Among the wide diversity of existing technologically mature batteries, lithium-ion (Li-ion) batteries have become popular because of their longevity, high energy density, high efficiency and lack of memory effect. Differential charging of cells with age has turned balancing management systems into an important research subject. This paper proposes a new battery management system (BMS) to improve the capacity usage and lifespan of large Li-ion battery packs and a new charging algorithm based on the traditional multistage method. The main advantages of the proposed system are its versatility and ability to implement different charging and balancing methods in a very accessible way. The combination of charging methods with balancing methods represents an evolution when compared with other works in the literature.

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Primary adrenal lymphoma (PAL) is a very rare type of non-Hodgkin’s lymphoma (NHL). Herein, we report a case of NHL of both adrenal glands in a 69-year-old man. The patient was admitted because of a 1-month history of B symptoms and symptomatic hypotension. Biochemical analysis showed normocytic normochromic anaemia, hyponatraemia, hyperkalaemia, and elevated lactate dehydrogenase, C-reactive protein and D-dimers. A computed tomography scan revealed bilateral enlargement of the adrenal glands. There was no evidence of endocrine adrenal dysfunction. The mass in the right adrenal gland was biopsied and histopathology identified a diffuse large B-cell lymphoma of the activated B-cell subtype. A positron emission tomography 18F-fluorodeoxyglucose scan showed intensive hypermetabolic lesions involving both adrenal glands, as well as other locations, with higher uptake in the adrenal glands. Taken together, these findings suggested the diagnosis of PAL. The patient responded favourably to debulking therapy and is currently undergoing chemotherapy.

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This paper shows the potential of artificial intelligence (AI) in Li-ion battery charging methods by introducing a new charging algorithm based on artificial neural networks (ANNs). The proposed charging algorithm is able to find an optimized charging current profile, through ANNs, considering the real-time conditions of the Li-ion batteries. To test and validate the proposed approach, a low-cost battery management system (BMS) was developed, supporting up to 168 cells in series and n cells in parallel. When compared with the multistage charging algorithm, the proposed charging algorithm revealed a shorter charging time (7.85%) and a smaller temperature increase (32.95%). Thus, the results show that the proposed algorithm based on AI is able to effectively charge and balance batteries and can be regarded as a subject of interest for future research.