Konferensartikel

Optimization of process parameters in series hydraulic hybrid system through multi-objective function

Somashekhar S. Hiremath
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, INDIA

R. Ramakrishnan
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, INDIA

M. Singaperumal
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, INDIA

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp1392a20

Ingår i: 13th Scandinavian International Conference on Fluid Power; June 3-5; 2013; Linköping; Sweden

Linköping Electronic Conference Proceedings 92:20, s. 199-205

Visa mer +

Publicerad: 2013-09-09

ISBN: 978-91-7519-572-8

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

Rising demand; scarcity and lower production rate of crude oil has made fuel an unaffordable for a public passenger vehicle. In this scenario; an energy or fuel efficient system called hybrid system is the hardcore requirement of the automotive industry. The latest gasoline-electric hybrid and hydraulic hybrid systems are significantly more energy and fuel efficient than conventional vehicles. Strength of electric hybrids is its high energy density of electric batteries; allowing for large storage in relatively compact and lightweight batteries. Hydraulic hybrid is the potential technological solution for the limitations in the electric hybrid vehicle. Scalability of hydraulic hybrid system to larger; more powerful vehicles like garbage trucks; passenger bus and delivery trucks when compared to its counterparts. In this paper; a new configuration of the series hydraulic hybrid system has been proposed. The dynamic response of the system is studied using simulation results of the system model in AMESim tool. Sizing of key components in the system involves a parametric optimization with objective function as maximize system energy delivered. However; A trade-off prevails between the system energy consumed and energy delivered. Hence; the process parameters of the system are optimized through multi-objective function. The system simulation results after optimization apparently show that; optimal system parameters significantly improve energy efficiency

Nyckelord

Series hydraulic hybrid; Multi-objective function; Energy efficiency; Optimization

Referenser

[1] Allen E. Fuhs. Hybrid vehicles and the future of personal transportation ; CRC Press; 2009.

[2] Davis S C; Diegel S W. Transportation Energy Data book. Center for transportation analysis engineering science and technology division. 24th ed. Oak Ridge
National Laboratory; 2004.

[3] Karden E.; S. Ploumen; B. Fricke; T. Miller and K. Snyder. Energy storage devices for future hybrid electric vehicle; Journal of Power Sources; 168:2-11; 2007.

[4] Clement-Nyns K. Impact of plug-in hybrid electric vehicles on the electricity system. PhD Thesis. Katholieke Universiteit Leuven ; 205; 2010.

[5] Baseley; S.; Ehret; C.; Greif; E.; and Kliffken; M.; Hydraulic Hybrid Systems for Commercial Vehicles; SAE Technical Paper 2007-01-4150; 2007; doi: 10.4271/2007-01-4150.

[6] Hui; S.; Jihai; J. and Xin; W. Torque control strategy for a parallel hydraulic hybrid vehicle; Journal of Terramechanics; 46(6): 259–265; 2009.

[7] Wu; B.; Lin; C.-C.; Filipi; Z.; Peng H.; Assanis; D. Optimal Power Management for a Hydraulic Hybrid Delivery Truck ; Journal of Vehicle System Dynamics; 42(1): 23-40; 2004.

[8] Van de Ven J D ; Olson M W; Li P Y. Development of a hydro-mechanical hydraulic hybrid drive train with independent wheel torque control for urban passenger vehicle. Proceedings of the International Fluid Power Exposition; Las Vegas; Nevada; 2008.

[9] Filipi; Z. and Kim; Y. J. Hydraulic Hybrid Propulsion for Heavy Vehicles: Combining the Simulation and Engine-in-the-Loop Techniques to Maximize the Fuel Economy and Emission Benefits; Les Rencontres Scientifiques de l’IFP; Advances in Hybrid Powertrains; 2008.

[10] Hui S ; “Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulated annealing genetic algorithm”; Engineering Applications of Artificial Intelligence; 23:27-33; 2010.

[11] Black T ; An overview of parameter control methods by self-adaptation in evolutionary algorithms; Fundamenta Informaticae; 35:51-66;1998.

[12] Ajith Abraham; Lakhmi Jain and Robert Goldberg (Eds.); Evolutionary Multiobjective Optimization-Theoretical Advances and Applications; Springer; Berlin; 2005. ISBN 978-1-84628-137-2.

[13] Ramakrishnan R.; Somashekhar S. Hiremath and M. Singaperumal. Power bond graph modeling of series hydraulic hybrid system; International Conference on Fluid Mechanics and Fluid Power; IIT Madras; Chennai; 608:1-8; 2010.

[14] Young Jae Kim and Zoran Filipi; “Series Hydraulic Hybrid Propulsion for a Light Truck –Optimizing the Thermostatic Power Management; Soceity of Automobile Engineerings; 24:1597-1609; 2007.

[15] Elder F.T. and D.R. Otis. Simulation of a hydraulic hybrid powertrain; American Society Of Mechanical Engineers; 73-ICT-50; 1973.

[16] Young Jae Kim and Zoran Filipi; “Simulation Study of a Series Hydraulic Hybrid Propulsion System for a Light Truck”; SAE 2007-01-4151; University of Michigan; 2007.

[17] R. Ramakrishnan; Somashekhar S. Hiremath and M. Singaperumal. Theoretical investigations on the effect of system parameters in series hydraulic hybrid system with hydrostatic regenerative braking. The Journal of Mechanical Science and Technology; 26(5):1321-1331; 2012.

[18] Kruse R.E.; T.A. Huls; "Development of the Federal Urban Driving Schedule;" SAE Paper No. 730553; 1973.

[19] ModeFRONTIER; Version 3.1.0; ESTECO; Trieste; Italy.

[20] Bäck T.; Evolution Strategies Module R1.1 for ModeFRONTIER.; Technical Report 2004-05.

Citeringar i Crossref