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In this paper, a bi-directional-buck-boost-converter-based active equalizer is developed. The energy between adjacent cells can be transferred bi-directionally by manipulating the balancing current to solve the unbalanced problem in a battery module. It is noted that the conduction time of the main switch in the conventional buck-boost equalizer is...
Citations
... However, a large number of switching switches are required, and there are problems such as large circuit volume, complex control, and low reliability. Therefore, the active equalization topology based on a converter is suitable for energy transfer between adjacent batteries [10,11]. When it is applied to a battery pack with many cells, it will lead to slow equalization speed and low equalization efficiency. ...
As a bi-directional converter, the Buck-Boost converter, which has the advantages of simple structure and taking the SOC of the battery as the balance variable, is adopted as the balance topology in this paper. In view of the shortcomings of traditional balance topology, which can only balance two adjacent batteries, resulting in a long balance time and insufficient balance accuracy, a cascade active balance charging topology that can balance in intra-group and inter-group situations simultaneously is proposed. At the same time, the fuzzy control algorithm and model predictive control are used as the balance control strategies, respectively, to control whether the MOSFET is on or off in the balance topology circuit. The duty cycle is dynamically adjusted to the size of the balance current to achieve the balance of the battery pack. The results show that the cascade Buck-Boost balance topology based on model prediction control can accurately control the balancing current and improve the accuracy and speed of the balance, and it is more suitable for the actual working process.
... In addition, changes in temperature, self-discharge rate, aging degradation, and voltage imbalance occur, resulting in reliability and safety issues [5]. To avoid these problems, a battery management system (BMS) is used, responsible for monitoring the voltage, current, and temperature parameters and controlling through software and hardware [6,7]. ...
Lithium-ion batteries (LIBs) are the state-of-the-art technology for energy storage systems. LIBs can store energy for longer, with higher density and power capacity than other technologies. Despite that, they are sensitive to abuses and failures. If the battery management system (BMS) operates incorrectly or some anomalies appear, performance and security issues can be observed in LIBs. BMSs are also hard-programmed, have complex circuits, and have low computational resources, which limit the use of prognoses and diagnoses systems operating in real-time and embedded in the vehicle. Therefore, some technologies, such as edge and cloud computing, data-driven approaches, and machine learning (ML) models, can be applied to help the BMS manage the LIBs. Therefore, this work presents an edge–cloud computing system composed of two ML approaches (anomaly detection and failure classification) to identify the abuses in the LIBs in real-time. To validate the work, 36 NMC cells with a nominal capacity of 2200 mAh and voltage of 3.7 V were used to build the experiments segmented into three steps. Firstly, 12 experiments under failures were realized, which resulted in a high capacity loss. Then, the data were used to build both ML models. In the second step, the anomaly approach was applied to 12 cells observing the cells’ temperature anomalies. Then, the combination of IF and RF was applied to another 12 cells. The IF could reduce the capacity loss by about 45% when multiple abuses were applied to the cells. Despite that, this approach could not avoid some failures, such as overdischarging. Conversely, combining IF and RF could significantly reduce the capacity loss by 91% for the multiple abuses. The results concluded that ML could help the BMS identify failures in the first stage and reduce the capacity loss in LIBs.
Since the twenty-first century, with the rapid social and technological progress, issues of energy and environment have become increasingly prominent. With the continuous increase of car parc, vehicle emissions and corresponding energy consumption have gradually become part of the environment and energy issues that cannot be ignored. Battery electric vehicles are considered among the most suitable and promising vehicles for the future society because of their advantages of zero emission, zero pollution and low noise.
