TY - INPR A1 - Köster, Dominik A1 - Porkert, Florian A1 - Volbert, Klaus T1 - Multi-Day Scheduling for Electric Vehicle Routing: A Novel Model and Comparison Of Metaheuristics N2 - The increasing use of electric vehicles (EVs) requires efficient route planning solutions that take into account the limited range of EVs and the associated charging times, as well as the different types of charging stations. In this work, we model and solve an electric vehicle routing problem (EVRP) designed for a cross-platform navigation system for individual transport. The aim is to provide users with an efficient route for their daily appointments and to reduce possible inconveniences caused by charging their EV. Based on these assumptions, we propose a multi-day model in the form of a mixed integer programming (MIP) problem that takes into account the vehicle's battery capacity and the time windows of user's appointments. The model is solved using various established metaheuristics, including tabu search (TS), adaptive large neighborhood search (ALNS), and ant colony optimization (ACO). Furthermore, the performance of the individual approaches is analyzed using generated ensembles to estimate their behavior in reality and is compared with the exact results of the Google OR-Tools solver. 6 pages, 5 figures KW - Charging stations KW - Electric vehicles KW - Metaheuristics KW - Smart cities KW - Vehicle routing Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2512.21346 ER - TY - CHAP A1 - Schmidt, Hans-Peter A1 - Fuchs, Andreas A1 - Fuchs, Stefan T1 - Simultaneous contact less charging of multiple electric vehicles: Contact less power and data transfer for multiple distributed loads T2 - 2014 4th International Electric Drives Production Conference ( 30 Sept.-1 Oct. 2014, Nuremberg, Germany N2 - A design of a 3-phase inductive power and data transfer for multiple stationary loads is proposed. It focuses on stable load voltages while minimizing electromagnetic interference. Loads are contactless charging units of electric vehicles at car parks. Physical properties of the inductive couplers are modelled via 3-D FEM and lumped parameters are used for system studies. Design principles are verified at laboratory scale with a purpose built test stand. KW - charging infrastructure KW - Coils KW - contact less charging KW - Couplers KW - Couplings KW - Data transfer KW - Electric vehicles KW - electro mobility KW - Feeds KW - multiple stationary loads KW - Windings Y1 - 2014 U6 - https://doi.org/10.1109/EDPC.2014.6984435 SP - 1 EP - 3 PB - IEEE ER - TY - CHAP A1 - Reindl, Andrea A1 - Meier, Hans A1 - Niemetz, Michael A1 - Park, Sangyoung T1 - Decentralized Battery Management System with Customized Hardware Components T2 - IEEE 19th Student Conference on Research and Development (SCOReD), Sustainable Engineering and Technology towards Industry Revolution: 23-25 Nov. 2021, Kota Kinabalu, Malaysia N2 - With an increasing number of sold electric vehicles (EVs), a large number of used batteries will be at disposal. How to deal with these resources is one of the major challenges in reducing the environmental impact of batteries throughout their entire life cycle. Heterogeneous cell parameters due to the different usage histories are a challenge for second-life use. The effort and cost of remanufacturing required to test and assemble a new battery pack is a further concern. Systems that combine battery packs/modules without full reassembly offer advantages such as cost and reusability. A decentralized battery management system (DBMS) provides a suitable architecture for such systems involving different types of batteries. In this paper, an architecture for a decentralized, battery state-dependent control is shown. The proposed DBMS supports various types of batteries, is scalable and flexibly adaptable for a wide range of applications. Despite the significant advantages, there are increased requirements to meet for the hardware implementation and the applied control strategies. Therefore, the necessary hardware components and their requirements are described and the hardware implementations are provided. KW - battery fitness KW - Battery management system KW - Battery management systems KW - bidirectional power flow KW - Computer architecture KW - Costs KW - DC-DC power converters KW - decentralized control KW - Electric vehicles KW - Hardware KW - load sharing KW - Microprocessors KW - Renewable energy sources KW - second life battery Y1 - 2021 U6 - https://doi.org/10.1109/SCOReD53546.2021.9652737 SP - 350 EP - 355 PB - IEEE ER - TY - JOUR A1 - Trost, Tobias A1 - Sterner, Michael A1 - Bruckner, Thomas T1 - Impact of electric vehicles and synthetic gaseous fuels on final energy consumption and carbon dioxide emissions in Germany based on long-term vehicle fleet modelling JF - Energy N2 - Based on a prospective scenario analysis, possible vehicle fleet developments for the individual motor car traffic (vehicle categories N1 and M1) are investigated for Germany in order to determine the long-term vehicle fleet structure, final energy demand, and related carbon dioxide emissions until the year 2050. In this framework, a vehicle fleet model was developed which combines a bottom-up consumer demand model with a dynamic stock-flow approach. Special emphasis is thereby given to different electric power-trains and synthetic gaseous fuels based on the power-to-gas technology. In detail, two different main scenarios are developed and, in addition, the impact of different carbon dioxide taxation levels of fossil fuels on the vehicle fleet structure are analysed. The scenario results reveal a broad range of possible future vehicle fleet structures. In the short to medium timeframe, the internal combustion engine dominates the fleet as a result of efficiency improvements and an increased use of natural gas as automotive fuel. The development of electric power-trains is initially marked by hybrid vehicles, whereas battery electric vehicles dominate the fleet structure in the long-term. Under favourable conditions, also synthetic gaseous fuels are competitive which can reduce carbon dioxide emissions even further. KW - Carbon dioxide emissions KW - Electric vehicles KW - Final energy consumption KW - Power-to-gas KW - Total cost of ownership KW - Vehicle fleet modelling Y1 - 2017 U6 - https://doi.org/10.1016/j.energy.2017.10.006 SN - 0360-5442 VL - 141 SP - 1215 EP - 1225 PB - Elsevier ER -