Abstract
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.
Highlights
In recent years, the importance and necessity for power generation systems based on renewable power sources have become a primary focus of research [1,2]
This study proposes an alternative and cost-effective approach of benchmark stress test and diagnosis of the battery storage system (BSS) during the early designing phase comparing to high-cost industrial devices
The main circuit of the improved multi type chemistry charging/discharging (MTC) for the secondary-batteries is configured in the same way as the previous model, but the quality of the electronic components are improved in faster switching time, and higher voltage and current operation, with a lower operating temperature of the components due to a better cooling system
Summary
The importance and necessity for power generation systems based on renewable power sources have become a primary focus of research [1,2]. If the accuracy is low, ANN will continue to train and updating its weights until the error is reduced and the structure is optimized This helps for the implementation of real-time estimation of the SoC or SoH of the battery as well it can enrich the generalization of the model. Comparing to other methods of SoC estimation is lacking in generality, which leads to wrong SOC estimation in certain battery types This method is based on a high accurate and reliable model in order to obtain the characteristics of the real-life battery and predict its behavior under a wide variety of conditions. It is a time saving and mobile friendly approach for low- and medium-power applications
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