{"id":980,"date":"2026-07-31T19:34:48","date_gmt":"2026-08-01T00:34:48","guid":{"rendered":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/?p=980"},"modified":"2026-07-31T19:34:51","modified_gmt":"2026-08-01T00:34:51","slug":"meghana-sudarshan-alexey-serov-casey-jones-surya-mitra-ayalasomayajula-r-edwin-garcia-vikas-tomar-data-driven-autoencoder-neural-network-for-onboard-bms-lithium-ion-battery-degradation-predicti","status":"publish","type":"post","link":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/2026\/07\/31\/meghana-sudarshan-alexey-serov-casey-jones-surya-mitra-ayalasomayajula-r-edwin-garcia-vikas-tomar-data-driven-autoencoder-neural-network-for-onboard-bms-lithium-ion-battery-degradation-predicti\/","title":{"rendered":"Meghana Sudarshan, Alexey Serov, Casey Jones, Surya Mitra Ayalasomayajula, R Edwin Garc\u00eda, Vikas Tomar “Data-driven autoencoder neural network for onboard BMS Lithium-ion battery degradation prediction.” Journal of Energy Storage. 82:110575, 2024."},"content":{"rendered":"\n

Meghana Sudarshan, Alexey Serov, Casey Jones, Surya Mitra Ayalasomayajula, R Edwin Garc\u00eda, Vikas Tomar “Data-driven autoencoder neural network for onboard BMS Lithium-ion battery degradation prediction.<\/em>” Journal of Energy Storage. <\/strong>82:110575, 2024. https:\/\/doi.org\/10.1016\/j.est.2024.110575<\/a><\/p>\n\n\n\n

Abstract<\/h2>\n\n\n\n

An\u00a0autoencoder<\/a>\u00a0based\u00a0neural network architecture<\/a>, CD-Net, is proposed to predict Lithium-ion\u00a0battery<\/a>\u00a0capacity degradation as a function of operation time as part of a\u00a0battery management system<\/a>. CD-Net’s generalization performance on various LIB cell chemistry is tested. The incorporation of cell chemistry in CD-Net leads to an improvement in the overall\u00a0battery<\/a>\u00a0capacity prediction accuracy of >2\u00a0% for LiNiMnCoO2<\/sub>cells, >5\u00a0% for LiNiCoAlO2<\/sub>\u00a0cells, and >12\u00a0% for LiFePO4<\/sub>\u00a0cells when compared to the similar ML models that do not incorporate cell chemistry information. A comparison of onboard battery health prediction using CD-Net against support vector regression, Bayesian regression, and\u00a0Gaussian<\/a>\u00a0process regression-based approaches shows that CD-Net has higher computational efficiency with <2\u00a0% of relative remaining useful life (RUL) prediction error in a no-cell chemistry information setting. In summary, our work presents a chemistry-independent\u00a0neural network<\/a>\u00a0model tailored specifically for onboard BMS applications, showcasing notable\u00a0predictive capabilities<\/a>\u00a0in the context of Lithium-ion battery health assessment.<\/p>\n","protected":false},"excerpt":{"rendered":"

Meghana Sudarshan, Alexey Serov, Casey Jones, Surya Mitra Ayalasomayajula, R Edwin Garc\u00eda,…<\/p>\n

Continue reading “Meghana Sudarshan, Alexey Serov, Casey Jones, Surya Mitra Ayalasomayajula, R Edwin Garc\u00eda, Vikas Tomar “Data-driven autoencoder neural network for onboard BMS Lithium-ion battery degradation prediction.” Journal of Energy Storage. 82:110575, 2024.”<\/span>…<\/a><\/div>\n
Continue reading \"Meghana Sudarshan, Alexey Serov, Casey Jones, Surya Mitra Ayalasomayajula, R Edwin Garc\u00eda, Vikas Tomar “Data-driven autoencoder neural network for onboard BMS Lithium-ion battery degradation prediction.” Journal of Energy Storage. 82:110575, 2024.\"<\/span>…<\/a><\/div>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[45],"tags":[9,74,81],"class_list":["post-980","post","type-post","status-publish","format-standard","hentry","category-papers","tag-batteries","tag-battery-degradation","tag-machine-learning","entry"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/peeeSR-fO","jetpack_likes_enabled":true,"jetpack-related-posts":[{"id":974,"url":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/2026\/07\/31\/casey-m-jones-meghana-sudarshan-r-edwin-garcia-vikas-tomar-direct-measurement-of-internal-temperatures-of-commercially-available-18650-lithium-ion-batteries-scientific-reports-131-14421-20\/","url_meta":{"origin":980,"position":0},"title":"Casey M Jones, Meghana Sudarshan, R Edwin Garc\u00eda, Vikas Tomar “Direct measurement of internal temperatures of commercially-available 18650 lithium-ion batteries.” Scientific Reports 13(1): 14421, 2023.","author":"redwing","date":"07\/31\/2026","format":false,"excerpt":"Casey M Jones, Meghana Sudarshan, R Edwin Garc\u00eda, Vikas Tomar \"Direct measurement of internal temperatures of commercially-available 18650 lithium-ion batteries.\" Scientific Reports 13(1): 14421, 2023. https:\/\/doi.org\/10.1038\/s41598-023-41718-w Abstract Direct access to internal temperature readings in lithium-ion batteries provides the opportunity to infer physical information to study the effects of increased heating,\u2026","rel":"","context":"In "Papers"","block_context":{"text":"Papers","link":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/category\/papers\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":978,"url":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/2026\/07\/31\/alfredo-sanjuan-a-surya-mitra-r-edwin-garcia-sei-coated-carbon-particles-electrochemomechanical-fracture-mechanisms-journal-of-the-electrochemical-society-1712-020529-2024\/","url_meta":{"origin":980,"position":1},"title":"Alfredo Sanjuan, A Surya Mitra, R Edwin Garc\u00eda “SEI-coated carbon particles: electrochemomechanical fracture mechanisms.” Journal of The Electrochemical Society. 171(2): 020529, 2024.","author":"redwing","date":"07\/31\/2026","format":false,"excerpt":"Alfredo Sanjuan, A Surya Mitra, R Edwin Garc\u00eda \"SEI-coated carbon particles: electrochemomechanical fracture mechanisms.\" Journal of The Electrochemical Society. 171(2): 020529, 2024.https:\/\/doi.org\/10.1149\/1945-7111\/ad1d92 Abstract By starting from fundamental physical principles, a generalized theoretical framework was developed to engineer the intercalation-induced mechanical degradation in SEI-coated carbon particles from the surrounding electrolyte in\u2026","rel":"","context":"In "Papers"","block_context":{"text":"Papers","link":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/category\/papers\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":948,"url":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/2022\/08\/17\/a-jana-s-mitra-s-das-w-c-chueh-m-z-bazant-r-edwin-garcia-physics-based-reduced-order-degradation-model-of-lithium-ion-batteries-journal-of-power-sources-545231900-2022\/","url_meta":{"origin":980,"position":2},"title":"A. Jana, S. Mitra, S. Das, W.C. Chueh, M.Z. Bazant, R. Edwin Garc\u00eda “Physics-based, reduced order degradation model of lithium-ion batteries.” Journal of Power Sources. 545:231900, (2022).","author":"redwing","date":"08\/17\/2022","format":false,"excerpt":"A. Jana, S. Mitra, S. Das, W.C. Chueh, M.Z. Bazant, R.Edwin Garc\u00eda \"Physics-based, reduced order degradation model of lithium-ion batteries.\" Journal of Power Sources. 545:231900, (2022). https:\/\/doi.org\/10.1016\/j.jpowsour.2022.231900 Abstract A physics-based, reduced order framework is developed to calculate the charge capacity loss contributions from spatially homogeneous and heterogeneous degradation mechanisms, chemomechanical\u2026","rel":"","context":"In "Papers"","block_context":{"text":"Papers","link":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/category\/papers\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":939,"url":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/2022\/08\/04\/y-sun-s-mitra-ayalasomayajula-a-deva-g-lin-r-edwin-garcia-artificial-intelligence-inferred-microstructural-properties-from-voltage-capacity-curves-scientific-reports-1213421\/","url_meta":{"origin":980,"position":3},"title":"Y. Sun, S. Mitra Ayalasomayajula, A. Deva, G. Lin & R. Edwin Garc\u00eda “Artificial intelligence inferred microstructural properties from voltage\u2013capacity curves.” Scientific Reports. 12:13421, 2022.","author":"redwing","date":"08\/04\/2022","format":false,"excerpt":"Y. Sun, S. Mitra Ayalasomayajula, A. Deva, G. Lin & R. Edwin Garc\u00eda \"Artificial intelligence inferred microstructural properties from voltage\u2013capacity curves.\" Scientific Reports. 12:13421, 2022. https:\/\/doi.org\/10.1038\/s41598-022-16942-5 Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains\u2026","rel":"","context":"In "Papers"","block_context":{"text":"Papers","link":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/category\/papers\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":811,"url":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/2019\/03\/21\/a-jana-g-m-shaver-r-edwin-garcia-physical-on-the-fly-capacity-degradation-prediction-of-linimncoo2-graphite-cells-journal-of-power-sources-422-2019-185-195\/","url_meta":{"origin":980,"position":4},"title":"A. Jana, G. M. Shaver, R. Edwin Garc\u00eda “Physical, on the fly, capacity degradation prediction of LiNiMnCoO2- graphite cells,” Journal of Power Sources. 422 (2019) 185\u2013195","author":"redwing","date":"03\/21\/2019","format":false,"excerpt":"A. Jana, G. M. Shaver, R. Edwin Garc\u00eda \"Physical, on the fly, capacity degradation prediction of LiNiMnCoO2- graphite cells,\" Journal of Power Sources. 422 (2019) 185\u2013195;\u00a0https:\/\/doi.org\/10.1016\/j.jpowsour.2019.02.073 abstract A physics-based, reduced order model was developed to describe the capacity degradation in LiNiMnCoO2- graphite cells. By starting from fundamental principles, the model\u2026","rel":"","context":"In "Papers"","block_context":{"text":"Papers","link":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/category\/papers\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":517,"url":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/2017\/11\/04\/x-jin-a-vora-v-hoshing-t-saha-g-shaver-re-garcia-o-wasynczuk-s-varigonda-physically-based-reduced-order-capacity-loss-model-for-graphite-anodes-in-li-ion-battery-cells-journal-of-powe\/","url_meta":{"origin":980,"position":5},"title":"X Jin, A Vora, V Hoshing, T Saha, G Shaver, RE Garc\u00eda, O Wasynczuk, S Varigonda “Physically-based reduced-order capacity loss model for graphite anodes in Li-ion battery cells.”\u00a0Journal of Power Sources, 342:750-761, 2017.","author":"redwing","date":"11\/04\/2017","format":false,"excerpt":"X Jin, A Vora, V Hoshing, T Saha, G Shaver, RE Garc\u00eda, O Wasynczuk, S Varigonda \"Physically-based reduced-order capacity loss model for graphite anodes in Li-ion battery cells.\"\u00a0Journal of Power Sources, 342:750-761, 2017. Abstract Physically-based Li-ion electrochemical cell models have been shown capable of predicting cell performance and degradation, but\u2026","rel":"","context":"In "Papers"","block_context":{"text":"Papers","link":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/category\/papers\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/wp-json\/wp\/v2\/posts\/980","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/wp-json\/wp\/v2\/comments?post=980"}],"version-history":[{"count":1,"href":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/wp-json\/wp\/v2\/posts\/980\/revisions"}],"predecessor-version":[{"id":981,"href":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/wp-json\/wp\/v2\/posts\/980\/revisions\/981"}],"wp:attachment":[{"href":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/wp-json\/wp\/v2\/media?parent=980"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/wp-json\/wp\/v2\/categories?post=980"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/engineering.purdue.edu\/ComputationalMaterials\/index.php\/wp-json\/wp\/v2\/tags?post=980"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}