We consider the prediction algorithm and performance evaluation for prognostics and health management (PHM) problems, especially the prediction of remaining useful life (RUL) for the milling machine cutter and lithium ‐
ion battery. We modeled battery as a voltage source and internal resisters. By analyzing voltage change trend during discharge, we made the prediction of battery remain discharge time in one discharge cycle. By analyzing internal resistance change trend during multiple cycles, we were able to predict the battery remaining useful time during its life time. We showed that the battery rest profile is correlated with the RUL. Numerical results using the realistic battery aging data from NASA prognostics data repository yielded satisfactory performance for battery prognosis as measured by certain performance metrics. We built a battery test platform and simulated more usage pattern and verified the prediction algorithm. Prognostic performance metrics were used to compare different algorithms.
","abstract_html":"<p> <p>We consider the prediction algorithm and performance evaluation for prognostics and health management (PHM) problems, especially the prediction of remaining useful life (RUL) for the milling machine cutter and lithium ‐ </p> </p> <p> <p>ion battery. We modeled battery as a voltage source and internal resisters. By analyzing voltage change trend during discharge, we made the prediction of battery remain discharge time in one discharge cycle. By analyzing internal resistance change trend during multiple cycles, we were able to predict the battery remaining useful time during its life time. We showed that the battery rest profile is correlated with the RUL. Numerical results using the realistic battery aging data from NASA prognostics data repository yielded satisfactory performance for battery prognosis as measured by certain performance metrics. We built a battery test platform and simulated more usage pattern and verified the prediction algorithm. Prognostic performance metrics were used to compare different algorithms.</p> </p> <p> </p>","abstract_has_math":false,"creators":["Liu, Gang"],"institution":null,"degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Chen, Huimin","Jilkov, Vesselin","Li, X. Rong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-08-04T07:00:00Z","date_published":"2011-08-04T07:00:00Z","updated_at":"2026-07-24T05:28:10Z","subjects":["Prognostics and Health Management, Remaining Useful Life, Lithium‐ion Battery, Linear","Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uno.edu/td/456","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chen, Huimin","Jilkov, Vesselin","Li, X. Rong"]},{"key":"dc:creator","label":"Author","values":["Liu, Gang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Prognostics and Health Management, Remaining Useful Life, Lithium‐ion Battery, Linear","Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uno.edu/td/456"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["
We consider the prediction algorithm and performance evaluation for prognostics and health management (PHM) problems, especially the prediction of remaining useful life (RUL) for the milling machine cutter and lithium ‐
ion battery. We modeled battery as a voltage source and internal resisters. By analyzing voltage change trend during discharge, we made the prediction of battery remain discharge time in one discharge cycle. By analyzing internal resistance change trend during multiple cycles, we were able to predict the battery remaining useful time during its life time. We showed that the battery rest profile is correlated with the RUL. Numerical results using the realistic battery aging data from NASA prognostics data repository yielded satisfactory performance for battery prognosis as measured by certain performance metrics. We built a battery test platform and simulated more usage pattern and verified the prediction algorithm. Prognostic performance metrics were used to compare different algorithms.
"]},{"key":"dc:title","label":"Title","values":["A Study on Remaining Useful Life Prediction for Prognostic Applications"]}]}],"canonical_facts":{"dc:contributor":["Chen, Huimin","Jilkov, Vesselin","Li, X. Rong"],"dc:creator":["Liu, Gang"],"dc:description.abstract":["
We consider the prediction algorithm and performance evaluation for prognostics and health management (PHM) problems, especially the prediction of remaining useful life (RUL) for the milling machine cutter and lithium ‐
ion battery. We modeled battery as a voltage source and internal resisters. By analyzing voltage change trend during discharge, we made the prediction of battery remain discharge time in one discharge cycle. By analyzing internal resistance change trend during multiple cycles, we were able to predict the battery remaining useful time during its life time. We showed that the battery rest profile is correlated with the RUL. Numerical results using the realistic battery aging data from NASA prognostics data repository yielded satisfactory performance for battery prognosis as measured by certain performance metrics. We built a battery test platform and simulated more usage pattern and verified the prediction algorithm. Prognostic performance metrics were used to compare different algorithms.
"],"dc:identifier":["https://scholarworks.uno.edu/td/456"],"dc:subject":["Prognostics and Health Management, Remaining Useful Life, Lithium‐ion Battery, Linear","Electrical and Computer Engineering"],"dc:title":["A Study on Remaining Useful Life Prediction for Prognostic Applications"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T05:28:10Z"}