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Abstract 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.

Abstract Pollution sources release contaminants into water bodies via sewage outfalls (SOs). Using high-resolution images to interpret SOs is laborious and expensive because it needs specific knowledge and must be done by hand. Integrating unmanned aerial vehicles (UAVs) and deep learning technology could assist in constructing an automated effluent SOs detection tool by gaining specialized knowledge. Achieving this objective requires high-quality image datasets for model training and testing. However, there is no satisfactory dataset of SOs. This study presents a high-quality dataset named the images for sewage outfalls objective detection (iSOOD). The 10481 images in iSOOD were captured using UAVs and handheld cameras by individuals from the river basin in China. This study has carefully annotated these images to ensure accuracy and consistency. The iSOOD has undergone technical validation utilizing the YOLOv10 series objective detection model. Our study could provide high-quality SOs datasets for enhancing deep-learning models with UAVs to achieve efficient and intelligent river basin management.