Control and state estimation for energy recuperation in fully electric vehicles

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Control and state estimation for energy recuperation in fully electric vehicles

Falcone, Paolo / Lidberg, Mathias / Olafsdottir, Jona Marin / Jansen, Sven / Iersel, Sven van

In fully electric vehicles, the drive train motors can have a great benefit for vehicle dynamics control, since they can be simultaneously used as precise torque sensors and actuators. For instance, drive motors at each wheel can be used to estimate individual tyre forces, thus achieving more precise vehicle state and parameters estimation. Moreover, with motors at each wheel it is possible to command individual driving and braking torques, which is expected to enhance the vehicle stability and handling capabilities. Energy recuperation in fully electric vehicles is mainly limited by the requirement to preserve vehicle stability but it is also dependent on the brake system design and the ability of the control system. The boundaries of vehicle stability are difficult to assess, and must be approached with care, e.g. when maximizing the regenerative braking during cornering on slippery surfaces. Model based predictive control (MPC) is used in this paper to account for the main vehicle dynamics behaviour and to optimize the regenerative braking. A state estimator is also used to estimate the states fed back to the controller and other vehicle and road parameters used in the MPC problem formulation. The state estimation is capable of predicting road friction and accurately provides the current driving condition to the controller also under challenging braking conditions such as mu-split braking. The MPC controller shows a clear compromise between vehicle stability and the energy recuperation potential. The developed MPC concept together with the state estimator is a versatile method that can adapt to various brake designs and tuning objectives. The proposed setup is a good starting point for implementation on an actual vehicle.