Efficient linear predictive model with short term features for lithium-ion batteries state of health estimation. Ang, E. Y. M. & Paw, Y. C. Journal of Energy Storage, 44:103409, December, 2021. ![Efficient linear predictive model with short term features for lithium-ion batteries state of health estimation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The need to predict the State of Health (SoH) of lithium-ion batteries accurately and efficiently is rising with growing use of such batteries in safety critical applications. In this paper, an intuitive and efficient predictive algorithm that can estimate the SoH of lithium-ion batteries with accuracy on par with more complex and computationally demanding models is presented. The predictive algorithm uses the battery's temperature and voltage time discharge profile to predict its current SoH with root mean square error (RMSE) of 1%. It is shown that the crux in achieving good prediction accuracy lies in data preprocessing, which are cleansing, normalization and retaining of key features that are rich in information from the raw measured data. Finally, a simplified version of the algorithm using only voltage time profiles for model training is proposed that provides less than 12% RMSE error, comparable with current state of the art. This algorithm can be easily implemented in most applications since only measured voltage data is required. Throughout this paper, the proposed algorithm is tested with publicly available dataset and comparison is done with existing literature results to benchmark the proposed algorithm performance.
@article{ang_efficient_2021,
title = {Efficient linear predictive model with short term features for lithium-ion batteries state of health estimation},
volume = {44},
issn = {2352-152X},
url = {https://www.sciencedirect.com/science/article/pii/S2352152X21010951},
doi = {10.1016/j.est.2021.103409},
abstract = {The need to predict the State of Health (SoH) of lithium-ion batteries accurately and efficiently is rising with growing use of such batteries in safety critical applications. In this paper, an intuitive and efficient predictive algorithm that can estimate the SoH of lithium-ion batteries with accuracy on par with more complex and computationally demanding models is presented. The predictive algorithm uses the battery's temperature and voltage time discharge profile to predict its current SoH with root mean square error (RMSE) of 1\%. It is shown that the crux in achieving good prediction accuracy lies in data preprocessing, which are cleansing, normalization and retaining of key features that are rich in information from the raw measured data. Finally, a simplified version of the algorithm using only voltage time profiles for model training is proposed that provides less than 12\% RMSE error, comparable with current state of the art. This algorithm can be easily implemented in most applications since only measured voltage data is required. Throughout this paper, the proposed algorithm is tested with publicly available dataset and comparison is done with existing literature results to benchmark the proposed algorithm performance.},
language = {en},
urldate = {2021-10-25},
journal = {Journal of Energy Storage},
author = {Ang, Elisa Y. M. and Paw, Yew Chai},
month = dec,
year = {2021},
keywords = {Battery state of health, Data analytics, Linear predictive model, Lithium-ion battery, Machine learning, Predictive maintenance},
pages = {103409},
}
Downloads: 0
{"_id":"xvxwSnehEABmninaH","bibbaseid":"ang-paw-efficientlinearpredictivemodelwithshorttermfeaturesforlithiumionbatteriesstateofhealthestimation-2021","author_short":["Ang, E. Y. M.","Paw, Y. C."],"bibdata":{"bibtype":"article","type":"article","title":"Efficient linear predictive model with short term features for lithium-ion batteries state of health estimation","volume":"44","issn":"2352-152X","url":"https://www.sciencedirect.com/science/article/pii/S2352152X21010951","doi":"10.1016/j.est.2021.103409","abstract":"The need to predict the State of Health (SoH) of lithium-ion batteries accurately and efficiently is rising with growing use of such batteries in safety critical applications. In this paper, an intuitive and efficient predictive algorithm that can estimate the SoH of lithium-ion batteries with accuracy on par with more complex and computationally demanding models is presented. The predictive algorithm uses the battery's temperature and voltage time discharge profile to predict its current SoH with root mean square error (RMSE) of 1%. It is shown that the crux in achieving good prediction accuracy lies in data preprocessing, which are cleansing, normalization and retaining of key features that are rich in information from the raw measured data. Finally, a simplified version of the algorithm using only voltage time profiles for model training is proposed that provides less than 12% RMSE error, comparable with current state of the art. This algorithm can be easily implemented in most applications since only measured voltage data is required. Throughout this paper, the proposed algorithm is tested with publicly available dataset and comparison is done with existing literature results to benchmark the proposed algorithm performance.","language":"en","urldate":"2021-10-25","journal":"Journal of Energy Storage","author":[{"propositions":[],"lastnames":["Ang"],"firstnames":["Elisa","Y.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Paw"],"firstnames":["Yew","Chai"],"suffixes":[]}],"month":"December","year":"2021","keywords":"Battery state of health, Data analytics, Linear predictive model, Lithium-ion battery, Machine learning, Predictive maintenance","pages":"103409","bibtex":"@article{ang_efficient_2021,\n\ttitle = {Efficient linear predictive model with short term features for lithium-ion batteries state of health estimation},\n\tvolume = {44},\n\tissn = {2352-152X},\n\turl = {https://www.sciencedirect.com/science/article/pii/S2352152X21010951},\n\tdoi = {10.1016/j.est.2021.103409},\n\tabstract = {The need to predict the State of Health (SoH) of lithium-ion batteries accurately and efficiently is rising with growing use of such batteries in safety critical applications. In this paper, an intuitive and efficient predictive algorithm that can estimate the SoH of lithium-ion batteries with accuracy on par with more complex and computationally demanding models is presented. The predictive algorithm uses the battery's temperature and voltage time discharge profile to predict its current SoH with root mean square error (RMSE) of 1\\%. It is shown that the crux in achieving good prediction accuracy lies in data preprocessing, which are cleansing, normalization and retaining of key features that are rich in information from the raw measured data. Finally, a simplified version of the algorithm using only voltage time profiles for model training is proposed that provides less than 12\\% RMSE error, comparable with current state of the art. This algorithm can be easily implemented in most applications since only measured voltage data is required. Throughout this paper, the proposed algorithm is tested with publicly available dataset and comparison is done with existing literature results to benchmark the proposed algorithm performance.},\n\tlanguage = {en},\n\turldate = {2021-10-25},\n\tjournal = {Journal of Energy Storage},\n\tauthor = {Ang, Elisa Y. M. and Paw, Yew Chai},\n\tmonth = dec,\n\tyear = {2021},\n\tkeywords = {Battery state of health, Data analytics, Linear predictive model, Lithium-ion battery, Machine learning, Predictive maintenance},\n\tpages = {103409},\n}\n\n\n\n","author_short":["Ang, E. Y. M.","Paw, Y. C."],"key":"ang_efficient_2021","id":"ang_efficient_2021","bibbaseid":"ang-paw-efficientlinearpredictivemodelwithshorttermfeaturesforlithiumionbatteriesstateofhealthestimation-2021","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S2352152X21010951"},"keyword":["Battery state of health","Data analytics","Linear predictive model","Lithium-ion battery","Machine learning","Predictive maintenance"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["XJ7Gu6aiNbAiJAjbw","XvjRDbrMBW2XJY3p9","3C6BKwtiX883bctx4","5THezwiL4FyF8mm4G","RktFJE9cDa98BRLZF","qpxPuYKLChgB7ox6D","PfM5iniYHEthCfQDH","SZvSgtLYdBsPSQ3NM","iwKepCrWBps7ojhDx"],"keywords":["battery state of health","data analytics","linear predictive model","lithium-ion battery","machine learning","predictive maintenance"],"search_terms":["efficient","linear","predictive","model","short","term","features","lithium","ion","batteries","state","health","estimation","ang","paw"],"title":"Efficient linear predictive model with short term features for lithium-ion batteries state of health estimation","year":2021}