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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.

Rear underrun protection device is crucial for rear impact and rear under-running of the passenger vehicles to the heavy duty trucks. Rear underrun protection device design should obey the safety regulative rules and successfully pass several test conditions. The objective and scope of this paper is the constrained optimization of the design of a rear underrun protection device (RUPD) beam of heavy duty trucks for impact loading using correlated CAE and test methodologies. In order to minimize the design iteration phase of the heavy duty truck RUPD, an effective, real-life testing correlated, finite element model have been constructed via RADIOSS software. Later on, Pareto Optimization has been applied to the finite element model, by constructing designed experiments. The best solution has been selected in terms of cost, manufacturing and performance. Finally, real-life verification testing has been applied for the correlation of the optimum solution.