Browse Publications Technical Papers 2019-01-0461

Energy-Oriented Torque Allocation Strategy Design of 4WID Electric Vehicle Using Slope Information 2019-01-0461

The paper proposes an energy-oriented torque allocation strategy to reduce the energy consumption of four-wheel independent driving (4WID) electric vehicles (EVs). To compute its energy efficiency accurately, the measured efficiency map is fitted by the cubic spline interpolation method. The energy-oriented torque allocation strategy is designed by minimizing the energy consumption during vehicle driving and braking. According to the varying requirement of torque and speed, the torque of front and rear axle cab be allocated dynamically by adjusting the torque allocation coefficients. To ensure the high efficiency of motors and reduce the energy consumption caused by current shock, the torque allocation coefficients are obtained from two kinds of sub strategies. A fuzzy logic controller is designed to combine the derived torque allocation coefficient above, by adopting Mamdani structure with 2 inputs and 1 output. It is validated by simulating under driving cycles and the roads with up-down slopes. Simulation results of driving cycles show that the proposed strategy can dynamically distribute the front and rear axle torque at different velocities, reducing the energy consumption compared to the strategy with average torque distribution. Using the road elevation information and driving cycles, the energy-oriented torque allocation strategy can reduce the vehicle energy consumption by 4% compared to the strategy with average torque distribution.


Subscribers can view annotate, and download all of SAE's content. Learn More »


Members save up to 18% off list price.
Login to see discount.
Special Offer: Download multiple Technical Papers each year? TechSelect is a cost-effective subscription option to select and download 12-100 full-text Technical Papers per year. Find more information here.
We also recommend:

On-Track Measurement of Road Load Changes in Two Close-Following Vehicles: Methods and Results


View Details


A Dual-Use Enterprise Context for Vehicle Design and Technology Valuation


View Details


Using Machine Learning to Guide Simulations Over Unique Samples from Trip Profiles


View Details