Reducing range anxiety to stimulate use of electric vehicles

Although battery electric vehicles have many advantages, such as low energy cost and no local emissions, the spread is still limited. For many drivers, range anxiety, the concern about whether or not you can reach your destination based on the current battery charge of your vehicle, is holding them back. In his thesis, Jiquan Wang, PhD student in the research group Dynamics & Control, aims at reducing the driver’s range anxiety and extending the driving range of battery electric vehicles based on the current hardware technology.

Due to the increased focus on environmental issues and sustainable energy, battery electric vehicles (BEVs) are receiving more and more attention. They have advantages in energy cost and local emissions compared to internal combustion engine vehicles. However, the spread of BEVs is limited by a high initial price, long charging time, few charging stations and limited driving range. The limited driving range and long charging time of batteries make the drivers more concerned about whether they can reach the destination based on the current battery state of charge, which is called range anxiety. Range anxiety is considered as one of the major factors that affect the acceptance of BEVs. 

In his thesis, Jiquan Wang analyzed, modelled and validated the energy consumption of a BEV. By comparing the regenerative braking control strategy of the vehicle with a one-pedal-driving algorithm insight is gained in the improved vehicle powertrain efficiency and driving experience. Subsequently, the energy consumption along a chosen route is predicted based on route information and energy consumption models. The algorithm can then adjust to the energy consumption prediction based on the driving behavior during driving.
Furthermore, an advanced route searching algorithm, to find the most energy efficient route for BEVs, is developed. This route searching algorithm is verified by simulations and driving tests on the public road.

Jiquan Wang, research group Dynamics & Control, will defend his dissertation on October 31, 2016, at 4pm.