Accurate estimation of energy consumption for EVs and the remaining driving range is crucial for effective energy management. Most existing studies on EV energy consumption estimation are based on correlation-based prediction. However, they do not offer much insight into the causal effects of the driving factors. This study presents a causal machine learning framework that considers the vehicle speed as an intervention to estimate the effect on the energy consumption and the remaining driving range of the EV. Telemetry data obtained from a Musoshi Pop-Up Mini electric cargo vehicle at different speed regimes, payload, and environmental conditions are used to evaluate the proposed framework. This involves the construction of a domain-informed directed acyclic graph for confounding identification and the implementation of Orthogonal Double Machine Learning via the integration of DoWhy and EconML LinearDML estimator for Average Treatment Effects (ATE) and Conditional Average Treatment Effects (CATE). Multiple specifications of the nuisance model and robustness checks, such as placebo refutation, subset stability, overlap checks, and regularization sensitivity, are carried out to ensure the validity of the obtained causal effects. The findings reveal a statistically stable causal relationship between vehicle speed and segment-level energy consumption, with significant heterogeneity observed depending on the operating conditions, where the effect of road slope was found to be the main moderator. The energy effects are further propagated to the remaining driving range to obtain the operational interpretation. Overall, the proposed framework reveals that causal intervention-aware modeling can produce reliable and interpretable insights for range-aware control and smart energy management in electric vehicles.
Gandhi et al. (2026) studied this question.