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This paper describes the development of an integrated simulation model for evaluating the effects of electrically heating the three-way catalyst (TWC) in a series hybrid electric vehicle (s-HEV) on fuel economy and exhaust gas purification performance. Engine and TWC models were developed in GT-Power to predict exhaust emissions during transient operation. These models were validated against data from vehicle tests using a chassis dynamometer and integrated into an s-HEV model built in MATLAB/Simulink. The s-HEV model accurately reproduced the performance characteristics of the vehicle’s engine, motor, generator, and battery during WLTC mode operation. It can thus be used to predict the fuel consumption, emissions, and performance of individual powertrain components. The engine combustion characteristics were reproduced with reasonable accuracy for the first 50 combustion cycles, representing the cold-start condition of the driving mode.

The application of biodiesel as an alternative fuel for petroleum diesel fuel is very effective for the reduction of CO₂ emission, because biodiesel is produced from renewable biomass resources. In Japan, neat biodiesel derived from waste cooking oil has often been applied to commercial vehicles. However, it is possible that the difference of fuel properties between conventional diesel fuel and biodiesel causes the problems on exhaust emission characteristics of diesel engine. Therefore, it is necessary to clarify the effect of biodiesel fuelling on exhaust emissions from commercial vehicles. Light-duty garbage trucks and heavy-duty diesel buses which were actually fueled with biodiesel in Kyoto, Japan, were used for test vehicles in this study. The exhaust emissions from these vehicles during JE05 mode tests were compared between biodiesel derived from waste cooking oil and conventional diesel fuel.

For a series hybrid electric vehicle (SHEV), the electric motor is responsible for driving the wheels, while the engine drives the only generator to provide electricity. SHEVs set a control strategy to make the engine run near the fixed operating point with high thermal efficiency, thereby effectively reducing fuel consumption. The powertrain system of HEV is more complex than that of a conventional drive system using only an internal combustion engine, and it is time-consuming to obtain the optimal components specification values and control parameters. Therefore, automatic optimization methods are required nowadays. We used Genetic Algorithm (GA) as the optimization method and optimize powertrain specifications and control parameter values to reduce fuel consumption. The results show that it is an effective optimization method.

A major issue of battery electric vehicles (BEV) is optimizing driving range and energy consumption. Under actual driving, transient thermal and electrical performance changes could deteriorate the battery cells and pack. These performances can be investigated and controlled efficiently with a thermal management system (TMS) via model-based development. A complete battery pack contains multiple cells, bricks, and modules with numerous coolant pipes and flow channels. However, such an early modeling stage requires detailed cell geometry and specifications to estimate the thermal and electrochemical energies of the cell, module, and pack. To capture the dynamic performance changes of the LIB pack under real driving cycles, the thermal energy flow between the pack and its TMS must be well predicted. This study presents a BTMS model development and validation method for a 75-kWh battery pack used in mass-production, mid-size battery SUV under WLTC.