Travail de fin d'études et stage[BR]- Travail de fin d'études : Modelling of Charging systems for electric vehicles and assessment of charging scenarios[BR]- Stage d'insertion professionnelle
Koujou, Karim
Promotor(s) : Duysinx, Pierre
Date of defense : 4-Sep-2023/5-Sep-2023 • Permalink : http://hdl.handle.net/2268.2/18385
Details
Title : | Travail de fin d'études et stage[BR]- Travail de fin d'études : Modelling of Charging systems for electric vehicles and assessment of charging scenarios[BR]- Stage d'insertion professionnelle |
Translated title : | [fr] Modélisation de systèmes de recharge pour les véhicules électriques et évaluation de scénarios de recharge |
Author : | Koujou, Karim |
Date of defense : | 4-Sep-2023/5-Sep-2023 |
Advisor(s) : | Duysinx, Pierre |
Committee's member(s) : | Nélis, Marc
Cornélusse, Bertrand Toussaint, Yves Vanderbeeken, Romain |
Language : | English |
Number of pages : | 95 |
Keywords : | [en] Electric vehicles [en] Charging [en] AC/DC [en] Battery Modelling [en] Power Loss |
Discipline(s) : | Engineering, computing & technology > Mechanical engineering |
Research unit : | Université de Liège |
Name of the research project : | Modelling of Charging Systems for Electric Vehicles and Assessment of Charging Scenarios |
Target public : | Researchers Professionals of domain Student General public |
Institution(s) : | Université de Liège, Liège, Belgique |
Degree: | Master en ingénieur civil mécanicien, à finalité spécialisée en technologies durables en automobile |
Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[en] This Master’s Thesis focuses on modeling, simulating and assessing various charging scenarios, charger types and methods for a Nissan Leaf equipped with different battery sizes. This research aims to determine charging losses and time needed to evaluate whether larger batteries are an effective solution to range anxiety. Conductive charging is influenced by factors such as converter inefficiency, connector and cable losses in addition to battery internal resistance. Whereas inductive charging is mainly affected by misalignment and air gap between the charger and EV, resonant circuit losses and coil and ferrite losses. Both charging methods are influenced by the battery's state of charge and health, ambient temperature, and varying charging powers.
Two different driving routines were simulated, a long trip and a daily commute to work. The long trip simulation highlighted the effects of using DC fast chargers and their main influence on battery degradation, results showed that the 100 kW DC Level 2 fast charger was more efficient than the 50 kW DC Level 1 fast charger. The urban commute simulation suggested an optimal charging strategy when the State of Charge drops between 40-60%, maximizing efficiency and battery life. Moreover, results indicated that the 22 kW AC Level 2 charger was the most efficient charger compared to other AC chargers. An overall simulation was conducted comparing the 40 kWh vs 62 kWh. The findings suggest that instead of investing in larger battery packs for EVs, investing in advanced charging infrastructures, battery enhancements against degradation and especially in wireless charging promises a more sustainable EV future.
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