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Intelligent Real-time Management of Energy Flows in a Connected Electric Vehicle

Rezaei Larijani, Morteza | 2023

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  1. Type of Document: Ph.D. Dissertation
  2. Language: English
  3. Document No: 57222 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Zolghadri, Mohammad Reza; Hedayati Kia, Shahin; Taghavipour, Amir; El Hajjaji, Ahmad
  7. Abstract:
  8. Energy management strategies (EMSs) play a crucial role in enhancing the performance of electric vehicles (EVs). This Ph.D. thesis focuses on developing an intelligent EMS (IESM) aimed at mitigating battery degradation by optimally splitting the load current within a hybrid energy storage system (HESS) that integrates batteries and supercapacitors (SCs) in a connected EV. The battery and SC packs are arranged in a semi-active topology utilizing a bi-directional DC-DC converter, which regulates the SC current through a model predictive control (MPC) supervisory controller. Initially, a straightforward approach is proposed for the real-time modeling of EV, including their primary electrical and mechanical components. To represent the Li-ion battery cell, a double RC equivalent circuit is employed, with model parameters dependent on the state-of-charge (SoC), and extracted using the electrochemical battery cell model in MapleSim commercial software. Subsequently, additional components such as SCs, DC-DC converter, inverter, electric motor, and mechanical transmission are incorporated into the model. The resulting EV model is compared with the EV model using CarSim commercial software. For optimization-based control, the state-space representation of the HESS is derived, which is linear and parameter-dependent. Hence, the IEMS is developed based on a linear parameter-varying model predictive control (LPV MPC) approach. This leads to a quadratic programming (QP) optimization problem with a cost function based on the square of the battery pack current and the squared error of the state-of-voltage (SoV) of the SC. Additionally, SoV control is based on the EV’s upcoming acceleration by adjusting a related weighting factor, thereby providing a better opportunity to extend the battery lifecycle. The proposed LPV-MPC-based IEMS and HESS models were evaluated and implemented in a real-time digital simulator (RTDS) using a dSPACE SCALEXIO under two standard drive cycles. The results are compared with those of five EMSs: LPV-MPC with fixed SoV control, LPV-MPC with speed-dependent SoV control, LTI-MPC, filter-based method, and rule-based approach. Compared with LPV-MPC with fixed SoV control, the proposed method demonstrates reductions of up to 18.82% in the battery current root-mean-square (RMS), 30.26% and 25.85% in discharge/charge peak current, 9.71% in ampere-hour throughput, 4.78% and 29.06% in capacity and energy loss, respectively
  9. Keywords:
  10. Energy Management ; Smart Energy Management ; Electric Motor Vehicles ; Model-Based Predictive Control ; Quadratic Programming ; Hybrid Energy Storage System ; Real Time Simulator

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