Genetic-fuzzy HEV control strategy based on driving cycle recognition

A genetic-fuzzy HEV control strategy based on driving cycle recognition （DCR） was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was used for traffic condition recognition based on ten parameters of each driving cycle. The DCR was utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. A fuzzy logic controller （FLC） was designed to be intelligent to manage the engine to work in the vicinity of its optimal condition. The fuzzy membership function parameters were optimized using the genetic algorithm （GA） for each driving cycle. The result is that the DCR_ fuzzy controller can reduce the fuel consumption by 1. 9%, higher than only CYC _ HWFET optimized fuzzy （0.2%） or CYC _ WVUSUB optimized fuzzy （0.7%）. The DCR_ fuzzy method can get the better result than only optimizing one cycle on the complex real traffic conditions.

非等直径柱肋阵列的冷却特性

为优化柱肋阵列冷却效果,通过缩小柱肋阵列中局部柱肋直径得到3种非等直径柱肋阵列通道。采用瞬态液晶测试技术和数值模拟方法研究了其传热和流阻特性。结果表明:保持奇数排柱肋直径的大小,仅减小偶数排的直径,能在传热减弱不多的情况下,使通道的流动阻力大幅降低。适当调整奇/偶数排柱肋的直径,雷诺数为10 000~50 000,奇偶数排柱肋直径比为1.4的非等直径柱肋阵列通道,其平均传热系数仅减小了约6%,而阻力系数则减小了约21%。