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Design and Optimization of a Mild Hybrid Electric Vehicle with Energy-Efficient Longitudinal Control
ISSN: 2691-3747, e-ISSN: 2691-3755
Published February 17, 2021 by SAE International in United States
Citation: Taoudi, A., Haque, M., Luo, C., Strzelec, A. et al., "Design and Optimization of a Mild Hybrid Electric Vehicle with Energy-Efficient Longitudinal Control," SAE Int. J. Elec. Veh. 10(1):55-78, 2021, https://doi.org/10.4271/14-10-01-0005.
The development, design, and modeling of a new hybrid electric vehicle (HEV) and its optimal control strategies to improve energy consumption have far-reaching impact on the environment, economy, and overall society. The EcoCAR Mobility Challenge (ECMC) focuses on the development of a mild HEV (mHEV) with connected and automated features for use in a car-sharing fleet. The Mississippi State University (MSU) EcoCAR team developed an analytical model of an HEV to meet two objectives: (1) estimate an initial design space that can meet Vehicle Technical Specifications (VTS) desired by the target market and (2) meet the competition requirements, both technical and nontechnical. The initial design space was narrowed down to three mild through-the-road hybrids with a rear axle motor (P4) based on our analysis. A higher fidelity model of the three architectures was then developed and validated with experimental data collected by the team and publicly available data from the United States (US) Environmental Protection Agency (EPA). A selection matrix was then used to choose the architecture that guarantees sustainable success over the competition. To further capitalize on the electrification of the powertrain, the MSU team has designed a longitudinal controller based on a finite-horizon model predictive controller (MPC) to maximize fuel economy and offer a higher degree of safety and driver comfort. A particle swarm optimization (PSO) algorithm is adopted to parameterize the longitudinal controller for maximizing the fuel economy over a city drive cycle. The longitudinal controller significantly improves the drive quality and safety as compared to a proportional-integral-derivative (PID)-based controller. The mHEV architecture provides a 34% improvement in fuel economy over the original stock vehicle in a weighted city and highway drive cycles’ combination, while an additional 9.8% improvement is demonstrated when the longitudinal controller is used in a city drive cycle.