IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v105y2013icp335-348.html
   My bibliography  Save this article

Fuel economy optimization of an Atkinson cycle engine using genetic algorithm

Author

Listed:
  • Zhao, Jinxing
  • Xu, Min

Abstract

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%.

Suggested Citation

  • Zhao, Jinxing & Xu, Min, 2013. "Fuel economy optimization of an Atkinson cycle engine using genetic algorithm," Applied Energy, Elsevier, vol. 105(C), pages 335-348.
    • Handle: RePEc:eee:appene:v:105:y:2013:i:c:p:335-348
    DOI: 10.1016/j.apenergy.2012.12.061
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191200952X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2012.12.061?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Y.G. & Pilidis, P., 2010. "GA-based design-point performance adaptation and its comparison with ICM-based approach," Applied Energy, Elsevier, vol. 87(1), pages 340-348, January.
    2. Fontana, G. & Galloni, E., 2009. "Variable valve timing for fuel economy improvement in a small spark-ignition engine," Applied Energy, Elsevier, vol. 86(1), pages 96-105, January.
    3. D'Errico, G. & Cerri, T. & Pertusi, G., 2011. "Multi-objective optimization of internal combustion engine by means of 1D fluid-dynamic models," Applied Energy, Elsevier, vol. 88(3), pages 767-777, March.
    4. Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
    5. Zhao, Jinxing & Xu, Min & Li, Mian & Wang, Bin & Liu, Shuangzhai, 2012. "Design and optimization of an Atkinson cycle engine with the Artificial Neural Network Method," Applied Energy, Elsevier, vol. 92(C), pages 492-502.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wan, Peng & Liu, Bolan & Li, Ben & Liu, Fanshuo & Zhang, Junwei & Fan, Wenhao & Tang, Jingxian, 2023. "Engine modelling architecture study for hybrid electric vehicle diagnosis application," Energy, Elsevier, vol. 282(C).
    2. Yu, Xunzhao & Zhu, Ling & Wang, Yan & Filev, Dimitar & Yao, Xin, 2022. "Internal combustion engine calibration using optimization algorithms," Applied Energy, Elsevier, vol. 305(C).
    3. Li, Yangtao & Khajepour, Amir & Devaud, Cécile & Liu, Kaimin, 2017. "Power and fuel economy optimizations of gasoline engines using hydraulic variable valve actuation system," Applied Energy, Elsevier, vol. 206(C), pages 577-593.
    4. Wang, Ligang & Yang, Yongping & Dong, Changqing & Morosuk, Tatiana & Tsatsaronis, George, 2014. "Multi-objective optimization of coal-fired power plants using differential evolution," Applied Energy, Elsevier, vol. 115(C), pages 254-264.
    5. Li, Yangyang & Zhou, Shi & Liu, Jingping & Tong, Ji & Dang, Jian & Yang, Fuyuan & Ouyang, Minggao, 2023. "Multi-objective optimization of the Atkinson cycle gasoline engine using NSGA Ⅲ coupled with support vector machine and back-propagation algorithm," Energy, Elsevier, vol. 262(PA).
    6. Meng Xia & Fujun Zhang, 2020. "Application of Multi-Parameter Fuzzy Optimization to Enhance Performance of a Regulated Two-Stage Turbocharged Diesel Engine Operating at High Altitude," Energies, MDPI, vol. 13(17), pages 1-12, August.
    7. Yuh-Yih Wu & James H. Wang & Faizan Mushtaq Mir, 2018. "Improving the Thermal Efficiency of the Homogeneous Charge Compression Ignition Engine by Using Various Combustion Patterns," Energies, MDPI, vol. 11(11), pages 1-20, November.
    8. Baklacioglu, Tolga & Turan, Onder & Aydin, Hakan, 2015. "Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks," Energy, Elsevier, vol. 86(C), pages 709-721.
    9. Xu, Zheng & Ji, Fenzhu & Ding, Shuiting & Zhao, Yunhai & Zhang, Xiangbo & Zhou, Yu & Zhang, Qi & Du, Farong, 2020. "High-altitude performance and improvement methods of poppet valves 2-stroke aircraft diesel engine," Applied Energy, Elsevier, vol. 276(C).
    10. Zhao, Xiancong & Bai, Hao & Lu, Xin & Shi, Qi & Han, Jiehai, 2015. "A MILP model concerning the optimisation of penalty factors for the short-term distribution of byproduct gases produced in the iron and steel making process," Applied Energy, Elsevier, vol. 148(C), pages 142-158.
    11. Yi Dong & Jianmin Liu & Yanbin Liu & Xinyong Qiao & Xiaoming Zhang & Ying Jin & Shaoliang Zhang & Tianqi Wang & Qi Kang, 2020. "A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment," Energies, MDPI, vol. 13(1), pages 1-24, January.
    12. Li, Yangtao & Khajepour, Amir & Devaud, Cécile, 2018. "Realization of variable Otto-Atkinson cycle using variable timing hydraulic actuated valve train for performance and efficiency improvements in unthrottled gasoline engines," Applied Energy, Elsevier, vol. 222(C), pages 199-215.
    13. Zhaohui Jin & Wei Hong & Tian You & Yan Su & Xiaoping Li & Fangxi Xie, 2020. "Effect of Multi-Factor Coupling on the Movement Characteristics of the Hydraulic Variable Valve Actuation," Energies, MDPI, vol. 13(11), pages 1-20, June.
    14. Liu, Qi & Guo, Tao & Fu, Jianqin & Dai, Hongliang & Liu, Jingping, 2022. "Experimental study on the effects of injection parameters and exhaust gas recirculation on combustion, emission and performance of Atkinson cycle gasoline direct-injection engine," Energy, Elsevier, vol. 238(PB).
    15. Tadros, M. & Ventura, M. & Guedes Soares, C., 2019. "Optimization procedure to minimize fuel consumption of a four-stroke marine turbocharged diesel engine," Energy, Elsevier, vol. 168(C), pages 897-908.
    16. De Bellis, Vincenzo, 2016. "Performance optimization of a spark-ignition turbocharged VVA engine under knock limited operation," Applied Energy, Elsevier, vol. 164(C), pages 162-174.
    17. Yang, Zhuyong & Miganakallu, Niranjan & Miller, Tyler & Worm, Jeremy & Naber, Jeffrey & Roth, David, 2020. "Comparing methods for improving spark-ignited engine efficiency: Over-expansion with multi-link cranktrain and high compression ratio with late intake valve closing," Applied Energy, Elsevier, vol. 262(C).
    18. Paykani, Amin & Frouzakis, Christos E. & Boulouchos, Konstantinos, 2019. "Numerical optimization of methane-based fuel blends under engine-relevant conditions using a multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 242(C), pages 1712-1724.
    19. Zhao, Jinxing, 2017. "Research and application of over-expansion cycle (Atkinson and Miller) engines – A review," Applied Energy, Elsevier, vol. 185(P1), pages 300-319.
    20. Zhu, Zhenxia & Zhang, Fujun & Li, Changjiang & Wu, Taotao & Han, Kai & Lv, Jianguo & Li, Yunlong & Xiao, Xuelian, 2015. "Genetic algorithm optimization applied to the fuel supply parameters of diesel engines working at plateau," Applied Energy, Elsevier, vol. 157(C), pages 789-797.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Yangtao & Khajepour, Amir & Devaud, Cécile & Liu, Kaimin, 2017. "Power and fuel economy optimizations of gasoline engines using hydraulic variable valve actuation system," Applied Energy, Elsevier, vol. 206(C), pages 577-593.
    2. De Bellis, Vincenzo, 2016. "Performance optimization of a spark-ignition turbocharged VVA engine under knock limited operation," Applied Energy, Elsevier, vol. 164(C), pages 162-174.
    3. Zhang, Qiang & Ogren, Ryan M. & Kong, Song-Charng, 2016. "A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA," Applied Energy, Elsevier, vol. 165(C), pages 676-684.
    4. Li, Yangtao & Khajepour, Amir & Devaud, Cécile, 2018. "Realization of variable Otto-Atkinson cycle using variable timing hydraulic actuated valve train for performance and efficiency improvements in unthrottled gasoline engines," Applied Energy, Elsevier, vol. 222(C), pages 199-215.
    5. Tadros, M. & Ventura, M. & Guedes Soares, C., 2019. "Optimization procedure to minimize fuel consumption of a four-stroke marine turbocharged diesel engine," Energy, Elsevier, vol. 168(C), pages 897-908.
    6. Teodosio, Luigi & Pirrello, Dino & Berni, Fabio & De Bellis, Vincenzo & Lanzafame, Rosario & D'Adamo, Alessandro, 2018. "Impact of intake valve strategies on fuel consumption and knock tendency of a spark ignition engine," Applied Energy, Elsevier, vol. 216(C), pages 91-104.
    7. Zhao, Jinxing, 2017. "Research and application of over-expansion cycle (Atkinson and Miller) engines – A review," Applied Energy, Elsevier, vol. 185(P1), pages 300-319.
    8. Curto-Risso, P.L. & Medina, A. & Calvo Hernández, A. & Guzmán-Vargas, L. & Angulo-Brown, F., 2011. "On cycle-to-cycle heat release variations in a simulated spark ignition heat engine," Applied Energy, Elsevier, vol. 88(5), pages 1557-1567, May.
    9. Osorio, Julian D. & Rivera-Alvarez, Alejandro, 2018. "Efficiency enhancement of spark-ignition engines using a Continuous Variable Valve Timing system for load control," Energy, Elsevier, vol. 161(C), pages 649-662.
    10. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    11. Shen, Kai & Xu, Zishun & Chen, Hong & Zhang, Zhendong, 2021. "Investigation on the EGR effect to further improve fuel economy and emissions effect of Miller cycle turbocharged engine," Energy, Elsevier, vol. 215(PB).
    12. Richard Fiifi Turkson & Fuwu Yan & Mohamed Kamal Ahmed Ali & Bo Liu & Jie Hu, 2016. "Modeling and Multi-Objective Optimization of Engine Performance and Hydrocarbon Emissions via the Use of a Computer Aided Engineering Code and the NSGA-II Genetic Algorithm," Sustainability, MDPI, vol. 8(1), pages 1-15, January.
    13. Ashish J Chaudhari & Santosh K Hotta & Niranjan Sahoo & Vinayak Kulkarni, 2019. "Effect of vertical location of the spark plug on the performance of a raw biogas-fueled variable compression ratio spark ignition engine," Energy & Environment, , vol. 30(7), pages 1313-1338, November.
    14. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    15. Mofid, Hossein & Jazayeri-Rad, Hooshang & Shahbazian, Mehdi & Fetanat, Abdolvahhab, 2019. "Enhancing the performance of a parallel nitrogen expansion liquefaction process (NELP) using the multi-objective particle swarm optimization (MOPSO) algorithm," Energy, Elsevier, vol. 172(C), pages 286-303.
    16. Zhang, Zhijin & Zhang, Haiyan & Wang, Tianyou & Jia, Ming, 2014. "Effects of tumble combined with EGR (exhaust gas recirculation) on the combustion and emissions in a spark ignition engine at part loads," Energy, Elsevier, vol. 65(C), pages 18-24.
    17. Yuan, Zhipeng & Fu, Jianqin & Liu, Qi & Ma, Yinjie & Zhan, Zhangsong, 2018. "Quantitative study on influence factors of power performance of variable valve timing (VVT) engines and correction of its governing equation," Energy, Elsevier, vol. 157(C), pages 314-326.
    18. Ramasamy, D. & Zainal, Z.A. & Kadirgama, K. & Walker-Gitano Briggs, Horizon, 2016. "Effect of dissimilar valve lift on a bi-fuel CNG engine operation," Energy, Elsevier, vol. 112(C), pages 509-519.
    19. Pauras Sawant & Michael Warstler & Saiful Bari, 2018. "Exhaust Tuning of an Internal Combustion Engine by the Combined Effects of Variable Exhaust Pipe Diameter and an Exhaust Valve Timing System," Energies, MDPI, vol. 11(6), pages 1-16, June.
    20. Ali, Mohamed Kamal Ahmed & Fuming, Peng & Younus, Hussein A. & Abdelkareem, Mohamed A.A. & Essa, F.A. & Elagouz, Ahmed & Xianjun, Hou, 2018. "Fuel economy in gasoline engines using Al2O3/TiO2 nanomaterials as nanolubricant additives," Applied Energy, Elsevier, vol. 211(C), pages 461-478.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:105:y:2013:i:c:p:335-348. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.