Resumen
In this study, a scaled-down system, which can be used as a benchmark test for the battery storage designing of electric vehicles (EVs) is proposed. This model was based on the hardware simulator of the battery storage system (BSS) used from a single cell up to 4 cells in a series pack system, which simulates a practical battery pack. The developed simulator can charge and discharge any rechargeable battery, such as Li-Ion, Ni?MH or Pb battery. The scaling ratio of the simulator was evaluated by the ratio of the current or power of the battery pack specimen related to the specification. Also, this study proposes an innovative state of charge (SoC) estimation of the battery pack for EVs based on genuine results obtained through practical tests. This estimation was carried out by an adaptive artificial neural network (ANN) algorithm, using simple inputs. As well, this model can deduct the state of health (SoH) of the battery pack based on the power output level and waveform characteristics. The results of the ANN showed high generalization, a low error of SoC estimation at the level of 1.1%, with a calculation time less than 16.5 s. Regarding the hardware simulator, the similarity of the results and waveform accuracy of the scaled-down battery systems compared with the real battery pack was very acceptable with a maximum deviation of 2.1% in the worst scenario. The cells cycled with different depths of discharge (DoD) or C-rates, at different temperatures with different initial SoCs using any arbitrary current waveforms. Our conclusions will help battery manufacturers to test and evaluate the performance of the BSS in different applications, such as EVs, PV generation, and wind farm, with significant cost reduction. Also, the ANN algorithm can be used and embedded in EVs or in any other industrial application, as proposed in this paper. This study contributed to the real-time diagnosis of the BSS without interrupting the normal operation based on its features.