Title :
Long-Trip Optimal Energy Planning With Online Mass Estimation for Battery Electric Vehicles
Author :
Maalej, Khalil ; Kelouwani, Sousso ; Agbossou, Kodjo ; Dube, Yves ; Henao, Nilson
Author_Institution :
Dept. of Mech. Eng., Univ. du Quebec a Trois-Rivieres, Trois-Rivieres, QC, Canada
Abstract :
This paper addresses the optimal battery charging schedule for a long trip. Starting with a fully charged battery, the electric vehicle (EV) must stop at least once for battery charging before reaching its destination. Since the battery lifespan is thoroughly related to its depth-of-discharge and since the charging time can be long, it is therefore useful to provide a feasible battery charging schedule during the long trip. Minimizing a cost function, which includes the charging energy cost, battery degradation, and the charging duration, an optimal charging schedule is proposed and successfully validated with small-pickup EV data. In addition, this method takes into account the possible mass change, as well as wind effect on the predicted energy consumption. A comparative study with the commonly used maximum depleting and charging schedule suggests that the proposed approach is efficient, and it contributes to reducing the overall trip duration while reducing, at the same time, the battery degradation.
Keywords :
battery powered vehicles; power consumption; secondary cells; EV optimal battery charging scheduling; battery degradation; battery electric vehicle; battery lifespan; cost function minimization; depth-of-discharge; energy consumption; long-trip optimal energy planning; online mass estimation; Batteries; Charging stations; Estimation; Schedules; US Department of Defense; Vehicle dynamics; Vehicles; Battery degradation; Electric vehicles; battery degradation; charging schedule; electric vehicles (EVs); energy efficiency; energy management; vehicle dynamics;
Journal_Title :
Vehicular Technology, IEEE Transactions on
DOI :
10.1109/TVT.2014.2376700