Reuse and recycling of retired electric vehicle (EV) batteries offer a sustainable waste management approach but face decision-making challenges. Based on the process-based life cycle assessment method, we present a strategy to optimize pathways of retired battery treatments economically and environmentally. The strategy is applied to various reuse scenarios with capacity configurations, including energy storage systems, communication base stations, and low-speed vehicles. Hydrometallurgical, pyrometallurgical, and direct recycling considering battery residual values are evaluated at the end-of-life stage. For the optimized pathway, lithium iron phosphate (LFP) batteries improve profits by 58% and reduce emissions by 18% compared to hydrometallurgical recycling without reuse. Lithium nickel manganese cobalt oxide (NMC) batteries boost profit by 19% and reduce emissions by 18%. Despite NMC batteries exhibiting higher immediate recycling returns, LFP batteries provide superior long-term benefits through reuse before recycling. Our strategy features an accessible evaluation framework for pinpointing optimal pathways of retired EV batteries.

Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems.

Rapid and accurate state of health (SOH) estimation of retired batteries is a crucial pretreatment for reuse and recycling. However, data-driven methods require exhaustive data curation under random SOH and state of charge (SOC) retirement conditions. Here, we show that the generative learning-assisted SOH estimation is promising in alleviating data scarcity and heterogeneity challenges, validated through a pulse injection dataset of 2700 retired lithium-ion battery samples, covering 3 cathode material types, 3 physical formats, 4 capacity designs, and 4 historical usages with 10 SOC levels. Using generated data, a regressor realizes accurate SOH estimations, with mean absolute percentage errors below 6% under unseen SOC. We predict that assuming uniform deployment of the proposed technique, this would save 4.9 billion USD in electricity costs and 35.8 billion kg CO₂ emissions by mitigating data curation costs for a 2030 worldwide battery retirement scenario. This paper highlights exploiting limited data for exploring extended data space using generative methods, given data can be time-consuming, expensive, and polluting to retrieve for many estimation and predictive tasks.