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• W4311232092 abstract "Modeling of batteries is necessary to control their operating mode and diagnose their condition. It is important to model the life cycle, i. e. degradation of basic parameters over a long service life. This is due to the fact that the cost of buffering electricity by batteries is associated with their cycling resource, which can be increased by optimizing the mode of operation of the drive in the energy system. The existing models of battery degradation are characterized by specificity, limited work on standardized charge-discharge cycles, and mathematical cumbersomeness. The article proposes a universal approach devoid of the above disadvantages. The concept of continuous battery wear during the service life is used. A simple empirical model is presented that does not consider in detail the characteristics of the state of batteries during a separate charge-discharge cycle, and does not include voltaic variables. The model considers the intensity of the current wear of the battery as a function of the state of its charge, temperature, the current of the external circuit and the current of self-discharge, the full charge that has flowed through the battery since the beginning of its operation. In this case, the amount of wear (degradation) is determined by the integral of the function of the intensity of current wear over the battery life. To optimize the parameters of the model, a random search method is used in combination with a genetic selection algorithm. The corresponding model of degradation of parameters for the Delta GEL-12-55 lead-acid battery has been constructed, in which the data on degradation of capacity given in the technical description from the manufacturer are used. The efficiency of the parameter optimization algorithm and the adequacy of the resulting model are shown. The model developed by the authors can be used for technical and economic calculations of generator – storage –consumer systems, hybrid power storage systems, and compact representation of large volumes of experimental data on the degradation of specific batteries." @default.
• W4311232092 created "2022-12-24" @default.
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• W4311232092 date "2022-12-07" @default.
• W4311232092 modified "2023-10-04" @default.
• W4311232092 title "Universal Simulation Model of Battery Degradation with Optimization of Parameters by Genetic Algorithm" @default.
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• W4311232092 doi "https://doi.org/10.21122/1029-7448-2022-65-6-481-498" @default.
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