Paper Info
Title
Electric Vehicle Optimized Charge and Drive Management
Abstract
Electric vehicles (EVs) have been considered as a solution to the environmental issues caused by transportation, such as air pollution and greenhouse gas emission. However, limited energy capacity, scarce EV supercharging stations, and long recharging time have brought anxiety to drivers who use EVs as their main mean of transportation. Furthermore, EV owners need to deal with a huge battery replacement cost when the battery capacity degrades. Yet in-house EV chargers affect the pattern of the power grid load, which is not favorable to the utilities. The driving route, departure/arrival time of daily trips, and electricity price influence the EV energy consumption, battery lifetime, electricity cost, and EV charger load on the power grid. The EV driving range and battery lifetime issues have been addressed by battery management systems and route optimization methodologies. However, in this article, we are proposing an optimized charge and drive management (OCDM) methodology that selects the optimal driving route, schedules daily trips, and optimizes the EV charging process while considering the driver’s timing preference. Our methodology will improve the EV driving range, extend the battery lifetime, reduce the recharging cost, and diminish the influence of EV chargers on the power grid. The performance of our methodology compared to the state of the art have been analyzed by experimenting on three benchmark EVs and three drivers. Our methodology has decreased EV energy consumption by 27%, improved the battery lifetime by 24.8%, reduced the electricity cost by 35%, and diminished the power grid peak load by 17% while increasing less than 20 minutes of daily driving time. Moreover, the scalability of our OCDM methodology for different parameters (e.g., time resolution and multiday cycles) in terms of execution time and memory usage has been analyzed.
Year
DOI
Venue
2017
10.1145/3084686
ACM Trans. Design Autom. Electr. Syst.
Keywords
Field
DocType
Electric vehicle,battery,smart grid,power estimation,MILP
Automotive engineering,Smart grid,Simulation,Electric vehicle,Computer science,Real-time computing,Driving range,Schedule,Battery (electricity),Energy consumption,Greenhouse gas,Scalability
Journal
Volume
Issue
ISSN
23
Issue-in-Progress
1084-4309
Citations 
PageRank 
References 
3
0.39
30
Authors
3
Name
Order
Citations
PageRank
Korosh Vatanparvar113416.20
Mohammad Abdullah Al Faruque262765.35
FaruqueMohammad Abdullah Al341.07