Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data.However,generating such data,whether in the laboratory or the field,is time-and resource-intensive.Here,we pr...Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data.However,generating such data,whether in the laboratory or the field,is time-and resource-intensive.Here,we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need for invasive battery characterization,aiming to augment the datasets used by data-driven models for degradation prediction.We validate our method by evaluating the performance of both shallow and deep learning models using diverse datasets from laboratory and field applications.These datasets encompass various chemistries and realistic conditions,including cell-to-cell variations,measurement noise,varying chargedischarge conditions,and capacity recovery.Our results show that it is possible to reduce cell-testing efforts by at least 50%by substituting synthetic data into an existing dataset.This paper highlights the effectiveness of our synthetic data augmentation method in supplementing existing methodologies in battery health prognostics while dramatically reducing the expenditure of time and resources on battery aging experiments.展开更多
基金funding from the research project"SPEED"(03XP0585)funded by the German Federal Ministry of Education and Research(BMBF).
文摘Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data.However,generating such data,whether in the laboratory or the field,is time-and resource-intensive.Here,we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need for invasive battery characterization,aiming to augment the datasets used by data-driven models for degradation prediction.We validate our method by evaluating the performance of both shallow and deep learning models using diverse datasets from laboratory and field applications.These datasets encompass various chemistries and realistic conditions,including cell-to-cell variations,measurement noise,varying chargedischarge conditions,and capacity recovery.Our results show that it is possible to reduce cell-testing efforts by at least 50%by substituting synthetic data into an existing dataset.This paper highlights the effectiveness of our synthetic data augmentation method in supplementing existing methodologies in battery health prognostics while dramatically reducing the expenditure of time and resources on battery aging experiments.
