{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T13:12:28Z","timestamp":1790169148413,"version":"4.1.0"},"reference-count":37,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52172365"],"award-info":[{"award-number":["52172365"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the era of big data, using big data to realize the online estimation of battery SOH has become possible. Traditional solutions based on theoretical models cannot take into account driving behavior and complicated environmental factors. In this paper, an approximate SOH degradation model based on real operating data and environmental temperature data of electric vehicles (EVs) collected with a big data platform is proposed. Firstly, the health indicators are extracted from the historical operating data, and the equivalent capacity at 25 \u00b0C is obtained based on the capacity\u2013temperature empirical formula and the capacity offset. Then, the attenuation rate during each charging and discharging process is calculated by combining the operating data and the environmental temperature. Finally, the long short-term memory (LSTM) neural network is used to learn the degradation trend of the battery and predict the future decline trend. The test results show that the proposed method has better performance.<\/jats:p>","DOI":"10.3390\/s22155762","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:15:26Z","timestamp":1659485726000},"page":"5762","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Data-Driven Approach to State of Health Estimation and Prediction for a Lithium-Ion Battery Pack of Electric Buses Based on Real-World Data"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4154-4763","authenticated-orcid":false,"given":"Nan","family":"Xu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China"},{"name":"Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu 610039, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fenglai","family":"Yue","sequence":"additional","affiliation":[{"name":"Vehicle Energy Efficiency and Carbon Emission Reduction Evaluation Laboratory, National New Energy Vehicle Technology Innovation Center, Beijing 100176, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5189","DOI":"10.1021\/acs.est.0c07030","article-title":"Circularity of Lithium-Ion Battery Materials in Electric Vehicles","volume":"55","author":"Dunn","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.energy.2016.01.062","article-title":"Large-scale deployment of electric taxis in Beijing: A real-world analysis","volume":"100","author":"Zou","year":"2016","journal-title":"Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113648","DOI":"10.1016\/j.apenergy.2019.113648","article-title":"Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks","volume":"254","author":"Hong","year":"2019","journal-title":"Appl. Energy."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105516","DOI":"10.1016\/j.ijepes.2019.105516","article-title":"Lithium-ion battery pack equalization based on charging voltage curves","volume":"115","author":"Song","year":"2020","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Salehen, P.M.W., Su\u2019ait, M.S., Razali, H., and Sopian, K. (2017, January 22\u201325). Development of battery management systems (BMS) for electric vehicles (EVs) in Malaysia. Proceedings of the MATEC Web Conference, Seoul, Korea.","DOI":"10.1063\/1.4981173"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1109\/TIA.2017.2756026","article-title":"Degradation behavior of lithium-ion batteries during calendar ageing\u2014The case of the internal resistance increase","volume":"54","author":"Stroe","year":"2017","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, Y.P., and Huan, M.H. (2016). A Method of Battery State of Health Prediction Based on AR-Particle Filter, SAE. SAE Technical Paper.","DOI":"10.4271\/2016-01-1212"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.apenergy.2018.03.053","article-title":"An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application","volume":"219","author":"Xiong","year":"2018","journal-title":"Appl. Energy."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wesskamp, P., Reitemeyer, S., and Melbert, J. (2017). Online Capacity Estimation for Automotive Lithium-Ion Cells Incorporating Temperature-Variation and Cell-Aging, SAE. SAE Technical Paper.","DOI":"10.4271\/2017-01-1191"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1016\/j.energy.2018.03.023","article-title":"Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries","volume":"150","author":"Zheng","year":"2018","journal-title":"Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.jpowsour.2015.09.033","article-title":"Modeling mechanical degradation in lithium-ion batteries during cycling: Solid electrolyte interphase fracture","volume":"300","author":"Laresgoiti","year":"2015","journal-title":"J. Power Sources."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.apenergy.2016.08.080","article-title":"Electric vehicle charging demand forecasting model based on big data technologies","volume":"183","author":"Arias","year":"2016","journal-title":"Appl. Energy."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1016\/j.jpowsour.2013.05.040","article-title":"A review on lithium-ion battery ageing mechanisms and estimations for automotive applications","volume":"241","author":"Deguilhem","year":"2013","journal-title":"J. Power Sources"},{"key":"ref_14","first-page":"52","article-title":"Machine Learning in Big Data","volume":"1","author":"Wang","year":"2016","journal-title":"Int. J. Adv. Appl. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cao, M.D., Zhang, T., Wang, Y., Zhang, S.H., and Liu, Y.J. (2020, January 16\u201318). A Deep Learning Method with Ensemble Learning for Capacity Estimation of Lithium-ion Battery. Proceedings of the 2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai), Shanghai, China.","DOI":"10.1109\/PHM-Shanghai49105.2020.9280981"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"660","DOI":"10.3390\/en12040660","article-title":"A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm","volume":"12","author":"Phattara","year":"2019","journal-title":"Energies"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dos Santos, S.R., Aranha, J.C.M.S., do Nascimento, T.C., Vieira, D., Junior, E.M.O., and Cerri, F. (2018). Study of Machine Learning Algorithms to State of Health Estimation of Iron Phosphate Lithium-Ion Battery Used in Fully Electric Vehicles, SAE. SAE Technical Paper.","DOI":"10.4271\/2018-36-0178"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.jpowsour.2016.07.065","article-title":"A novel state of health estimation method of Li-ion battery using group method of data handling","volume":"327","author":"Wu","year":"2016","journal-title":"J. Power Sources."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, J.C., Hou, J., and Zhang, Z.J. (2020, January 22\u201324). Online State-of-Health Estimation for the Lithium-Ion Battery Based on An LSTM Neural Network with Attention Mechanism. Proceedings of the 2020 Chinese Control and Decision Conference (CCDC), Hefei, China.","DOI":"10.1109\/CCDC49329.2020.9164547"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.measurement.2017.11.016","article-title":"A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity","volume":"116","author":"Chen","year":"2018","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102118","DOI":"10.1016\/j.est.2020.102118","article-title":"A health indicator extraction based on surface temperature for lithium-ion batteries remaining useful life prediction","volume":"34","author":"Feng","year":"2021","journal-title":"J. Energy Storage"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.energy.2017.12.144","article-title":"A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries","volume":"145","author":"Yang","year":"2018","journal-title":"Energy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5634","DOI":"10.1109\/TIE.2017.2782224","article-title":"Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression","volume":"65","author":"Wei","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4110","DOI":"10.1109\/TVT.2018.2864688","article-title":"Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles","volume":"68","author":"Xiong","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liang, K.Z., Zhang, Z.S., Liu, P., and Jiang, S.F. (2019). Data-driven ohmic resistance estimation of battery packs for electric vehicles. Energies, 12.","DOI":"10.3390\/en12244772"},{"key":"ref_26","unstructured":"She, C.Q., Zhang, L., Wang, Z.P., Sun, F.C., Liu, P., and Song, C.B. (2021). Battery State of Health Estimation Based on Incremental Capacity Analysis Method: Synthesizing from Cell-Level Test to Real-World Application. IEEE J. Emerg. Sel. Top. Power Electron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104584","DOI":"10.1016\/j.est.2022.104584","article-title":"Feature-based lithium-ion battery state of health estimation with artificial neural networks","volume":"50","author":"Driscoll","year":"2022","journal-title":"J. Energy Storage"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/TVT.2017.2751613","article-title":"The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles","volume":"67","author":"Shen","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"114019","DOI":"10.1016\/j.apenergy.2019.114019","article-title":"An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management","volume":"257","author":"Hu","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"101836","DOI":"10.1016\/j.est.2020.101836","article-title":"Intelligent state of health estimation for lithium-ion battery pack based on big data analysis","volume":"32","author":"Song","year":"2020","journal-title":"J. Energy Storage"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s40684-019-00034-3","article-title":"Numerical Simulation of Prismatic Lithium-Ion Battery Life Cycles Under a Wide Range of Temperature","volume":"6","author":"Kwon","year":"2019","journal-title":"Int. J. Precis. Eng. Manuf. Green Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.ress.2018.02.022","article-title":"State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble","volume":"174","author":"Yu","year":"2018","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"87178","DOI":"10.1109\/ACCESS.2019.2925468","article-title":"A neural-network-based method for RUL prediction and SOH monitoring of lithium-ion battery","volume":"7","author":"Qu","year":"2019","journal-title":"IEEE Access."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"125814","DOI":"10.1016\/j.jclepro.2021.125814","article-title":"Online accurate state of health estimation for battery systems on real-world electric vehicles with variable driving conditions considered","volume":"294","author":"Hong","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_35","unstructured":"Li, Z. (2011). Characterization Research of LiFeO4Batteries for Application on Pure Electric Vehicles. [Ph.D. Thesis, Tsinghua University]."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhao, G.Q., Zhang, G.H., Liu, Y.F., Zhang, B., and Hu, C. (2017, January 19\u201321). Lithium-ion battery remaining useful life prediction with deep belief network and relevance vector machine. Proceedings of the 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), Dallas, TX, USA.","DOI":"10.1109\/ICPHM.2017.7998298"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1109\/TVT.2018.2805189","article-title":"Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries","volume":"67","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Veh. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5762\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:01:09Z","timestamp":1760140869000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,2]]},"references-count":37,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155762"],"URL":"https:\/\/doi.org\/10.3390\/s22155762","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,2]]}}}