad-8093-dfc9ff24c403/
U2 - 10.1109/ITEC53557.2022.9813916
DO - 10.1109/ITEC53557.2022.9813916
M3 - Conference contribution
AN - SCOPUS:85134660914
T3 - 2022 IEEE Transportation Electrification Conference and Expo, ITEC 2022
SP - 196
EP - 201
BT - 2022 IEEE Transportation Electrification Conference and Expo, ITEC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Transportation Electrification Conference and Expo, ITEC 2022
Y2 - 15 June 2022 through 17 June 2022
ER -