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Grey wolf algorithm for multidimensional engine optimization of converted plug-in hybrid electric vehicle

机译：改进的插电式混合动力汽车多维引擎优化的灰狼算法

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The paper presents the application of grey wolf algorithm for multidimensional engine optimization of converted parallel operated diesel plug-in hybrid electric vehicle to optimize specific fuel consumption (FC) and emissions. All emissions hydrocarbon (HC), carbon monoxide (CO), nitrogen oxide (NOx) and particulate matter (PM) are considered as optimization parameters. Offline engine maps of FC, HC, CO, NOx and PM are generated for 70 hp engine by data obtained from Oak Ridge National Laboratory for study. MATLAB program is used for simulation. A grey wolf coding is developed and tested extensively for various values of speed and torque. The optimization results obtained are verified by available engine maps. The optimization performance and its environmental impact are discussed in detail. It is observed that grey wolf optimizer (GWO) gives the global minimum value with slight deviation, although least computation time and simplicity makes this algorithm a potential candidate for real-time implementation.
机译：本文介绍了灰狼算法在转换并联操作柴油插电式混合动力汽车多维发动机优化中的应用，以优化比油耗（FC）和排放。所有排放的碳氢化合物（HC），一氧化碳（CO），氮氧化物（NOx）和颗粒物（PM）均被视为优化参数。根据从橡树岭国家实验室获得的数据，为70马力的发动机生成了FC，HC，CO，NOx和PM的离线发动机图。 MATLAB程序用于仿真。灰狼编码已针对各种速度和扭矩值进行了广泛开发和测试。获得的优化结果通过可用的发动机图进行验证。详细讨论了优化性能及其对环境的影响。可以看到，灰狼优化器（GWO）给出的全局最小值略有偏差，尽管最少的计算时间和简单性使该算法成为实时实现的潜在候选者。

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