Genetic algorithm with small population size for search feasible control parameters for parallel hybrid electric vehicles

Yu-Huei Cheng; Ching-Ming Lai; Jiashen Teh; Yu-Huei Cheng; Ching-Ming Lai; Jiashen Teh

doi:10.3934/energy.2017.6.930

AIMS Energy

2017, Volume 5, Issue 6: 930-943. doi: 10.3934/energy.2017.6.930

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Research article Topical Sections

Genetic algorithm with small population size for search feasible control parameters for parallel hybrid electric vehicles

1.
Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung, Taiwan
2.
Department of Vehicle Engineering, National Taipei University of Technology, Taipei, Taiwan
3.
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia

Received: 19 September 2017 Accepted: 16 November 2017 Published: 22 November 2017

The control strategy is a major unit in hybrid electric vehicles (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 electric assist control strategy (EACS) is used as the fundamental control strategy of parallel HEVs. The dynamic performance requirements stipulated in the Partnership for a New Generation of Vehicles (PNGV) is considered to maintain the vehicle performance. The known ADvanced VehIcle SimulatOR (ADVISOR) is used to simulate a specific parallel HEV with urban dynamometer driving schedule (UDDS). Five population sets with size 5, 10, 15, 20, and 25 are used in the GA. The experimental results show that the GA with population size of 25 is the best for selecting feasible control parameters in parallel HEVs.
- control strategy,
- electric assist control strategy (EACS),
- genetic algorithm (GA),
- hybrid electric vehicle (HEV)
Citation: Yu-Huei Cheng, Ching-Ming Lai, Jiashen Teh. Genetic algorithm with small population size for search feasible control parameters for parallel hybrid electric vehicles[J]. AIMS Energy, 2017, 5(6): 930-943. doi: 10.3934/energy.2017.6.930

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Abstract

The control strategy is a major unit in hybrid electric vehicles (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 electric assist control strategy (EACS) is used as the fundamental control strategy of parallel HEVs. The dynamic performance requirements stipulated in the Partnership for a New Generation of Vehicles (PNGV) is considered to maintain the vehicle performance. The known ADvanced VehIcle SimulatOR (ADVISOR) is used to simulate a specific parallel HEV with urban dynamometer driving schedule (UDDS). Five population sets with size 5, 10, 15, 20, and 25 are used in the GA. The experimental results show that the GA with population size of 25 is the best for selecting feasible control parameters in parallel HEVs.

References

[1]	Baumann BM, Washington G, Glenn BC, et al. (2000) Mechatronic design and control of hybrid electric vehicles. IEEE-ASME T Mech 5: 58–72. doi: 10.1109/3516.828590
[2]	Lin CC, Peng H, Grizzle JW, et al. (2003) Power management strategy for a parallel hybrid electric truck. IEEE T Contr Syst T 11: 839–849. doi: 10.1109/TCST.2003.815606
[3]	Pisu P, Rizzoni G (2007) A comparative study of supervisory control strategies for hybrid electric vehicles. IEEE T Contr Syst T 15: 506–518. doi: 10.1109/TCST.2007.894649
[4]	Salmasi FR (2007) Control strategies for hybrid electric vehicles: evolution, classification, comparison, and future trends. IEEE T Veh Technol 56: 2393–2404. doi: 10.1109/TVT.2007.899933
[5]	Yu H, Kuang M, McGee R (2014) Trip-oriented energy management control strategy for plug-in hybrid electric vehicles. IEEE T Contr Syst T 22: 1323–1336. doi: 10.1109/TCST.2013.2278684
[6]	Panday A, Bansal HO (2016) Energy management strategy implementation for hybrid electric vehicles using genetic algorithm tuned pontryagin's minimum principle controller. Int J Veh Technol 2016: 1–13.
[7]	Cheng YH (2014) Computational intelligence-based polymerase chain reaction primer selection based on a novel teaching-learning-based optimisation. Iet Nanobiotechnol 8: 238–246. doi: 10.1049/iet-nbt.2013.0055
[8]	Cheng YH (2015) Estimation of teaching-learning-based optimization primer design using regression analysis for different melting temperature calculations. IEEE T Nanobiosci 14: 3–12.
[9]	Chuang LY, Cheng YH, Yang CH (2015) PCR-CTPP design for enzyme-free SNP genotyping using memetic algorithm. IEEE T Nanobiosci 14: 13–23. doi: 10.1109/TNB.2015.2392782
[10]	Cheng YH (2016) A novel teaching-learning-based optimization for improved mutagenic primer design in mismatch PCR-RFLP SNP genotyping. IEEE/ACM T Comput Bi 13: 86–98.
[11]	Cheng YH, Kuo CN, Lai CM (2016) Effective natural PCR-RFLP primer design for SNP genotyping using teaching-learning-based optimization with elite strategy. IEEE T Nanobiosci 15: 657–665. doi: 10.1109/TNB.2016.2597867
[12]	Cheng YH, Kuo CN, Lai CM (2016) An improved evolutionary method with test in different crossover rates for PCR-RFLP SNP genotyping primer design. Int J Min Metall Mech Eng 4: 25–29.
[13]	Xue B, Zhang M, Browne WN, et al. (2016) A survey on evolutionary computation approaches to feature selection. IEEE T Evolut Comput 20: 606–626. doi: 10.1109/TEVC.2015.2504420
[14]	Zhang X, Tian Y, Cheng R, et al. (2015) An efficient approach to nondominated sorting for evolutionary multiobjective optimization. IEEE T Evolut Comput 19: 201–213. doi: 10.1109/TEVC.2014.2308305
[15]	Paulinas M, Ušinskas A (2015) A survey of genetic algorithms applications for image enhancement and segmentation. Inf Technol Control 36: 278–284.
[16]	Roberge V, Tarbouchi M, Labonté G (2013) Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE T Ind Inform 9: 132–141. doi: 10.1109/TII.2012.2198665
[17]	Yoon Y, Kim YH (2013) An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE T Cybernetics 43: 1473–1483. doi: 10.1109/TCYB.2013.2250955
[18]	Biserni C, Dalpiaz F, Fagundes T, et al. (2017) Geometric optimization of morphing fins coupled with a semicircular heat generating body: a numerical investigation on the basis of Bejan's theory. Int Commun Heat Mass 86: 81–91. doi: 10.1016/j.icheatmasstransfer.2017.05.006
[19]	Biserni C, Dalpiaz F, Fagundes T, et al. (2017) Constructal design of T-shaped morphing fins coupled with a trapezoidal basement: a numerical investigation by means of exhaustive search and genetic algorithm. Int J Heat Mass Tran 109: 73–81. doi: 10.1016/j.ijheatmasstransfer.2017.01.033
[20]	Montazeri-Gh M, Poursamad A, Ghalichi B (2006) Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles. J Franklin I 343: 420–435. doi: 10.1016/j.jfranklin.2006.02.015
[21]	Moore TC, Lovins AB (1995) Vehicle design strategies to meet and exceed PNGV goals. SAE Technical Paper.
[22]	Wu J, Zhang CH, Cui NX (2008) PSO algorithm-based parameter optimization for HEV powertrain and its control strategy. Int J Automot Techn 9: 53–59. doi: 10.1007/s12239-008-0007-8
[23]	Long V, Nhan N (2012) Bees-algorithm-based optimization of component size and control strategy parameters for parallel hybrid electric vehicles. Int J Automot Techn 13: 1177–1183. doi: 10.1007/s12239-012-0121-5
[24]	Cheng YH, Lai CM (2017) Control strategy optimization for parallel hybrid electric vehicles using a memetic algorithm. Energies 10: 305–325. doi: 10.3390/en10030305
[25]	Johnson VH, Wipke KB, Rausen DJ (2000) HEV control strategy for real-time optimization of fuel economy and emissions. SAE transactions 109: 1677–1690.
[26]	Wikipedia, Partnership for a New Generation of Vehicles. Available from: https://en.wikipedia.org/wiki/Partnership_for_a_New_Generation_of_Vehicles.

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