Data-driven decomposition analysis and estimation of link-level electric vehicle energy consumption under real-world traffic conditions

Abstract

Electric vehicles (EVs) have great potential to reduce transportation-related fossil fuel consumption as well as pollutant and greenhouse gas (GHG) emissions, due to their use of renewable electricity as the sole energy source. Therefore, the wide-spread deployment of EVs is regarded asan attractive means to mitigate the environmental problems (e.g., air pollution and climate change) resulting from transportation activities. Government agencies are trying to promote EV deployment by allocating considerable funding as well as promulgating supportive policies. However, the mass adoption of EVs is still impeded by the limited charging infrastructure and all-electric-range (AER). All these lead to a critical research topic: the EV energy consumption analysis and estimation under real-world traffic conditions, which is fundamental to various types of EV-centred applications that aim at improving the EV energy efficiency and extending the AER. For example, eco-routing systems for EVs rely on accurate link-level energy consumption estimation to calculate the EV energy consumption costs of the different route options. In this work, to obtain an accurate link-level energy consumption estimation model for EVs, the energy consumption under real-world traffic congestion is decomposed based on two proposed impact factors: positive kinetic energy (PKE) and negative kinetic energy (NKE). Upon this decomposition, a data-driven model is built to estimate EV energy consumption on each roadway link considering real-world traffic conditions. Finally, the model performance is evaluated by comparing with the performance of baseline model adapted from existing models. The results show that the proposed EV link-level energy consumption estimation model outperforms the existing models in terms of accuracy, implying that it is quite promising in various on-board EV applications.