Abstract:

Electric vehicle (EV) technology is rapidly advancing, owing to their negligible local emissions. The lack of charging infrastructure and limited driving range remains the most significant challenges to EV adoption. Combination of those limiting factors causes ‘range anxiety’ in EV users. In this study, an EV range estimation technique is proposed to recognize the current driving pattern and then classify it into one of the predefined clusters (driving modes). The future energy consumption per kilometre is then tuned according to the average energy consumption of each cluster. Having an updated energy consumption rate, the EV range is calculated based on the battery state-of-charge. Different features are considered for driving pattern clustering where ‘average speed’ and ‘average power’ were identified as the best choices for this application. The effectiveness of the proposed EV range estimator is validated using real driving data that gives an average error of 9% in EV energy consumption estimation ahead. © IMechE 2021.