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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 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.