In this paper, a control-oriented Linear Parameter Varying (LPV) model of an energy efficient electric vehicle is proposed, designed for model-based control to minimize energy consumption. The control inputs of the model include the torque reference and the actual cornering radius. The LPV model assesses the impact of cornering on driving resistances and, consequently, on energy consumption, which represents a novel approach. Due to the driving characteristics and the model nonlinear dynamics of the vehicle, a velocity-linearization based method was applied to obtain the parameter-dependent form. The obtained LPV model was then validated by using logged driving data, showing a root mean square error (RMSE) of 0.4682 m/s compared to the measured speed profile, thereby confirming the model's accuracy. The proposed LPV model can be utilized to develop energy-efficient driving strategies, making it highly relevant for the design and operation of energy-efficient vehicles.

This paper introduces a control-oriented Linear Parameter Varying (LPV) model of an energy-efficient electric vehicle, enhanced to account for wind-induced disturbances. The proposed model structure is designed to support model-based control strategies focused on minimizing energy consumption. In addition to core control inputs—such as torque reference and cornering radius—the model integrates a simulated representation of wind effects on the vehicle’s longitudinal dynamics. To manage the underlying nonlinearities of the vehicle dynamics, a trajectory-based linearization approach was employed to construct the baseline LPV model without wind effects. The accuracy of the extended model was validated using real-world speed profile data. Owing to its modular and control-compatible design, the model provides a solid foundation for testing and developing energy-saving control strategies, making it especially applicable to the design and operation of energy-efficient electric vehicles. The proposed model holds significant potential for further reducing energy consumption, particularly in urban transportation scenarios.

This paper presents the design and evaluation of a Linear Time-Varying Linear Quadratic Gaussian (LTV-LQG) controller for an energy efficient electric vehicle, using a predetermined driving strategy as the reference trajectory. The proposed approach begins with the development of a structured nonlinear vehicle model based on relevant subsystems, enabling accurate energy consumption estimation with a deviation of less than 2% from experimental measurements. This model serves as the basis for computing a near-optimal driving trajectory. The nonlinear model is linearized along the predefined trajectory to support control design. A time-varying control structure is then developed, integrating a Kalman filter that estimates unmeasured external disturbances, such as wind, and enhances feedback performance. The proposed control strategy is evaluated through simulations and compared to a rule-based switching controller that replicates human-like driving behavior. The simulation results demonstrate that the LTV-LQG controller consistently satisfies the time constraints in both headwind- and tailwind-dominant scenarios, where the switching controller tends to exceed the time limit. Moreover, in tailwind-dominant cases, the LTV-LQG controller achieves lower energy consumption (up to 15.4%). The proposed framework represents a computationally efficient and practically feasible control solution for electric vehicles operating under realistic disturbance conditions.