Fuel economy optimization of an Atkinson cycle engine using genetic algorithm
Fuel economy optimization of an Atkinson cycle engine using genetic algorithm
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DOI：
10.1016/j.apenergy.2012.12.061
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发表时间：
2013-05
期刊：
影响因子：
11.2
通讯作者：
Jinxing Zhao;Min Xu
中科院分区：
文献类型：
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作者：
Jinxing Zhao;Min Xu
基于人工神经网络方法[1]，设计了几何压缩比（GCR）为12.5的阿特金森循环发动机，以最大化全负荷工况的燃料经济性。然而，阿特金森循环发动机通常在部分负载条件下运行，特别是在中高负载范围内。部分负荷的燃油经济性优化对降低总燃油消耗量更为重要。阿特金森循环发动机采用进气门关闭正时和电子节流控制相结合的负荷控制策略，对燃油经济性有影响。此外，排气门开启正时、火花角和空燃比也会影响燃油经济性。如果通过实验在整个操作范围内校准这些操作变量，难度和成本将成为一个大问题。提出了一种基于物理模型的Atkinson循环发动机优化设计方案，将MATLAB遗传算法与Atkinson循环发动机一维GT-Power仿真模型相耦合。GT-Power模型经过改进，通过使用覆盖整个操作范围的各种速度-负载点的实验数据校准燃烧和传热子模型的参数，以准确模拟部分负载条件。基于部分负荷标定的GT-Power模型，采用遗传算法对燃油经济性进行了优化。在对每个速度-负荷点进行优化后，得到了IVC定时、SA等的控制图。然后将这些数值优化的控制映射输入发动机控制单元（ECU）作为发动机标定的初始值，并进一步进行实验优化。实验结果表明，部分负荷GT-Power模型具有足够的预测精度，最大误差为8.5%。经遗传算法优化后，在整个工作范围内，燃油经济性得到了较大的改善，最大改善幅度达到7.67%。
An Atkinson cycle engine with geometrical compression ratio (GCR) of 12.5 has been designed by maximizing fuel economy at full load operating conditions based on the Artificial Neural Network Method [1]. However, the Atkinson cycle engine generally operates at part load conditions especially in the middle to high load range. Optimization of the fuel economy for part load is more important in reducing the total fuel consumption. The Atkinson cycle engine applies the load control strategy that combines the intake valve closure (IVC) timing and electrically throttling control (ETC), which has an impact to the fuel economy. Moreover, the exhaust valve opening (EVO) timing, spark angle (SA) and air–fuel-ratio (AFR) also affect the fuel economy. If calibrating these operating variables over the entire operating range through experiments, the difficulty and cost will become a big issue. A physical model based optimization scheme by coupling MATLAB genetic algorithm (GA) and 1-D GT-Power simulation models of the Atkinson cycle engine are proposed. The GT-Power models were improved to accurately simulate the part load conditions, by calibrating parameters of the combustion and heat transfer sub-models using experimental data taken at various speed–load points covering the entire operating range. The fuel economy was optimized based on the part-load calibrated GT-Power models using the Genetic Algorithm. After each speed–load point was optimized, the control maps for the IVC timings, SA, etc. were obtained. Then these numerically optimized control maps were input into the engine control unit (ECU) as the initial values of the engine calibration, which were further experimentally optimized. The experimental results show that the part-load GT-Power models have sufficient prediction accuracy, with maximal error of 8.5%. After optimized by GA, the fuel economy was greatly improved over the operating range, with the maximal improvement up to 7.67%.