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A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA

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  • Zhang, Qiang
  • Ogren, Ryan M.
  • Kong, Song-Charng

Abstract

Efficient optimization algorithms are critical to the development of new engine technology. In this study, experimental investigations were carried out on optimizing the performance of a four-cylinder, turbocharged, direct-injection diesel engine running with soy biodiesel. An effective hybrid particle swarm optimization (PSO) and genetic algorithm (GA) method using a small population was developed and tested to optimize five operating parameters, including EGR rate, pilot timing, pilot ratio, main injection timing, and injection pressure. Based on the measured engine performance and emissions, results show that the new hybrid algorithm can significantly speed up the optimization process and achieve a superior optimum as compared to the basic GA method. The new hybrid PSO–GA method is expected to perform as an effective tool for rapid engine performance optimization.

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  • Zhang, Qiang & Ogren, Ryan M. & Kong, Song-Charng, 2016. "A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA," Applied Energy, Elsevier, vol. 165(C), pages 676-684.
    • Handle: RePEc:eee:appene:v:165:y:2016:i:c:p:676-684
    DOI: 10.1016/j.apenergy.2015.12.044
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