Abstract When a lithium-ion battery’s state of health (SOH) falls below 80%, it must be retired from electric vehicle (EV) use and properly redirected. Two main options are available: recycling or repurposing for lower-demand applications. Since recycling remains costly and complex, repurposing is often the preferred option—resulting in what is known as a second-life battery (SLB). SLBs are potentially used in stationary energy storage systems or low-speed vehicles. However, SLBs require exceptional management to ensure safe operation, as their packs typically consist of heterogeneous batteries with varying characteristics. Therefore, intelligent control is essential for the charge/discharge process. Key indicators such as state of charge (SOC) and SOH are widely studied and critical for SLB operation. Additional indicators—state of energy (SOE), state of power (SOP), and state of temperature (SOT)—are also necessary for increasing the performance. Given the complexity and dynamic nature of SLB systems, advanced management strategies are needed, such as those based on machine learning (ML). In this work, ML was implemented using an ensemble learning (EL) approach to minimize overfitting and estimate the key parameters of SLBs. Thirty experiments were conducted on an SLB pack of four heterogeneous batteries to generate sufficient data for training and testing the EL models. The results showed low RMSE values for random forest and bagging models in estimating SOC, SOE, SOP, and SOT. This indicates that the approach is suitable for real-time SLB operation. Future work will improve SOP and SOT estimation and establish standard methods for calculating these parameters in SLBs.
Gotz et al. (2026) studied this question.