2019/01/01 by Karl Schwenk, Tim Harr, Schwenk, Karl +9
Energy · Engineering · #Advanced Battery Technologies Research #Electric Vehicles and Infrastructure #Energy, Environment, and Transportation Policies #FOS: Computer and information sciences #FOS: Electrical engineering #Other Computer Science (cs.OH) #Signal Processing (eess.SP) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1910.07503
openalex publication_date 2019/10/14 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Electric Vehicle (EV) penetration and renewable energies enables synergies\nbetween energy supply, vehicle users, and the mobility sector. However, also\nnew issues arise for car manufacturers: During charging and discharging of EV\nbatteries a degradation (battery aging) occurs that correlates with a value\ndepreciation of the entire EV. As EV users' satisfaction depends on reliable\nand value-stable products, car manufacturers offer charging assistants for\nsimplified and sustainable EV usage by considering individual customer needs\nand battery aging. Hitherto models to quantify battery aging have limited\npracticability due to a complex execution. Data-driven methods hold feasible\nalternatives for SOH estimation. However, the existing approaches barely use\nuser-related data. By means of a linear and a neural network regression model,\nwe first estimate the energy consumption for driving considering individual\ndriving styles and environmental conditions. In following work, the consumption\nmodel trained on data from batteries without degradation can be used to\nestimate the energy consumption for EVs with aged batteries. A discrepancy\nbetween the estimation and the real consumption indicates a battery aging\ncaused by increased internal losses. We then target to evaluate the influence\nof charging strategies on battery degradation.\n