Abstract
Accurate and efficient battery modeling is essential to maximize the performance of isolated energy systems and to extend battery lifetime. This paper proposes a battery model that represents the charging and discharging process of a lead-acid battery bank. This model is validated over real measures taken from a battery bank installed in a research center placed at “El Chocó”, Colombia. In order to fit the model, three optimization algorithms (particle swarm optimization, cuckoo search, and particle swarm optimization + perturbation) are implemented and compared, the last one being a new proposal. This research shows that the identified model is able to estimate real battery features, such as state of charge (SOC) and charging/discharging voltage. The comparison between simulations and real measures shows that the model is able to absorb reading problems, signal delays, and scaling errors. The approach we present can be implemented in other types of batteries, especially those used in stand-alone systems.
Highlights
Renewable and distributed energy generation are trendy research topics that have to go hand-in-hand with energy storage research
The extension we propose includes four extra parameters, which allow a better adjustment of the curves of charging/discharging voltage of the lead-acid battery
Process for all evolutionary algorithms (EA) is shown in Figure 4, which starts with the estimation of initial coefficient values that are grouped in a parameter set (PS), in the second step, the initial PS is used to create a random population of size j
Summary
Renewable and distributed energy generation are trendy research topics that have to go hand-in-hand with energy storage research. Guasch et al [39] used the battery model proposed by [40] and they added two extra parameters: the level of energy and the state of health. These new parameters are able to predict the degradation of the battery capacity and the increase of self-discharge current in the long-term. One disadvantage of the model is the high calculation time due to the large number of samples required for a good identification Another interesting EA that can be used in parameter identification is cuckoo search (CS) [44,45].
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