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Stability of motion, turnability, mobility and fuel consumption of all-wheel drive terrain vehicles strongly depends on engine power distribution among the front and rear driving axles and then between the left and right wheels of each axle. This paper considers kinematic discrepancy, which characterizes the difference of the theoretical velocities of the front and rear wheels, as the main factor that influences power distribution among the driving axles/wheels of vehicles with positively locked front and rear axles. The paper presents a new algorithm which enables minimization of the kinematic discrepancy factor for the improvement of AWD terrain vehicle dynamics while keeping up with minimal power losses for tire slip. Three control modes associated with gear ratio control of the front and rear driving axles are derived to provide the required change in kinematic discrepancy. Computer simulation results are presented for different scenarios of terrain and road conditions.

Anti-lock braking functions of electric vehicles with individual wheel drive can be effectively realized through the operation of in-wheel or on-board motors in the pure regenerative mode or in the blending mode with conventional electro-hydraulic anti-lock braking system (ABS). The regenerative ABS has an advantage in simultaneous improvement of active safety, energy efficiency, and driving comfort. In scope of this topic, the presented work introduces results of experimental investigations on a pure electric ABS installed on an electric powered sport utility vehicle (SUV) test platform with individual switch reluctance on-board electric motors transferring torque to the each wheel through the single-speed gearbox and half-shaft. The study presents test results of the vehicle braking on inhomogeneous low-friction surface for the case of ABS operation with front electric motors.

Active vehicle safety and driving assistance systems can be made more efficient, more robust and less complex if wheel load information would be available. Although this information could be determined via numerous different methods, due to various reasons, no commercially feasible approach has yet been introduced. In this paper the approach of bearing load estimation is topic of interest. Using the bearing for load measurement has considerable advantages making it commercially attractive as: i) it can be performed on a non-rotating part, ii) all wheel loads can be measured and iii) usually the bearing serves the entire lifetime of the vehicle. This paper proposes a novel approach for the determination of wheel loading. This new approach, based on the strain variance on the surface of the bearing outer ring, is tested on a dedicated bearing test setup.

This paper presents an innovative combined control using Model Predictive Control (MPC) to enhance the stability of automated vehicles. It integrates path tracking and vehicle stability control into a single controller to satisfy both objectives. The stability enhancement is achieved by computing two expected yaw rates based on the steering wheel angle and on lateral acceleration into the MPC model. The vehicle's stability is determined by comparing the two reference yaw rates to the actual one. Thus, the MPC controller prioritises path tracking or vehicle stability by actively varying the cost function weights depending on the vehicle states. Using two industrial standard manoeuvres, i.e. moose test and double lane change, we demonstrate a significant improvement in path tracking and vehicle stability of the proposed MPC over eight benchmark controllers in the high-fidelity simulation environment.