Among the many approaches towards fuel economy, the adoption of electric vehicles (EV) may have the greatest impact. However, existing studies on EV adoption predict very different market evolutions, which causes a lack of solid ground for strategic decision making. New methodological tools, based on Artificial Intelligence, might offer a different perspective. This paper proposes supervised Machine Learning (ML) techniques to identify key elements in EV adoption, comparing different ML methods for the classification of potential EV purchasers. Namely, Support Vector Machines, Artificial Neural Networks, Deep Neural Networks, Gradient Boosting Models, Distributed Random Forests, and Extremely Randomized Forests are modeled utilizing data gathered on users’ inclinations towards EV. Although a Support Vector Machine with polynomial kernel slightly outperforms the other algorithms, all of them exhibit comparable predictability, implying robust findings. Further analysis provides evidence that having only partial information (e.g. only socioeconomic variables) has a significant negative impact on model performance, and that the synergy across several types of variables leads to higher accuracy. Finally, the examination of misclassified observations reveals two well-differentiated groups, unveiling the importance that the profiling of potential purchaser may have for marketing campaigns as well as for public agencies that seek to promote EV adoption.

在许多实现燃油经济性的方法中，采用电动汽车（EV）可能会产生最大的影响。但是，有关电动汽车采用率的现有研究预测，市场的发展将有很大不同，这导致缺乏战略决策的坚实基础。基于人工智能的新方法论工具可能会提供不同的观点。本文提出了有监督的机器学习（ML）技术，以识别电动汽车采用中的关键要素，并比较不同的ML方法对潜在的电动汽车购买者进行分类。即，支持向量机，人工神经网络，深层神经网络，梯度提升模型，分布式随机森林和极度随机森林是利用用户对EV的偏好收集的数据进行建模的。尽管支持多项式内核的支持向量机略胜于其他算法，但它们都具有可比性的可预测性，这表明得出了可靠的结论。进一步的分析提供了证据，即仅拥有部分信息（例如，只有社会经济变量）会对模型性能产生重大的负面影响，并且跨几种类型的变量的协同作用会导致更高的准确性。最后，对错误分类的观察结果的检查揭示了两个完全不同的群体，揭示了潜在购买者的概况分析对于营销活动以及寻求推广电动汽车采用的公共机构的重要性。仅社会经济变量）对模型的性能具有重大的负面影响，并且跨几种类型的变量的协同作用可以提高准确性。最后，对错误分类的观察结果的检查揭示了两个完全不同的群体，揭示了潜在购买者的概况分析对于营销活动以及寻求推广电动汽车采用的公共机构的重要性。仅社会经济变量）对模型的性能具有重大的负面影响，并且跨几种类型的变量的协同作用可以提高准确性。最后，对错误分类的观察结果的检查揭示了两个完全不同的群体，揭示了潜在购买者的概况分析对于营销活动以及寻求推广电动汽车采用的公共机构的重要性。