Influence of battery capacity on performance of an electric vehicle fleet

Abstract

In this study, the influence of electric vehicle (EV) range on overall performance of an EV fleet is analysed. Various casestudies are investigated in which the EV fleet is simulated to cover a number of target points in a typical delivery problem. A trip scheduling algorithm is proposed in order to get all target points while considering the EVs range. The critical role of EV range in performance improvement of the whole fleet is analysed and an optimum EV range is obtained with regard to the whole fleet mileage. The results demonstrate that 250 km is an optimum range for an EV fleet to work in an area of 100×100 km². The number of target points, called task density, doesn't affect the optimum EV range very much and it can be determined only based on size of the service area. Finally, lithium-sulfur battery is discussed as a promising technology to extend EV range.

Optimization of power losses in electric vehicle battery by wireless charging method with consideration of the laser optic effect

“…Already a changeover switch for transition from load to battery is shown in the simulation circuit of secondary side control with LUO converter: in that if the switch is in ON (1) position, the PI controller will be ON and the LUO converter enabled, and the converter output is applied to the load. Similarly, if the switch is in OFF (0) position, the charging controller [11][12][13] will be ON and the LUO converter enabled, and the converter output is applied to the battery. This waveform will clearly explain the variation of duty cycle and the importance of secondary side control by the LUO converter, where battery charging 14,15 can be done with high duty ratio when compared to the inverter connected to the LED load.…”

Electric Vehicle Fleet Management Using Ant Colony Optimisation

“…For the sake of simplicity, no vehicle dynamics, nor topography considerations, nor energy recovery by kinematics or heat is implemented in this study. The minimum range of EVs to operate in a 50 × 50 2 area is determined based on a previous study by Fotouhi, et al, (2016) in the literature that suggests a range of 125 km for a similar size area. It should be noted that the main conclusions of this study are not expected to be affected by changing the range of EV.…”

“…A basic version of the Nearest Neighbour-based FMS algorithm has been developed in the literature by Fotouhi, et al, (2016). However, in this study, improved versions of the Nearest Neighbour technique are proposed and used that are called 2 Point Optimisation…”

“…Simultaneously, the depot is served as the only charging point (overnight slow charging). This case-study has been introduced in a previous study and a solution was obtained for it using Nearest-Neighbour technique (Fotouhi, et al, 2016). In this study, firstly the Nearest-Neighbour FMS algorithm is improved and secondly, a new FMS algorithm is developed based on ACO technique.…”

“…This piece of randomness in the FMS algorithm can lead to some gain in the performance since it contributes to algorithm's exploration. However, a previous study in the literature by Fotouhi, et al (2016) demonstrates that the initial point in such a FMS case-study is quite important. Although, the results are shown for Nearest…”