| This paper presents different approaches to optimize fast charging and workplace charging strategy of battery electric vehicle (BEV) drivers. For the fast charging analysis, a rule-based model was built to simulate BEV charging behavior. Monte Carlo analysis was performed to explore to the potential range of congestion at fast charging stations which could be more than four hours at the most crowded stations. Genetic algorithm was performed to explore the theoretical minimum waiting time at fast charging stations, and it can decrease the waiting time at the most crowded stations to be shorter than one hour. A deterministic approach was proposed as a feasible suggestion that people should consider to take fast charging when the state of charge is approaching 40 miles. This suggestion is hoped to help to minimize potential congestion at fast charging stations. For the workplace charging analysis, scenario analysis was performed to simulate temporal distribution of charging demand under different workplace charging strategies. It was found that if BEV drivers charge as much as possible and as late as possible at workplace, it could increase the utility of solar-generated electricity while relieve grid stress of extra intensive electricity demand at night caused by charging electric vehicles at home. |
