{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T05:02:25Z","timestamp":1789621345982,"version":"build-2803163510"},"reference-count":46,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005416","name":"The Research Council of Norway","doi-asserted-by":"publisher","award":["346347"],"award-info":[{"award-number":["346347"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]},{"award":["346347"],"award-info":[{"award-number":["346347"]}],"id":[{"id":"https:\/\/ror.org\/00epmv149","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Batteries"],"abstract":"<jats:p>Battery prognosis is a critical component of battery management systems, enabling the prediction of end of life (EoL) and remaining useful life (RUL). However, obtaining sufficiently large labelled datasets for data-driven prognosis is challenging because battery ageing experiments are time-consuming and expensive. To address this data scarcity, we propose a conditional generative adversarial network (GAN) framework for targeted synthetic battery-data generation, in which degradation regime is explicitly used as conditioning information. The framework generates samples from three degradation regions\u2014early, pre-knee, and post-knee\u2014and is investigated using two representations of the same underlying battery data: direct time series and Gramian Angular Fields (GAFs). The generated data are evaluated using representation-specific quantitative metrics together with qualitative distributional analyses. As an additional validation of synthetic-data utility, GAN-generated samples are incorporated as unlabelled data in a Mean Teacher semi-supervised EoL prediction framework. Across 10 matched random-seed runs, augmentation reduces the mean EoL prediction error for both representations. For the time-series workflow, MAE and RMSE decrease by 6.37% and 3.66%, respectively, while the GAF-based workflow shows larger reductions of 15.93% and 16.20%. The improvements in both metrics are statistically significant for the GAF-based workflow, and after augmentation no statistically significant difference is detected between the aggregate EoL prediction errors of the GAF and time-series-based models. These findings demonstrate the potential of targeted conditional GANs for battery-data augmentation and highlight GAF-based generation as a promising complementary approach to conventional time-series-based augmentation for battery prognosis.<\/jats:p>","DOI":"10.3390\/batteries12090358","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T15:54:19Z","timestamp":1789055659000},"page":"358","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Targeted Battery Degradation Data Augmentation: Comparison of Gramian Angular Fields and Time-Series Representations"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6383-1781","authenticated-orcid":false,"given":"Vamsi Krishna","family":"Garapati","sequence":"first","affiliation":[{"name":"Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), Gl\u00f8shaugen, 7034 Trondheim, Norway"},{"name":"Beyonder, Stokkamyrveien 30, 4313 Sandnes, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie","family":"Pires","sequence":"additional","affiliation":[{"name":"Beyonder, Stokkamyrveien 30, 4313 Sandnes, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanho","family":"Lee","sequence":"additional","affiliation":[{"name":"Beyonder, Stokkamyrveien 30, 4313 Sandnes, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1019-6655","authenticated-orcid":false,"given":"Jacob Joseph","family":"Lamb","sequence":"additional","affiliation":[{"name":"Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), Gl\u00f8shaugen, 7034 Trondheim, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115971","DOI":"10.1016\/j.rser.2025.115971","article-title":"Lithium-ion batteries and the future of sustainable energy: A comprehensive review","volume":"223","author":"Ngoy","year":"2025","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.joule.2019.11.018","article-title":"Battery Lifetime Prognostics","volume":"4","author":"Hu","year":"2020","journal-title":"Joule"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8200","DOI":"10.1039\/D1CP00359C","article-title":"Lithium ion battery degradation: What you need to know","volume":"23","author":"Edge","year":"2021","journal-title":"Phys. Chem. Chem. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114360","DOI":"10.1016\/j.apenergy.2019.114360","article-title":"Optimizing the operation of energy storage using a non-linear lithium-ion battery degradation model","volume":"261","author":"Maheshwari","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"082001","DOI":"10.1088\/1361-6501\/adfb97","article-title":"A comprehensive review of lithium-ion battery remaining useful life prediction: Methodologies, datasets, performance metrics, and future perspectives","volume":"36","author":"Xu","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"120248","DOI":"10.1016\/j.est.2025.120248","article-title":"Lithium-ion battery state of health estimation using a hybrid model with multiple health indicators","volume":"147","author":"Yang","year":"2026","journal-title":"J. Energy Storage"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112282","DOI":"10.1016\/j.rser.2022.112282","article-title":"Prognostics and health management of Lithium-ion battery using deep learning methods: A review","volume":"161","author":"Zhang","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Razavi-Far, R., Farajzadeh-Zanjani, M., Chakrabarti, S., and Saif, M. (2016). Data-driven prognostic techniques for estimation of the remaining useful life of lithium-ion batteries. Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, ON, Canada, IEEE.","DOI":"10.1109\/ICPHM.2016.7542870"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109285","DOI":"10.1016\/j.est.2023.109285","article-title":"Data-driven rapid lifetime prediction method for lithium-ion batteries under diverse fast charging protocols","volume":"74","author":"Chen","year":"2023","journal-title":"J. Energy Storage"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"234007","DOI":"10.1016\/j.jpowsour.2023.234007","article-title":"Online data-driven battery life prediction and quick classification based on partial charging data within 10 min","volume":"594","author":"Zhang","year":"2024","journal-title":"J. Power Sources"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"127585","DOI":"10.1016\/j.energy.2023.127585","article-title":"An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries","volume":"276","author":"Xu","year":"2023","journal-title":"Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"125501","DOI":"10.1016\/j.energy.2022.125501","article-title":"A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model","volume":"262","author":"Gu","year":"2023","journal-title":"Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4252","DOI":"10.1109\/TVT.2021.3071622","article-title":"Remaining useful life assessment for lithium-ion batteries using CNN-LSTM-DNN hybrid method","volume":"70","author":"Zraibi","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"122337","DOI":"10.1016\/j.est.2026.122337","article-title":"A review of data augmentation and evaluation for lithium-ion batteries from a temporal perspective","volume":"165","author":"Yao","year":"2026","journal-title":"J. Energy Storage"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2685","DOI":"10.1109\/TIE.2022.3170630","article-title":"State-of-health estimation with anomalous aging indicator detection of lithium-ion batteries using regression generative adversarial network","volume":"70","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"233472","DOI":"10.1016\/j.jpowsour.2023.233472","article-title":"A time-series Wasserstein GAN method for state-of-charge estimation of lithium-ion batteries","volume":"581","author":"Gu","year":"2023","journal-title":"J. Power Sources"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110281","DOI":"10.1016\/j.asoc.2023.110281","article-title":"A conditional generative adversarial network-based synthetic data augmentation technique for battery state-of-charge estimation","volume":"142","author":"Qiu","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110004","DOI":"10.1016\/j.est.2023.110004","article-title":"State of charge estimation for lithium-ion batteries based on data augmentation with generative adversarial network","volume":"80","author":"Hu","year":"2024","journal-title":"J. Energy Storage"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.jechem.2024.12.010","article-title":"A solution framework for the experimental data shortage problem of lithium-ion batteries: Generative adversarial network-based data augmentation for battery state estimation","volume":"103","author":"Sun","year":"2025","journal-title":"J. Energy Chem."},{"key":"ref_20","unstructured":"Wang, Z., and Oates, T. (2015). Imaging time-series to improve classification and imputation. Proceedings of the IJCAI\u201915: 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina, AAAI Press."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Das, O., Sui, X., Sanfilippo, F., and Rudra, S. (2025). Battery Time-Series Data Augmentation Using a GAF-Based Generative Framework. Proceedings of the IEEE EUROCON 2025-21st International Conference on Smart Technologies, Gdynia, Poland, IEEE.","DOI":"10.1109\/EUROCON64445.2025.11073369"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jechem.2025.08.035","article-title":"Battery SOH enhanced solution: Voltage reconstruction and image recognition response to loss of data scenarios","volume":"112","author":"Liu","year":"2026","journal-title":"J. Energy Chem."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10368","DOI":"10.1109\/TVT.2025.3545118","article-title":"Predicting automotive battery degradation trajectories using transformer with variational auto-encoder based data augmentation","volume":"74","author":"Xiang","year":"2025","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"237192","DOI":"10.1016\/j.jpowsour.2025.237192","article-title":"Enhanced fault detection in lithium-ion battery energy storage systems via transfer learning-based conditional GAN under limited data","volume":"645","author":"Yang","year":"2025","journal-title":"J. Power Sources"},{"key":"ref_25","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., and Weinberger, K. (2014). Generative Adversarial Nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, Curran Associates, Inc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.ins.2019.10.014","article-title":"Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification","volume":"512","author":"Zheng","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_27","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv."},{"key":"ref_28","first-page":"214","article-title":"Wasserstein Generative Adversarial Networks","volume":"Volume 70","author":"Precup","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning, PMLR, Sydney, Australia"},{"key":"ref_29","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Improved Training of Wasserstein GANs. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, Curran Associates, Inc."},{"key":"ref_30","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Modulating early visual processing by language. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, Curran Associates, Inc."},{"key":"ref_31","unstructured":"Miyato, T., and Koyama, M. (2018). cGANs with Projection Discriminator. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, Y., and He, K. (2018). Group Normalization. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, Computer Vision Foundation.","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref_33","unstructured":"Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (2018). Spectral Normalization for Generative Adversarial Networks. arXiv."},{"key":"ref_34","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., D\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). Time-series Generative Adversarial Networks. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada, Curran Associates, Inc."},{"key":"ref_35","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, Curran Associates, Inc."},{"key":"ref_36","unstructured":"Binkowski, M., Sutherland, D.J., Arbel, M., and Gretton, A. (2018). Demystifying MMD GANs. arXiv."},{"key":"ref_37","unstructured":"Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (2018). Assessing Generative Models via Precision and Recall. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, Curran Associates, Inc."},{"key":"ref_38","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., D\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). Improved Precision and Recall Metric for Assessing Generative Models. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada, Curran Associates, Inc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Seol, S., Yoon, J., Lee, J., and Kim, B. (2024). Metrics for evaluating synthetic time-series data of battery. Appl. Sci., 14.","DOI":"10.3390\/app14146088"},{"key":"ref_40","unstructured":"Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (2018). Are GANs Created Equal? A Large-Scale Study. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, Curran Associates, Inc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103329","DOI":"10.1016\/j.cviu.2021.103329","article-title":"Pros and cons of GAN evaluation measures: New developments","volume":"215","author":"Borji","year":"2022","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_42","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, Curran Associates, Inc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1038\/s41560-019-0356-8","article-title":"Data-driven prediction of battery cycle life before capacity degradation","volume":"4","author":"Severson","year":"2019","journal-title":"Nat. Energy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1038\/s41586-020-1994-5","article-title":"Closed-loop optimization of fast-charging protocols for batteries with machine learning","volume":"578","author":"Attia","year":"2020","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"015004","DOI":"10.1088\/3049-4761\/ae5067","article-title":"New algorithm for battery knee-onset identification and early prediction of end of life","volume":"2","author":"Garapati","year":"2026","journal-title":"Mach. Learn. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/PL00011669","article-title":"Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases","volume":"3","author":"Keogh","year":"2001","journal-title":"Knowl. Inf. Syst."}],"container-title":["Batteries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-0105\/12\/9\/358\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T04:15:16Z","timestamp":1789618516000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-0105\/12\/9\/358"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,10]]},"references-count":46,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["batteries12090358"],"URL":"https:\/\/doi.org\/10.3390\/batteries12090358","relation":{},"ISSN":["2313-0105"],"issn-type":[{"value":"2313-0105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,10]]}}}