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Developing a New Model for Energy Management System of Electric Vehicles Considering Battery Aging

Dadjo Eshghi, Mahdyar | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57082 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Moeini, Moein
  7. Abstract:
  8. Today, hybrid electric vehicles are known as one of the approaches with high potential to reduce and control emissions and fossil fuel consumption in the transportation sector, so that it can be a bridge between conventional vehicles and fully electric vehicles. One of the main weaknesses and obstacles to the widespread use of these vehicles is the habit of drivers and the lack of full evolution of the technology in the battery and its management. The one of solutions to overcome this problem is structural optimization and identification of factors effecting battery health and design of energy management system considering battery health. In this thesis, an energy management system has been created by the dynamics of the hybrid electric vehicle, all its components, the relations between them, as well as the battery health model consisting of the effect of battery temperature, depth of discharge, charge and discharge rates and state of charge fluctuations. Then, this energy management system is optimized with the objective function of reducing costs related to fuel consumption and battery degradation using dynamic programming strategy, deep deterministic policy gradient and deep Q learning. The simulation results show that by using expert knowledge, meaning the performance curve of the internal combustion engine and the changes in the internal resistance of the battery and the voltage of it by state of charge changes, are simplifying the operation space. The convergence efficiency has been improved up to 70.6%, and reaching 93.8% average fuel consumption can be achieved in the deep deterministic policy gradient strategy compared to dynamic programming
  9. Keywords:
  10. Hybrid Electric Vehicle (HEV) ; Energy Management System ; Battery Health ; Dynamic Programming ; Deep Deterministic Policy Gradient ; Deep Q Learning

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