Minimum-time manoeuvring in electric vehicles with four wheel-individual-motors

Abstract

The coordinated control of vehicle actuators is gaining more and more importance as new platforms are becoming available, with chassis endowed with many different actuators that may help controlling the vehicle motion. Furthermore, wheel individual motors allow using a single system to apply both positive and negative torques at the wheels, which can be actuated independently one from the other. In electric vehicles (EVs), moreover, such a freedom in the actuation mechanisms opens the way to the combined optimisation of performance and energy consumption issues. In this paper, the problem of minimum-time manoeuvring in EVs is addressed, and the proposed strategy is compared against a benchmark, a-causal optimal solution showing that only a negligible loss of performance is experienced.

Comparison of Path Tracking and Torque-Vectoring Controllers for Autonomous Electric Vehicles

“…TV controllers (TVCs) are easily implementable in electric vehicles (EVs) with individual wheel motors, since these solutions allow precise wheel torque controllability, usually with higher bandwidth than the conventional friction brakes and internal combustion engine drivetrains. In human driven EVs, TVCs can enhance the cornering response, e.g., by shaping the understeer characteristic, increasing agility and ensuring stability in extreme transients [16]- [23]. TVCs are usually implemented as yaw rate feedforward / feedback controllers, with the option of sideslip contributions.…”

Effect of handling characteristics on minimum time cornering with torque vectoring

“…This change of coordinate reference frame, from vehicle-centred to road-centred 'curvilinear coordinates', is made to ensure affine road boundary constraints. Thus the dynamic system in (13) becomes: The OCP now seeks to find the control vector sequence to minimise the cost function:…”

“…TV systems typically consist of a yaw rate reference, a feedback controller that outputs the required yaw moment (some systems include feedforward elements [9]) converted to individual wheel torque demands by the Control Allocator (CA). Advanced techniques using mathematical analysis and simulation tools have been used to optimise both controller [10][11][12] and CA performance [9,12,13]. In particular, de Castro et.…”

“…al. [13] developed a realisable causal feedforward CA scheme, considering an electric vehicle with four independent electric motors, seeking to minimise time to navigate a U-turn bend. They achieved this by using nonlinear optimal control techniques, which permitted both the ability to gain insight into optimal controls for distribution of torques but also the realistic emulation of a racing driver.…”

“…They achieved this by using nonlinear optimal control techniques, which permitted both the ability to gain insight into optimal controls for distribution of torques but also the realistic emulation of a racing driver. Numerous other vehicle dynamics studies using the optimal control technique to manoeuvre vehicles at the performance limit has permitted a realistic emulation of both circuit racing drivers [14][15][16][17][18], and rally trailbraking [19,20]; EV-specific topologies were considered in [13,21,22]. The nonlinear optimal control solution gives an ideal driver behaviour (no mistakes, friction-limit operation, full preview of future conditions)-which is not possible with a causal driver model, racing-line following [16], or model predictive control.…”