iEVEM: Big Data-Empowered Framework for Intelligent Electric Vehicle Energy Management - Siyan Guo; Cong Zhao (Systems)
Recent years have witnessed an unprecedented boom of Electric Vehicles (EVs). However, EVs’ further development confronts critical bottlenecks due to EV Energy (EVE) issues like battery hazards, range anxiety, and charging inefficiency. Emerging data-driven EVE Management (EVEM) is a promising solution but still faces fundamental challenges, especially in terms of reliability and efficiency. This article presents iEVEM, the first big data-empowered intelligent EVEM framework, providing systematic support to the essential driver-, enterprise-, and social-level intelligent EVEM applications. Particularly, a layered data architecture from heterogeneous EVE data management to knowledge-enhanced intelligent solution design is provided, and an edge–cloud collaborative architecture for the networked system is proposed for reliable and efficient EVEM, respectively. We conducted a proof-of-concept case study on a typical EVEM task (i.e., EV energy consumption outlier detection) using real driving data from 4000+ EVs within three months. The experimental results show that iEVEM achieves a significant boost in reliability and efficiency (i.e., up to 47.48% higher in detection accuracy and at least 3.07× faster in response speed compared with the state-of-art approaches). As the first intelligent EVEM framework, iEVEM is expected to inspire more intelligent energy management applications exploiting skyrocketing EV big data.