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The two-wheeler market in India is currently dominated by internal combustion engine (ICE) technology, and this vehicle segment is not subject to fuel consumption standards. In this paper, we first assess the technology used in India’s existing two-wheeler fleet. We then estimate the technology potential for improving the fuel efficiency of ICE two-wheelers and the costs associated with doing so and compare the cost-effectiveness of ICE two-wheelers and electric two-wheelers in reducing overall fleet fuel consumption. Our analysis indicates that the ICE vehicles are cost effective till a 23% fuel consumption reduction in 2025. However, if higher fuel consumption reduction is required by 2025, then electric two-wheelers become cost effective. A fleet average of 25 gCO2/km in 2025 can be expected to result in nearly a third of new two-wheelers sold being electric.

In this study, the combustion system of a light-duty compression ignition engine running on a market gasoline fuel with Research Octane Number (RON) of 91 was optimized using computational fluid dynamics (CFD) and Machine Learning (ML). This work was focused on optimizing the piston bowl geometry at two compression ratios (CR) (17 and 18:1) and this exercise was carried out at full-load conditions (20 bar indicated mean effective pressure, IMEP). First, a limited manual piston design optimization was performed for CR 17:1, where a couple of pistons were designed and tested. Thereafter, a CFD design of experiments (DoE) optimization was performed where CAESES, a commercial software tool, was used to automatically perturb key bowl design parameters and CONVERGE software was utilized to perform the CFD simulations. At each compression ratio, 128 piston bowl designs were evaluated.

Fuel economy (FE) and greenhouse gas (GHG) emissions measured via chassis testing under laboratory conditions were never intended to represent the wide range of real-world driving conditions that are experienced during a vehicle's lifetime. Comprehensive real-world information is needed to better assess US FE label adjustments, determine off-cycle credits for FE standards, and forecast real-world driving behavior, fuel consumption, and CO2 emissions. This paper explores a cost effective method to collect in-use fuel consumption data using the on-board diagnostics (OBD) data stream in light-duty vehicles (LDVs). The accuracy of fuel consumption calculated from the OBD data was analyzed in two ways. First, fuel rates calculated from standard OBD Parameter IDs (PIDs) were compared with fuel rate estimates based on enhanced PID (OEM fuel injector fuel rate) data in two different vehicles.

To restrain the environmental and energy problems caused by oil consumption and improve fuel economy of heavy-duty commercial vehicles, China started developing relevant standards from 2008. This paper introduces the background and development of China's national standard “Fuel consumption test methods for heavy-duty commercial vehicles”, and mainly describes the test method schemes, driving cycle and weighting factors for calculating average fuel consumption of various vehicle categories. The standard applies to heavy-duty vehicles with the maximum design gross mass greater than 3500 kg, including semi-trailer tractors, common trucks, dump trucks, city buses and common buses. The standard adopts the C-WTVC driving cycle which is adjusted on the basis of the World Transient Vehicle Cycle[1, 2] and specifies weighting factors of urban, rural and motorway segments for different vehicle categories.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NOx within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions.