Optimal dispatch approach for second-life batteries considering degradation with online SoH estimation

In light of upcoming electric vehicle (EV) battery retirement issues, second-life batteries (SLBs) have received increasing attention for their ability to extend the life-span of existing batteries and postpone the manufacturing of new batteries. When compared with new batteries, the process by which SLBs degrade demands more attention as they are more vulnerable to external stress and more likely to suffer from physical collapses. Their dispatch approaches, especially in terms of degradation, are essential tools for investors and end -users to investigate their technical and economic viability. In this paper, an optimal dispatch approach considering degradation with online state of health (SoH) estimation is developed, which integrates SoH into the optimization as a time-varying parameter affecting the battery performance. This online SoH estimation model leverages the Kalman filter's estimation power to achieve higher accuracy by combining short-term estimation and long-term prediction results. Moreover, the heterogeneous characteristics among these retired batteries due to their diverse first-life usage patterns and working conditions are considered by assigning different initial values of SoH and degradation paths. Subsequently, the performance of the proposed approach, an alternative dispatch approach considering degradation with a state of charge (SoC) based model, and a dispatch approach with no degradation consideration were compared in the case study. The results show that the proposed approach can lead to less battery degradation and can save costs with the batteries operating in a complementary way (not charging/discharging uniformly) to improve energy balancing and energy arbitrage.