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.

This work proposes a Clustering Algorithm to improve the energy efficiency of Bluetooth Mesh networks. To further reduce the burden over the Cluster Heads, a Radio Duty Cycling algorithm that requires only a simple modification on the Bluetooth packet transmission logic is proposed. Computer simulations show that the radio duty cycling and clustering methods are effective in improving energy efficiency. It is observed that duty cycling provides a 78% improvement on the energy efficiency. In addition, simulations show that the proposed clustering technique is effective in controlling the excessive message replication that is inherent in flooding operation, which in turn have a positive impact on packet delivery ratio (PDR) and network scalability. Finally, it can be observed that the proposed clustering algorithm together with the proposed radio duty cycling algorithm can provide an improvement on the energy efficiency when compared to the baseline Bluetooth Mesh profile.

This paper presents a hybrid equalization (EQ) topology of lithium-ion batteries (LIB). Currently, LIBs are widely used for electric mobility due to their characteristics of high energy density and multiple recharge cycles. In an electric vehicle (EV), these batteries are connected in series and/or parallel until the engine reaches the voltage and energy capacity required. For LIBs to operate safely, a battery management system (BMS) is required. This system monitors and controls voltage, current, and temperature parameters. Among the various functions of a BMS, voltage equalization is of paramount importance for the safety and useful life of LIBs. There are two main voltage equalization techniques: passive and active. Passive equalization dissipates energy, and active equalization transfers energy between the LIBs. The passive has the advantage of being simple to implement; however, it has a longer equalization time and energy loss. Active is complex to implement but has fast equalization time and lower energy loss. This paper proposes the combination of these two techniques to implement simultaneously to control a pack of LIBs, equalizing voltage between stacks and at the cell level. For this purpose, a pack of LIBs was simulated with sixty-four cells connected in series and divided into eight stacks with eight battery cells each. The rated voltage of each cell is 3.7 V, with a capacity of 106 Ah. The total pack has a voltage of 236.8 V and 25 kW. Some LIBs were fitted with different SOC values to simulate an imbalance between cells. In the simulations, different topologies were evaluated: passive and active topology at the cell level and combined active and passive equalization at the pack level. Results are compared as a response time and state of charge (SOC) level. In addition, equalization topologies are applied in an EV model with the FTP75 conduction cycle. In this way, it is possible to evaluate the autonomy of each equalization technique simulated in this work. The hybrid topology active at the stack level and passive at the module level showed promising results in equalization time and autonomy compared with a purely active or passive equalization technique. This combination is a solution to achieve low EQ time and satisfactory SOC when compared to a strictly active or passive EQ.

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