On the enhancement of vehicle handling and energy efficiency of electric vehicles with multiple motors: the iCOMPOSE project

Electric vehicles with multiple motors allow torque-vectoring, i.e., the individual control of each powertrain torque. Torque-vectoring (TV) can provide: i) enhancement of vehicle safety and handling, via the generation of a direct yaw moment to shape the understeer characteristics and increase yaw and sideslip damping; and ii) energy consumption reductions, via appropriate torque allocation to each motor. The FP7 European project iCOMPOSE thoroughly addressed i) and ii). Theoretical analyses were carried out to design state-of-the art TV controllers, which were validated through: a) vehicle simulations; and b) extensive experimental tests, which were performed at rolling road facilities and proving grounds, using a Range Rover Evoque prototype equipped with four identical on-board electric powertrains. This paper provides an overview of the TV-related contributions of iCOMPOSE

Modeling, simulation and control of a 4WD electric vehicle with in-wheel motors

A relatively new technology for the electric vehicles considers the use of brushless permanent magnet motors directly connected to the car wheels (in-wheel motors or hub motors). In order to evaluate the performance that can be obtained, a complete dynamic model of a four-wheel drive (4WD) electric vehicle equipped with four in-wheel motors is developed and a correspondent parametric simulator is implemented in Matlab/Simulink™. The simulator is also employed for designing, testing and comparing various control logics which reproduce the handling behavior of a real vehicle

A Fast and Parametric Torque Distribution Strategy for Four-Wheel-Drive Energy-Efficient Electric Vehicles

Electric vehicles (EVs) with four individually controlled drivetrains are over-actuated systems, and therefore, the total wheel torque and yaw moment demands can be realized through an infinite number of feasible wheel torque combinations. Hence, an energy-efficient torque distribution among the four drivetrains is crucial for reducing the drivetrain power losses and extending driving range. In this paper, the optimal torque distribution is formulated as the solution of a parametric optimization problem, depending on the vehicle speed. An analytical solution is provided for the case of equal drivetrains, under the experimentally confirmed hypothesis that the drivetrain power losses are strictly monotonically increasing with the torque demand. The easily implementable and computationally fast wheel torque distribution algorithm is validated by simulations and experiments on an EV demonstrator, along driving cycles and cornering maneuvers. The results show considerable energy savings compared to alternative torque distribution strategies

Torque Distribution Strategies for Energy-Efficient Electric Vehicles with Multiple Drivetrains

The paper discusses novel computationally efficient torque distribution strategies for electric vehicles with individually controlled drivetrains, aimed at minimizing the overall power losses while providing the required level of wheel torque and yaw moment. Analytical solutions of the torque control allocation problem are derived and effects of load transfers due to moderate driving/braking and cornering conditions are studied and discussed in detail. Influences of different drivetrain characteristics on the front and rear axles are described. The results of the analytically-derived algorithm are contrasted with those from two other control allocation strategies, based on the off-line numerical solution of more detailed formulations of the control allocation problem (i.e., a multi-parametric non-linear programming problem). The solutions of the control allocation problem are experimentally validated along multiple driving cycles and in steady-state cornering, on an electric vehicle with four identical drivetrains. The experiments show that the computationally efficient algorithms represent a very good compromise between low energy consumption and controller complexity