4.7 Article

Real-Time Torque-Split Strategy for P0+P4 Mild Hybrid Vehicles With eAWD Capability

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TTE.2021.3103149

Keywords

Torque; Tires; Force; Axles; Wheels; Fuel economy; Mechanical power transmission; Approximate equivalent consumption minimization strategy; braking force distribution; hybrid electric vehicle (HEV)

Funding

  1. Hyundai-Kia America Technical Center, Inc.

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In this study, a real-time torque-split strategy for a 48-V P0+P4 mild hybrid electric vehicle (MHEV) is proposed. The strategy considers realistic operational constraints and is optimized using dynamic programming. Simulation results show that the proposed strategy achieves close to global optimality in terms of fuel economy.
The 48-V P0+P4 mild hybrid electric vehicles (MHEVs) have been drawing attention for their potential in fuel economy (FE) improvement and electric all-wheel-drive capability through a dual-motor system. This P0+P4 system, enabled by the addition of an extra P4 motor to an existing P0 MHEV, poses the new control problem of optimal torque-split among three components, namely, an internal combustion engine and P0/P4 motors. This study presents an effective real-time torque-split strategy for a 48-V P0+P4 MHEV. To include realistic operational constraints, we consider the longitudinal load transfer of the vehicle, nonlinear tire effects, and constraints on braking force distribution for vehicle safety. First, an optimal torque-split problem for the considered P0+P4 MHEV is formulated and solved with dynamic programming (DP). Then, based on the DP results, we propose a real-time-implementable torque-split strategy using an approximated adaptive equivalent consumption minimization strategy and a suboptimal braking force distribution strategy. The simulation results reveal that the proposed strategy can achieve about 99.1% and 97.7% of global optimality in terms of FE under training and validation driving cycles compared to the results by the DP. As a comparison, the rule-based strategy, as a benchmark, achieves 93.6% and 94.5% under the same drive cycles.

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