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This paper is on the design of a steer-by-wire system for a light commercial vehicle. A hardware-in-the-loop simulation test rig with the actual rack and pinion mechanism of the light commercial vehicle under study was built for this purpose. The steer-by-wire actuator can be placed on either the second pinion, the first pinion or both in the double pinion steering test system used. The hardware and geometry of the steering test rig are identical to the implementation of the steering system in the test vehicle. Unnecessary and expensive road testing is avoided with this approach as most problems are identified and solved in the hardware-in-the-loop simulation phase conducted in the laboratory where the steering subsystem and its controller exist as hardware and the rest of the vehicle exist as a software model running in real time. Hardware-in-the-loop simulation results show the effectiveness of the proposed controller design in tracking desired steering dynamics.

This paper presents the development of a transient active control (TABC) system for the Ford Transit Connect light commercial vehicle using semi active suspensions. The control objective is to improve the ride comfort and road holding together with achieving roll and pitch stability using four semi active suspension dampers, hence called transient active body control. Semi-active control algorithms such as sky-hook, ground-hook and hybrid are applied to each suspension while the roll and pitch stabilizing controllers are designed separately and interfere with the local semi-active controllers through a supervisory control algorithm, if necessary. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed technique.

There is an increasing trend in the worldwide automotive area towards developing hybrid electric vehicles as an intermediate solution to fulfill the new, more stringent pollutant emission level requirements set by governments. Conversion of braking energy into electrical energy stored in the battery through regenerative braking is an important aspect of hybrid electric vehicles that increases their fuel efficiency. This paper presents an electric regenerative power assisted brake algorithm developed to enhance energy efficiency of a front and rear wheel drive parallel hybrid electric commercial vehicle. The commercial vehicle used in this study is a second generation research prototype Ford Transit Parallel Hybrid Electric Van. The existing hydraulic brake system of this van was not altered for reasons of safety and reliability in the case of a problem with regenerative barking.

In a Hybrid Electric Vehicle (HEV), the main aim is to decrease the fuel consumption and emissions without significant loss of driving performance. Maximizing Overall Efficiency Strategy (MOES) algorithm, presented here, distributes the power demand among the available paths to the wheels to improve fuel economy. In MOES, the vehicle is considered as a system whose input and output are power capability of consumed fuel and actual power transferred to the road, respectively. The aim of the strategy is to maximize the overall efficiency of the vehicle determined as the ratio of output power to input power. The control algorithm and driver model were prepared within Simulink and used to drive the Carmaker model of the vehicle which is a Ford Transit hybrid electric research prototype van. Simulations were carried out in 3 modes of the vehicle; conventional mode, regenerative braking only mode and full MOES mode to analyze the role of optimization better.

In this paper, the optimum linear/nonlinear spring and linear/nonlinear damper force versus displacement and force versus velocity characteristic functions, respectively, are determined using simple lumped parameter models of a quarter car front independent suspension and a half car rear solid axle suspension of a light commercial vehicle. The complexity of a nonlinear function optimization problem is reduced by determining the shape a priori based on typical shapes supplied by the car manufacturer and then scaling it up or down in the optimization process. The vehicle ride and handling responses are investigated considering models of increased complexity. The linear and nonlinear optimized spring characteristics are first obtained using lower complexity lumped parameter models. The commercial vehicle dynamics software Carmaker is then used in the optimization as the higher complexity, more realistic model.

For manual transmissions, including the automated types, reduced shifting effort and easy of gear set engagements in a short period of time without rattles and shakes are major requirements for the shift quality evaluations. Performance of the synchronizer mechanisms depends highly on the design, material and arrangement of the transmission synchronization components; thus, the synchronization process is a mechanical and tribological process which is influenced by numerous design parameters of the synchronizers, constraints and properties of the lubricated contacts. In this study, a detailed multi-body-dynamics model for a HCV (Heavy Commercial Vehicle) transmission gearset is presented; various synchronization simulations are performed and the results are compared with the objective shift quality measurements. The developed model yields total synchronization and engagement time based on the applied gear shifting effort.

In an effort to support design and testing activities at product development lifecycle of the engine, proper duty cycle is required. However, to collect data and develop accurate duty cycles, there are not any vehicles equipped with prototype engines at customers. Therefore, in this paper, discrete duty cycle development methodology is studied to generate trailer truck engine usage profile which represents driving conditions in Turkey for engines in development phase. Cycles are generated using several vehicles equipped with prototype engines and professional drivers that can mimic customer usage. Methodology is based on defining real-world customer driving profile, discretizing real-world drives into separate events, collecting vehicle data from each discrete drive, determining the weight of events by conducting customer surveys and creating a representative reference usage profile with data analysis.