IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v169y2016icp888-898.html
   My bibliography  Save this article

An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications

Author

Listed:
  • Blaifi, S.
  • Moulahoum, S.
  • Colak, I.
  • Merrouche, W.

Abstract

Modeling of batteries in photovoltaic systems has been a major issue related to the random dynamic regime imposed by the changes of solar irradiation and ambient temperature added to the complexity of battery electrochemical and electrical behaviors. However, various approaches have been proposed to model the battery behavior by predicting from detailed electrochemical, electrical or analytical models to high-level stochastic models. In this paper, an improvement of dynamic electrical battery model is proposed by automatic parameter extraction using genetic algorithm in order to give usefulness and future implementation for practical application. It is highlighted that the enhancement of 21 values of the parameters of CEIMAT model presents a good agreement with real measurements for different modes like charge or discharge and various conditions.

Suggested Citation

  • Blaifi, S. & Moulahoum, S. & Colak, I. & Merrouche, W., 2016. "An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications," Applied Energy, Elsevier, vol. 169(C), pages 888-898.
    • Handle: RePEc:eee:appene:v:169:y:2016:i:c:p:888-898
    DOI: 10.1016/j.apenergy.2016.02.062
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916301969
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.02.062?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. Waag, Wladislaw & Sauer, Dirk Uwe, 2013. "Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination," Applied Energy, Elsevier, vol. 111(C), pages 416-427.
    2. Illanes, Rafael & De Francisco, Adolfo & Núñez, Francisco & De Blas, Marian & García, Almudena & Torres, José Luis, 2014. "Dynamic simulation and modelling of stand-alone PV systems by using state equations and numerical integration methods," Applied Energy, Elsevier, vol. 135(C), pages 440-449.
    3. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    4. Liu, Guangming & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Hua, Jianfeng, 2015. "A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications," Applied Energy, Elsevier, vol. 149(C), pages 297-314.
    5. Yang, Woo-Joo & Wang, Hong-Yang & Lee, Dae-Hyung & Kim, Young-Bae, 2015. "Channel geometry optimization of a polymer electrolyte membrane fuel cell using genetic algorithm," Applied Energy, Elsevier, vol. 146(C), pages 1-10.
    6. 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.
    7. Fares, Robert L. & Meyers, Jeremy P. & Webber, Michael E., 2014. "A dynamic model-based estimate of the value of a vanadium redox flow battery for frequency regulation in Texas," Applied Energy, Elsevier, vol. 113(C), pages 189-198.
    8. Zhou, Wei & Yang, Hongxing & Fang, Zhaohong, 2008. "Battery behavior prediction and battery working states analysis of a hybrid solar–wind power generation system," Renewable Energy, Elsevier, vol. 33(6), pages 1413-1423.
    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. Wang, Xuewei & Wang, Jing & Wang, Lin & Yuan, Ruiming, 2019. "Non-overlapping moving compressive measurement algorithm for electrical energy estimation of distorted m-sequence dynamic test signal," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Banguero, Edison & Correcher, Antonio & Pérez-Navarro, Ángel & García, Emilio & Aristizabal, Andrés, 2020. "Diagnosis of a battery energy storage system based on principal component analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2438-2449.
    3. Bhattacharjee, Vikram & Khan, Irfan, 2018. "A non-linear convex cost model for economic dispatch in microgrids," Applied Energy, Elsevier, vol. 222(C), pages 637-648.
    4. H. Eduardo Ariza Chacón & Edison Banguero & Antonio Correcher & Ángel Pérez-Navarro & Francisco Morant, 2018. "Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms," Energies, MDPI, vol. 11(9), pages 1-14, September.
    5. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    6. Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
    7. Coppitters, Diederik & De Paepe, Ward & Contino, Francesco, 2020. "Robust design optimization and stochastic performance analysis of a grid-connected photovoltaic system with battery storage and hydrogen storage," Energy, Elsevier, vol. 213(C).
    8. Ke, Bwo-Ren & Chung, Chen-Yuan & Chen, Yen-Chang, 2016. "Minimizing the costs of constructing an all plug-in electric bus transportation system: A case study in Penghu," Applied Energy, Elsevier, vol. 177(C), pages 649-660.
    9. Blaifi, Sid-ali & Moulahoum, Samir & Taghezouit, Bilal & Saim, Abdelhakim, 2019. "An enhanced dynamic modeling of PV module using Levenberg-Marquardt algorithm," Renewable Energy, Elsevier, vol. 135(C), pages 745-760.
    10. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    11. Coppitters, Diederik & De Paepe, Ward & Contino, Francesco, 2021. "Robust design optimization of a photovoltaic-battery-heat pump system with thermal storage under aleatory and epistemic uncertainty," Energy, Elsevier, vol. 229(C).
    12. Wang, Xuewei & Wang, Jing & Yuan, Ruiming & Jiang, Zhenyu, 2019. "Dynamic error testing method of electricity meters by a pseudo random distorted test signal," Applied Energy, Elsevier, vol. 249(C), pages 67-78.
    13. Shyang-Chyuan Fang & Bwo-Ren Ke & Chen-Yuan Chung, 2017. "Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System," Energies, MDPI, vol. 10(7), pages 1-20, June.
    14. Seera, Manjeevan & Tan, Choo Jun & Chong, Kok-Keong & Lim, Chee Peng, 2021. "Performance analyses of various commercial photovoltaic modules based on local spectral irradiances in Malaysia using genetic algorithm," Energy, Elsevier, vol. 223(C).

    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. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    2. Avvari, G.V. & Pattipati, B. & Balasingam, B. & Pattipati, K.R. & Bar-Shalom, Y., 2015. "Experimental set-up and procedures to test and validate battery fuel gauge algorithms," Applied Energy, Elsevier, vol. 160(C), pages 404-418.
    3. Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
    4. Zhuang, Rui & Wang, Xiaonan & Guo, Miao & Zhao, Yingru & El-Farra, Nael H. & Palazoglu, Ahmet, 2020. "Waste-to-hydrogen: Recycling HCl to produce H2 and Cl2," Applied Energy, Elsevier, vol. 259(C).
    5. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    6. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    7. Lu, Guolong & Fan, Wenxuan & Lu, Dafeng & Zhao, Taotao & Wu, Qianqian & Liu, Mingxin & Liu, Zhenning, 2024. "Lung-inspired hybrid flow field to enhance PEMFC performance: A case of dual optimization by response surface and artificial intelligence," Applied Energy, Elsevier, vol. 355(C).
    8. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    9. Li, Yifeng & Bao, Jie & Skyllas-Kazacos, Maria & Akter, Md Parvez & Zhang, Xinan & Fletcher, John, 2019. "Studies on dynamic responses and impedance of the vanadium redox flow battery," Applied Energy, Elsevier, vol. 237(C), pages 91-102.
    10. John Jairo Ceballos & Andrés Melgar & Francisco V. Tinaut, 2021. "Influence of Environmental Changes Due to Altitude on Performance, Fuel Consumption and Emissions of a Naturally Aspirated Diesel Engine," Energies, MDPI, vol. 14(17), pages 1-41, August.
    11. Li, Hui & Nie, Weige & Duan, Huiming, 2024. "A Haavelmo grey model based on economic growth and its application to energy industry investments," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    12. Zhang, Yue & Farnoosh, Arash, 2019. "Analyzing the dynamic impact of electricity futures on revenue and risk of renewable energy in China," Energy Policy, Elsevier, vol. 132(C), pages 678-690.
    13. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    14. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    15. Ehab Issa El-Sayed & Salah K. ElSayed & Mohammad Alsharef, 2024. "Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques," Sustainability, MDPI, vol. 16(21), pages 1-21, October.
    16. Prasad, Abhnil A. & Taylor, Robert A. & Kay, Merlinde, 2017. "Assessment of solar and wind resource synergy in Australia," Applied Energy, Elsevier, vol. 190(C), pages 354-367.
    17. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    18. Rawat, Rahul & Kaushik, S.C. & Lamba, Ravita, 2016. "A review on modeling, design methodology and size optimization of photovoltaic based water pumping, standalone and grid connected system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1506-1519.
    19. Ko, Chi-Jyun & Chen, Kuo-Ching, 2024. "Using tens of seconds of relaxation voltage to estimate open circuit voltage and state of health of lithium ion batteries," Applied Energy, Elsevier, vol. 357(C).
    20. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.

    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:169:y:2016:i:c:p:888-898. 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.