<p>Second-life Batteries are the alternative to retired lithium-ion batteries that can no longer supply energy for high-speed electric vehicles. Despite that, a second-life batteries pack comprises cells composed of different capacities and resistances, which can reduce the efficiency and security of the pack. Therefore, it requires a complex battery management system to equalize the batteries, transferring the energy from the vital cells to the weak cells. This study presented a literature review based on the Proknow-C methodology of Machine Learning applications to operate Battery Management System for second-life batteries. The results indicated using multicell to multicell to equalize all cells of a second-life batteries pack. In this way, a flyback, multi-winding, and Cuk converter are reasonable solutions. Flyback isolates the input and output using a transform, increasing cost and complexity. On the other hand, multi-winding is a low-cost flyback version but has problems in the balance, such as cell confusion. Besides that, the review presented trends, such as the decentralized BMS that can be used to equalize the cells in a pack individually, increasing the equalizer’s cost. Conversely, wireless management reduces the wires and elements, which can minimize the cost of the converter. Finally, only two works applied machine learning techniques to optimize the BMS management for second-life batteries. This opens the opportunity for investigating the application of machine learning for equalizing the second-life batteries.</p>