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Experimental and Computational Optimization of Cold-Start Performance in 4DTNA Series Diesel Engines Under Variable Environmental Conditions
Abstract
This study aimed to optimize the cold-start performance of 4DTNA series diesel engines at low ambient temperatures through experimental testing and computational modelling. A total of 77 valid cold-start experiments were conducted using a specially designed test bench and processed with the Approximation_LSM software to develop a six-factor second-degree polynomial regression model. Key independent variables included fuel supply, fuel injection advance angle, equivalent cold-start temperature, crankshaft speed, glow plug preheating time, and glow plug tip temperature. The results indicated that optimal adjustment of fuel cycle parameters and glow plug heating significantly reduced cold-start time and exhaust smoke emissions. Simulation of cold engine oil resistance using an external inertial load proved accurate without requiring climate chambers. The validated regression model can be integrated into electronic control unit (ECU) algorithms for adaptive parameter adjustment, improving engine reliability and environmental performance in harsh conditions. Future work should assess the effects of alternative fuels and additives.
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DOI: https://doi.org/10.17509/ajse.v5i3.89104
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