In the first half of 2021, 2.65 million new battery electric vehicles and plug-in hybrid electric vehicles were sold worldwide. As a result, the market share of these vehicles in total sales of all passenger cars rises accordingly from 3% to 6.3%. According to the international energy agency (IEA), electric vehicles will have a market share of around 30% by 2030, resulting in a total number of 34 million electric vehicles on the road. These figures show that electromobility, in all its forms, is gaining momentum in the global automotive industry. In addition to the factors that significantly favor the electrification of road vehicles, there are some technological barriers to electromobility. In this context, this work deals with an important core component of electric vehicles – which poses major challenges to engineers and development processes – the battery. In addition to performance and efficiency, service life is one of the most frequently asked questions in terms of electric vehicles. To be able to answer this question, all the factors that influence the service life of batteries should be considered. One of the most important, of course, is the way vehicles are used (e.g. charging and discharging behavior) and thus the batteries in vehicles. Part of this work is the analyzation of realistic user profiles gathered from different real-data sources. These profiles are composed of driving and charging profiles and are intended to be used for different customer groups (e.g. private use, professional use, gender, age, geographical area, etc.). In addition, load and mission profiles for electric vehicles are analyzed, designed and created using proper data records. The base of the mission profiles consists of various significant factors: vehicle requirements, customer driving, driving cycles, charging distributions, location thermal profiles, and end-customer daily routines. The presented work deals with a novel approach to support and facilitate the design and development of batteries, by automating the process of collecting the aforementioned factors and applying the tool developed in the course of the approach in automotive development processes.
Graz University of Technology: Alexander Kreis, Muamer Majetic