Limited driving range and availability of charging infrastructures are still among the main barriers of adoption of electric vehicles (EVs) in the market. Combination of those limiting factors causes ‘range anxiety’ in EV users. While different EV battery technologies and charging infrastructures are under development, one short-term solution to reduce EV users’ range anxiety is to provide the EV user with an accurate range estimation. In this study, an EV range estimation technique is proposed that recognises the current driving pattern and then classifies 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.

有限的行驶里程和充电基础设施的可用性仍然是市场上采用电动汽车 (EV) 的主要障碍之一。这些限制因素的组合导致电动汽车用户的“里程焦虑”。虽然不同的 EV 电池技术和充电基础设施正在开发中，但减少 EV 用户里程焦虑的一种短期解决方案是为 EV 用户提供准确的里程估计。在这项研究中，提出了一种 EV 里程估计技术，该技术可以识别当前的驾驶模式，然后将其分类为一个预定义的集群（驾驶模式）。然后根据每个集群的平均能耗调整未来每公里的能耗。更新能源消耗率后，EV 续航里程将根据电池充电状态进行计算。驾驶模式聚类考虑了不同的特征，其中“平均速度”和“平均功率”被确定为该应用程序的最佳选择。使用实际驾驶数据验证了所提出的 EV 里程估计器的有效性，该数据在 EV 能源消耗估计中的平均误差为 9%。