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Tyres are the primary contact between the vehicle and the road. It serves as the medium of communication between the road and the rider, which it does in terms of road loads and displacements. Therefore, measurement of dynamic wheel forces experienced by a two-wheeler is crucial for tuning the ride and handling characteristics of the vehicle. Currently, there are standard wheel force transducers available in the market, which are extensively used in cars. However, mass of such a system is relatively high to be used on two-wheelers. Special wheels and adaptors increase the unsprung mass considerably, which changes the dynamics of the vehicle. Moreover, cost of such systems is exorbitantly high to be used for two-wheelers. This paper describes the development of an innovative and a highly versatile and low-cost alternative method for real-time measurement of dynamic wheel loads and moments of a two-wheeler when compared with the currently available systems in the market.

The importance of friction between tire and road for the dynamic behavior of road vehicles has been emphasized in many publications. Continuously updated knowledge of the friction potential and the friction demand can help to improve maneuverability and thereby safety of vehicles under slippery road conditions. An on line estimation method, based on combination of side force and self aligning torque, generated by the tire, is theoretically founded on a simple brush type tire model. The system is implemented in the front wheel suspension of a passenger car. To cope with the highly non-linear behavior of the wheel suspension and the actual tire, various static neural networks have been applied in the estimation procedure. Experiments have been carried out both in simulation using a full vehicle multi-body model and with an actual vehicle. Conclusions are drawn regarding the estimation principle, the application of neural networks and the implementation in a test vehicle.

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.

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.