Retired batteries pose a significant current and future challenge for electric mobility due to their high cost and the need for a state of health (SOH) above 80% to supply energy efficiently. Recycling and alternative applications are the primary options for these batteries, with recycling still undergoing research as regards more efficient and cost-effective techniques. While advancements have been made, researchers are actively seeking improved methods. Repurposing retired batteries for lower-performance applications like stationary systems or low-speed vehicles is recommended. Second-life batteries (SLB) can be directly reused or reconstructed, with the latter involving the disassembly, measurement, and separation of cells based on their characteristics. The traditional measurement process, involving full charge and discharge cycles, is time-consuming. To address this, a Machine Learning (ML)-based SOH estimator is introduced in this work, offering the instant measurement and estimation of battery health without complete discharge. The results indicate that the model can accurately identify SOH within a nominal capacity range of 1400–2300 mAh, with a resolution near 45.70 mAh, in under five minutes of discharging. This innovative technique could be instrumental in selecting and assembling SLB packs.

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.

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.

Recently, the increased adoption of electric vehicles (EVs) has significantly demanded new energy storage systems (ESS) technologies. In this way, Lithium-ion batteries (LIB) are the mainstream technology for this application. Lithium presents several advantages compared with other chemicals because it can provide delivery energy for a long time, a long lifetime, and high density and capacity. The LIB comprises several cells connected in different configurations, such as parallel, series, or combinations. This variety of designs makes the monitoring control process more complex, complicating diagnosing and prognosis of abuses and failures. To observe these difficulties, this paper presents sixteen experiments of a mini-packing of four cells under the main abuses found in the LIB. The time series data were collected during the abuses and saved in a CSV file. The results indicated that the current, temperature, and voltage should be used to identify the external short-circuit (ESC) failures in the packing of batteries. On the other side, only the voltage signature is able to determine the Over-Charging (OC), and finally, the combination of temperature and voltage should be used to identify and locate the Over-Discharging (OD) failures in different arrangements of packing. This study also provides ways to build mechanisms to protect the cells and avoid loss of performance and safety issues.

Battery thermal management systems (BTMS) are crucial for regulating temperature in lithium-ion battery packs, especially in high-power applications like electric vehicles. This study presents a multi-objective optimization of a 1P7S LFP prismatic battery pack using phase change material (PCM) as a BTMS. Previous research has primarily focused on reducing maximum temperature rise; this study addresses the challenge of minimizing inter-cell temperature differences. The pack’s thermal behavior is modeled under various conditions, utilizing advanced multiphysics simulations and efficient optimization algorithms. The approach considers multiple objectives: minimizing temperature rise, reducing temperature gradients, and optimizing PCM utilization. Results demonstrate the effectiveness of the optimized PCM-based BTMS in maintaining uniform temperature distribution across prismatic cells during high-power discharge. A Pareto front of optimal solutions is presented to balance thermal performance, system complexity, and cost. This research contributes to developing safer and more reliable battery systems for high-power applications.