![]() ![]() Yoon Y, Kim YH (2013) An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. Roberge V, Tarbouchi M, Labonté G (2013) Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. Paulinas M, Ušinskas A (2015) A survey of genetic algorithms applications for image enhancement and segmentation. (2015) An efficient approach to nondominated sorting for evolutionary multiobjective optimization. ![]() (2016) A survey on evolutionary computation approaches to feature selection. doi: 10.1109/TNB.2016.2597867Ĭheng YH, Kuo CN, Lai CM (2016) An improved evolutionary method with test in different crossover rates for PCR-RFLP SNP genotyping primer design. IEEE/ACM T Comput Bi 13: 86–98.Ĭheng YH, Kuo CN, Lai CM (2016) Effective natural PCR-RFLP primer design for SNP genotyping using teaching-learning-based optimization with elite strategy. doi: 10.1109/TNB.2015.2392782Ĭheng YH (2016) A novel teaching-learning-based optimization for improved mutagenic primer design in mismatch PCR-RFLP SNP genotyping. IEEE T Nanobiosci 14: 3–12.Ĭhuang LY, Cheng YH, Yang CH (2015) PCR-CTPP design for enzyme-free SNP genotyping using memetic algorithm. doi: 10.1049/iet-nbt.2013.0055Ĭheng YH (2015) Estimation of teaching-learning-based optimization primer design using regression analysis for different melting temperature calculations. Int J Veh Technol 2016: 1–13.Ĭheng YH (2014) Computational intelligence-based polymerase chain reaction primer selection based on a novel teaching-learning-based optimisation. Panday A, Bansal HO (2016) Energy management strategy implementation for hybrid electric vehicles using genetic algorithm tuned pontryagin's minimum principle controller. Yu H, Kuang M, McGee R (2014) Trip-oriented energy management control strategy for plug-in hybrid electric vehicles. Salmasi FR (2007) Control strategies for hybrid electric vehicles: evolution, classification, comparison, and future trends. Pisu P, Rizzoni G (2007) A comparative study of supervisory control strategies for hybrid electric vehicles. (2003) Power management strategy for a parallel hybrid electric truck. (2000) Mechatronic design and control of hybrid electric vehicles. Genetic algorithm with small population size for search feasible control parameters for parallel hybrid electric vehicles. electric assist control strategy (EACS),Ĭitation: Yu-Huei Cheng, Ching-Ming Lai, Jiashen Teh.The experimental results show that the GA with population size of 25 is the best for selecting feasible control parameters in parallel HEVs. Five population sets with size 5, 10, 15, 20, and 25 are used in the GA. DIFFERENT CONFIGURATION OF PARALLEL HYBRID VEHICLE SIMULATORThe known ADvanced VehIcle SimulatOR (ADVISOR) is used to simulate a specific parallel HEV with urban dynamometer driving schedule (UDDS). The dynamic performance requirements stipulated in the Partnership for a New Generation of Vehicles (PNGV) is considered to maintain the vehicle performance. The electric assist control strategy (EACS) is used as the fundamental control strategy of parallel HEVs. In order to provide suitable control parameters for reducing fuel consumptions and engine emissions while maintaining vehicle performance requirements, the genetic algorithm (GA) with small population size is applied to search for feasible control parameters in parallel HEVs. The control strategy is a major unit in hybrid electric vehicles (HEVs). ![]()
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