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Design and Simulation of an Intelligent Power Management Strategy and a Gearshifting Pattern for Parallel Hybrid Electric Vehicles

Sadeghian, Nasser | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 45599 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Saadat Foumani, Mahmoud; Salarieh, Hassan
  7. Abstract:
  8. A well-designed Control Strategy (CS) in Hybrid Electric Vehicles (HEVs), as the manager of energy flow, reduces the fuel consumption and/or air pollution, while keeps an eye for prolonging the components lifetime. In this regard, the present thesis provides a new CS for parallel HEVs, based on emotional learning. It is noteworthy that the controller consists of a Takagi-Sugeno-Kang (TSK) fuzzy system and a critic. The driver’s torque command, State of Charge (SOC) error, Internal Combustion Engine (ICE) brake torque error and its time derivative constitute the controller inputs, while the output is the ICE torque command. The critic produces two stress signals based on HEV’s battery and ICE operating points. These signals tune the TSK parameters online. The control strategy in critic follows the efficient ICE brake torque and also shifts the battery SOC in between two specific values. Simulation results indicate that the life-time of the battery significantly increases when comparisons are made with an online CS, named parallel electric assist control strategy. The results also reveal that the controller maintains the fuel consumption in three driving case studies, as well as the vehicle performance. Moreover, the robustness of the CS during electrical accessory loading has been investigated.
    Another study that is conducted in this thesis provides a fuzzy gearshifting strategy. The parameters of memebership functions of the controller outputs are optimized via particle swarm optimization algorithm. The optimization process is done with two different targets: fuel economy and vehicle performance. The results indicate that for better vehicle performance, the gearshifting should be done in higher engine speed. Also a well-designed CS can play more considerable role in fuel economy than a gearshifting strategy.
  9. Keywords:
  10. Control Strategy ; Neuro-Fuzzy Systems ; Hybrid Electric Vehicle (HEV) ; Particles Swarm Optimization (PSO) ; Gear Shifting ; Energy Management

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