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SUMMARY & CONCLUSIONSThe growing demand for reliable and efficient battery systems has intensified the need for advanced Battery Management Systems (BMS) equipped with Prognostics and Health Management (PHM) capabilities. While BMS technologies have progressed significantly, substantial challenges remain in acquiring the depth and quality of information needed for effective PHM implementation. PHM methods rely on sensor data and system operation signatures to monitor health, detect anomalies, and forecast failures. However, many systems face limitations due to sparse data coverage and high deployment costs.Foundation models are expansive neural networks trained on massive datasets, designed to handle a broad spectrum of tasks across various fields. They present powerful opportunities for advancing data-driven approaches by offering flexible, and scalable solutions. These models can be fine-tuned for specific applications, helping to speed up estimations while significantly cutting down the time and effort needed to build AI-based systems. This paper introduces a novel and practical approach to battery prognostics by leveraging foundation models for Remaining Useful Life (RUL) prediction using a minimal set of sensor data. Specifically, it investigates how pre-trained Chronos models—based on transformer architectures—can be used in zero-shot or low-data scenarios to support battery health assessment. The results show that foundation models are capable of extracting meaningful temporal features from battery discharge cycles, even when paired with simple downstream predictors.The study is demonstrated on an available lithium-ion battery dataset published by NASA’s Prognostics Center of Excellence [1]. The data were collected using a custom-built battery testbed, where batteries were subjected to accelerated aging through repeated charge and discharge cycles. Testing continued until each battery reached its end-of-life, defined by a 30% reduction in nominal capacity.Following initial dataset exploration, two sensor measurements—voltage and temperature—were selected for the RUL prediction task. The dataset contains additional features, such as load voltage and current measurements, which do not provide meaningful insights for the objectives of this study. Although the analysis focuses exclusively on discharge cycles and these two variables, the proposed architecture is general and can be extended to include additional sensor inputs. In the modeling pipeline, each discharge cycle is independently processed by a Chronos encoder to generate fixed-length embeddings. These embeddings represent a transformed version of the original sensor data into an N-dimensional space, offering a more effective representation for classification and regression tasks. These embeddings are then passed to a simple prediction head trained to estimate the RUL associated with each cycle.The experimental results demonstrate strong predictive performance. The model achieves an R2 score of 0.90 and a Root Mean Square Error (RMSE) of 11.3 cycles, confirming that meaningful degradation patterns can be captured with limited input features and minimal model complexity. An R-squared value of 0.90 indicates that 90% of the variance in the target variable is explained by the model, reflecting a high level of accuracy. The RMSE of 11.3 cycles quantifies the average prediction error, meaning the model's predictions deviate from the actual values by about 11.3 cycles on average.In summary, this work highlights the promise of foundation models for enabling efficient, low-cost RUL prediction in battery systems. It demonstrates that even with minimal sensor coverage and a basic regression model, pretrained time-series embeddings can provide sufficient information for accurate PHM. Future research may explore integrating more sophisticated regressors, incorporating a broader range of sensor data, and addressing prediction challenges during the final stages of battery life to further improve model precision and robustness.