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Hybrid electric vehicles employ a hybrid propulsion system to combine the energy efficiency of electric motor and a long driving range of internal combustion engine, thereby achieving a higher fuel economy as well as convenience compared with conventional ICE vehicles. However, the relatively complicated powertrain structures of HEVs necessitate an effective power management policy to determine the power split between ICE and EM. In this work, we propose a deep reinforcement learning framework of the HEV power management with the aim of improving fuel economy. The DRL technique is comprised of an offline deep neural network construction phase and an online deep Q-learning phase. Unlike traditional reinforcement learning, DRL presents the capability of handling the high dimensional state and action space in the actual decision-making process, making it suitable for the HEV power management problem. Enabled by the DRL technique, the derived HEV power management policy is close to optimal, fully model-free, and independent of a prior knowledge of driving cycles. Simulation results based on actual vehicle setup over real-world and testing driving cycles demonstrate the effectiveness of the proposed framework on optimizing HEV fuel economy.
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Electric Vehicle Modeling: Advanced Torque Split Analysis across Different Architectures
The proliferation of electric vehicles (EVs) is resulting in a big transition in the automotive industry, with the goal of reducing greenhouse gas emissions and improving energy efficiency. There are a variety of different architectural configurations and power distribution strategies that can be optimized for drivability performance, all-electric range, and overall efficiency. This paper describes the efforts of the research team in exploring different EV architectures to better understand their impacts on system performance in terms of energy efficiency and vehicle drivability. In search for an ideal powertrain architecture for a shared-use EV, the research team conducted a comprehensive analysis of a various EV architectures (including RWD and AWD) with different motor parameters, considering a spectrum of targeted vehicle technology specifications such as acceleration and braking performance, and fuel economy. To quantify these performance indices, a model-based design approach was utilized, leveraging the EV development tools developed by MATLAB/Simulink and Simscape. Standard driving cycles, e.g., Highway Fuel Economy Driving Schedule (HWFET) and Urban Dynamometer Driving Schedule (UDDS) were utilized to evaluate different EV powertrain architectures and rear/front wheel power splits. The simulation results showed that for the architectures (with respective parameters) investigated in this study, the AWD architectures have higher energy efficiency than the RWD architecture in the range of 5.4 – 37.9%. To further scrutinize performance across a wide spectrum of driving scenarios, we introduced a specialized modal driving profile. This comprehensive profile encompasses a diverse array of modal events, including varying acceleration rates and steady-state speeds, among others. In our analysis, we found that a standard torque split of 50/50 keeps a good balance between energy efficiency and drivability for our target AWD architecture.
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- Award ID(s):
- 2152258
- PAR ID:
- 10510635
- Publisher / Repository:
- Society of Automotive Engineers
- Date Published:
- Format(s):
- Medium: X
- Location:
- Detroit, Michigan, United States
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.4271/2024-01-2166
