Evaluation of Nature-Inspired Algorithms for Battery Parame-ter Estimation in Renewable Energy Generation Systems
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2018-08-13 
https://doi.org/10.14419/ijet.v7i3.15.17506
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Bird mating optimizer (BMO), Electrochemical impedance spectroscopy (EIS), Lightning search algorithm (LSA), Randle’s battery model. -
Batteries in renewable energy systems suffer problems that affect their service life and quality of performance. Therefore, battery management systems (BMS) are employed in battery-integrated systems to maintain optimal operating conditions by various dynamic impedance based battery models. These models require an accurate model parameter for BMS to work effectively. In this paper, two recently developed metaheuristic optimization, namely bird mating optimizer (BMO) and lightning search algorithm (LSA) is used effectively to determine the required parameters of well-known Randle’s battery model. Initially, electrochemical impedance spectroscopy (EIS) test for an EnerSys Cyclon lead-acid cell with a rated capacity of 2.5Ah is conducted using EZSTAT-pro Galvanostat/Potentiostat device from Nuvant systems Inc. Next, the Randle’s battery model parameters are obtained by BMO and LSA and its performances are evaluated. The results show that BMO and LSA can accurately find the model parameters. LSA obtains slightly more accurate results than BMO and converges much faster. However, for the same number of iterations, BMO takes less computation time than LSA. The optimized model can be used in BMS for fault finding and condition monitoring.
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References
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[7] Andre D, Meiler M, Steiner K, Walz H, Soczka-Guth T & Sauer DU (2011), Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling. Journal of Power Sources 196, 12, 5349-5356.[8] Ranieri M, Alberto D, Piret H & Cattin V (2017), Electronic module for the thermal monitoring of a Li-ion battery cell through the electrochemical impedance estimation. Microelectronics Reliability 79, 410-415.[9] Tröltzsch U, Kanoun O & Tränkler HR (2006), Characterizing aging effects of lithium ion batteries by impedance spectroscopy. Electrochimica Acta 51, 8–9, 1664-1672.[10] Waag W, Käbitz S & Sauer DU (2013), Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application. Applied Energy 102, 885-897.[11] Richardson RR, Ireland PT & Howey DA (2014), Battery internal temperature estimation by combined impedance and surface temperature measurement. 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John Wiley, UK.[18] Jong KD (1975), Analysis of the behavior of a class of genetic adaptive systems. Ph.D. Thesis. University of Michigan. Ann Arbor, MI.[19] Koza JR (1990), Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Stanford University, CA.[20] Holland JH (1975), Adaptation in Natural and Artificial Systems. University of Michigan Press. Ann Arbor, MI.[21] Goldberg DE (1989), Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Boston.[22] Glover F (1977), Heuristic for integer programming using surrogate constraints. Decision Sciences 8, 1, 156–166.[23] Kirkpatrick S, Gelatt C & Vecchi M (1983), Optimization by simulated annealing. Science 220, 671–680.[24] Geem ZW, Kim JH & Loganathan GV (2001), A new heuristic optimization algorithm: harmony search. Simulation 76, 2, 60– 68.[25] Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH & Teller E (1953), Equations of state calculations by fast computing machines. A Journal of Chemical Physics 21, 1087–1092.[26] Pincus M (1970), A Monte Carlo method for the approximate solution of certain types of constrained optimization problems. Operations Research 18, 1225–1228.[27] Schwefel HP (1994), On the evolution of evolutionary computation. In: Computational Intelligence: Imitating Life, IEEE Press. New York, pp. 116–124.[28] Kennedy J & Eberhat RC (1995), Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. IEEE Service Center, Piscataway, pp. 1942–1948.[29] Askarzadeh A (2014), Bird mating optimizer: An optimization algorithm inspired by bird mating strategies. Communications in Nonlinear Science and Numerical Simulation 9, 4, 1213-1228.[30] Omer ZM, Fardoun AA & Hussein AA (2016), Large scale photovoltaic array fault diagnosis for optimized solar cell parameters extracted by heuristic evolutionary algorithm. IEEE PES General Meeting, USA.[31] Zhu JJ, Huang M & Lu ZR (2017), Bird mating optimizer for structural damage detection using a hybrid objective function. Swarm and Evolutionary Computation 35, 41-52.[32] Shareef H, Ibrahim AA & Mutlag AH (2015), Lightning search algorithm. Applied Soft Computing 36, 315-333.[33] Shareef H, Mutlag AH & Mohamed A (2015), A novel approach for fuzzy logic PV inverter controller optimization using lightning search algorithm. Neurocomputing 168, 435-453.
- [1] Jossen A, Garche J & Sauer DU (2004), Operation conditions of batteries in PV applications. Solar Energy 76, 759-769. [2] Sauer DU, Bächler M, Bopp G, Höhe W, Mittermeier J, Sprau P, Willer B & Wollny M (1997), Analysis of the performance parameters of lead/acid batteries in photovoltaic systems. Journal of Power Sources 64, 1, 197-201.[3] Ruetschi P (2004), Aging mechanisms and service life of lead–acid batteries. Journal of Power Sources 127, 1-2, 33–44.[4] Lam LT, Haigh NP, Phyland CG & Urban AJ (2004), Failure mode of valve-regulated lead-acid batteries underhigh-rate partial-state-of-charge operation. Journal of Power Sources 133, 1, 126–134.[5] Catherino HA, Feres FF & Trinidad F (2004), Sulfation in lead–acid batteries. Journal of Power Sources 129, 1, 113–120.[6] ApÇŽteanu L, Hollenkamp AF & Koop MJ (1993), Electrolyte stratification in lead/acid batteries: Effect of grid antimony and relationship to capacity loss. Journal of Power Sources 46, 2–3, 239-250. [7] Andre D, Meiler M, Steiner K, Walz H, Soczka-Guth T & Sauer DU (2011), Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling. Journal of Power Sources 196, 12, 5349-5356.[8] Ranieri M, Alberto D, Piret H & Cattin V (2017), Electronic module for the thermal monitoring of a Li-ion battery cell through the electrochemical impedance estimation. Microelectronics Reliability 79, 410-415.[9] Tröltzsch U, Kanoun O & Tränkler HR (2006), Characterizing aging effects of lithium ion batteries by impedance spectroscopy. Electrochimica Acta 51, 8–9, 1664-1672.[10] Waag W, Käbitz S & Sauer DU (2013), Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application. Applied Energy 102, 885-897.[11] Richardson RR, Ireland PT & Howey DA (2014), Battery internal temperature estimation by combined impedance and surface temperature measurement. Journal of Power Sources 265, 254-261.[12] Gadsden SA & Habibi SR (2011), Model-Based Fault Detection of a Battery System in a Hybrid Electric Vehicle. 2011 IEEE Vehicle Power and Propulsion Conference. Chicago, pp. 1–6.[13] Brik K & Ammar FB (2008), The Fault tree analysis of lead acid battery’s degradation. Journal of Electrical Sytems 4, 2, 2-12.[14] Westerhoff U, Kroker T, Kurbach K & Kurrat M (2016), Electrochemical impedance spectroscopy based estimation of the state of charge of lithium-ion batteries. Journal of Energy Storage 8, 244-256.[15] KÅ™ivÃk P (2018), Methods of SoC determination of lead acid battery. Journal of Energy Storage 15, 191-195.[16] Böttiger M, Paulitschke M & Bocklisch T (2017), Systematic experimental pulse test investigation for parameter identification of an equivalent based lithium-ion battery model. Energy Procedia 135, 337-346.[17] Fogel LJ, Owens AJ & Walsh MJ (1966), Artificial Intelligence Through Simulated Evolution. John Wiley, UK.[18] Jong KD (1975), Analysis of the behavior of a class of genetic adaptive systems. Ph.D. Thesis. University of Michigan. Ann Arbor, MI.[19] Koza JR (1990), Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Stanford University, CA.[20] Holland JH (1975), Adaptation in Natural and Artificial Systems. University of Michigan Press. Ann Arbor, MI.[21] Goldberg DE (1989), Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Boston.[22] Glover F (1977), Heuristic for integer programming using surrogate constraints. Decision Sciences 8, 1, 156–166.[23] Kirkpatrick S, Gelatt C & Vecchi M (1983), Optimization by simulated annealing. Science 220, 671–680.[24] Geem ZW, Kim JH & Loganathan GV (2001), A new heuristic optimization algorithm: harmony search. Simulation 76, 2, 60– 68.[25] Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH & Teller E (1953), Equations of state calculations by fast computing machines. A Journal of Chemical Physics 21, 1087–1092.[26] Pincus M (1970), A Monte Carlo method for the approximate solution of certain types of constrained optimization problems. Operations Research 18, 1225–1228.[27] Schwefel HP (1994), On the evolution of evolutionary computation. In: Computational Intelligence: Imitating Life, IEEE Press. New York, pp. 116–124.[28] Kennedy J & Eberhat RC (1995), Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. IEEE Service Center, Piscataway, pp. 1942–1948.[29] Askarzadeh A (2014), Bird mating optimizer: An optimization algorithm inspired by bird mating strategies. Communications in Nonlinear Science and Numerical Simulation 9, 4, 1213-1228.[30] Omer ZM, Fardoun AA & Hussein AA (2016), Large scale photovoltaic array fault diagnosis for optimized solar cell parameters extracted by heuristic evolutionary algorithm. IEEE PES General Meeting, USA.[31] Zhu JJ, Huang M & Lu ZR (2017), Bird mating optimizer for structural damage detection using a hybrid objective function. Swarm and Evolutionary Computation 35, 41-52.[32] Shareef H, Ibrahim AA & Mutlag AH (2015), Lightning search algorithm. Applied Soft Computing 36, 315-333.[33] Shareef H, Mutlag AH & Mohamed A (2015), A novel approach for fuzzy logic PV inverter controller optimization using lightning search algorithm. Neurocomputing 168, 435-453.
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How to Cite
Shareef, H., Mainul Islam, M., & Sasi Stephen, S. (2018). Evaluation of Nature-Inspired Algorithms for Battery Parame-ter Estimation in Renewable Energy Generation Systems. International Journal of Engineering & Technology, 7(3.15), 80-85. https://doi.org/10.14419/ijet.v7i3.15.17506
