2020/09/23 by Karl Schwenk, Stefan Meisenbacher, Schwenk, Karl +9
Engineering · #Advanced Battery Technologies Research #Electric Vehicles and Infrastructure #Energy Harvesting in Wireless Networks #FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.12201
openalex publication_date 2020/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Smart charging of Electric Vehicles (EVs) reduces operating costs, allows\nmore sustainable battery usage, and promotes the rise of electric mobility. In\naddition, bidirectional charging and improved connectivity enables efficient\npower grid support. Today, however, uncoordinated charging, e.g. governed by\nusers' habits, is still the norm. Thus, the impact of upcoming smart charging\napplications is mostly unexplored. We aim to estimate the expenses inherent\nwith smart charging, e.g. battery aging costs, and give suggestions for further\nresearch. Using typical on-board sensor data we concisely model and validate an\nEV battery. We then integrate the battery model into a realistic smart charging\nuse case and compare it with measurements of real EV charging. The results show\nthat i) the temperature dependence of battery aging requires precise thermal\nmodels for charging power greater than 7 kW, ii) disregarding battery aging\nunderestimates EVs' operating costs by approx. 30%, and iii) the profitability\nof Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g.\nenergy arbitrage, depends on battery aging costs and the electricity price\nspread.\n