{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T11:13:51Z","timestamp":1767093231461,"version":"3.48.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"35-36","license":[{"start":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T00:00:00Z","timestamp":1603324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T00:00:00Z","timestamp":1603324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s00521-020-05430-4","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T14:03:07Z","timestamp":1603375387000},"page":"29077-29088","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Distributed computing based on AI algorithms in battery early warning and SoH prediction of the intelligent connected vehicles"],"prefix":"10.1007","volume":"37","author":[{"given":"Haibo","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Yaonan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Di","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Yougui","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,22]]},"reference":[{"issue":"4","key":"5430_CR1","doi-asserted-by":"publisher","first-page":"660","DOI":"10.3390\/en12040660","volume":"12","author":"P Khumprom","year":"2019","unstructured":"Khumprom P, Yodo N (2019) A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies 12(4):660","journal-title":"Energies"},{"issue":"4","key":"5430_CR2","doi-asserted-by":"publisher","first-page":"667","DOI":"10.3390\/en12040667","volume":"12","author":"F Adegbohun","year":"2019","unstructured":"Adegbohun F, Von Jouanne A, Lee KY (2019) Autonomous battery swapping system and methodologies of electric vehicles. Energies 12(4):667","journal-title":"Energies"},{"issue":"14","key":"5430_CR3","doi-asserted-by":"publisher","first-page":"2784","DOI":"10.3390\/en12142784","volume":"12","author":"L Li","year":"2019","unstructured":"Li L, Saldivar AAF, Bai Y, Li Y (2019) Battery remaining useful life prediction with inheritance particle filtering. Energies 12(14):2784","journal-title":"Energies"},{"key":"5430_CR4","doi-asserted-by":"publisher","first-page":"113381","DOI":"10.1016\/j.apenergy.2019.113381","volume":"251","author":"J Hong","year":"2019","unstructured":"Hong J, Wang Z, Yao Y (2019) Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks. Appl Energy 251:113381","journal-title":"Appl Energy"},{"issue":"16","key":"5430_CR5","doi-asserted-by":"publisher","first-page":"3122","DOI":"10.3390\/en12163122","volume":"12","author":"M Zeng","year":"2019","unstructured":"Zeng M, Zhang P, Yang Y, Xie C, Shi Y (2019) SOC and SOH joint estimation of the power batteries based on fuzzy unscented kalman filtering algorithm. Energies 12(16):3122","journal-title":"Energies"},{"issue":"3","key":"5430_CR6","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1109\/JSYST.2014.2306210","volume":"10","author":"S Wang","year":"2014","unstructured":"Wang S, Fan C, Hsu CH, Sun Q, Yang F (2014) A vertical handoff method via self-selection decision tree for internet of vehicles. IEEE Syst J 10(3):1183\u20131192","journal-title":"IEEE Syst J"},{"issue":"24","key":"5430_CR7","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.3390\/app9245391","volume":"9","author":"A Farzan Moghaddam","year":"2019","unstructured":"Farzan Moghaddam A, Van den Bossche A (2019) A smart high-voltage cell detecting and equalizing circuit for LiFePO4 batteries in electric vehicles. Appl Sci 9(24):5391","journal-title":"Appl Sci"},{"key":"5430_CR8","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1016\/j.compeleceng.2018.02.024","volume":"69","author":"RM Rizk-Allah","year":"2018","unstructured":"Rizk-Allah RM, Hassanien AE, Elhoseny M (2018) A multi-objective transportation model under neutrosophic environment. Comput Electr Eng 69:705\u2013719","journal-title":"Comput Electr Eng"},{"key":"5430_CR9","doi-asserted-by":"publisher","first-page":"114170","DOI":"10.1016\/j.apenergy.2019.114170","volume":"259","author":"Y Kang","year":"2020","unstructured":"Kang Y, Duan B, Zhou Z, Shang Y, Zhang C (2020) Online multi-fault detection and diagnosis for battery packs in electric vehicles. Appl Energy 259:114170","journal-title":"Appl Energy"},{"key":"5430_CR10","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.comnet.2018.07.001","volume":"144","author":"PM Kumar","year":"2018","unstructured":"Kumar PM, Manogaran G, Sundarasekar R, Chilamkurti N, Varatharajan R (2018) Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Comput Netw 144:154\u2013162","journal-title":"Comput Netw"},{"issue":"6","key":"5430_CR11","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.3390\/en11061490","volume":"11","author":"M Mathew","year":"2018","unstructured":"Mathew M, Janhunen S, Rashid M, Long F, Fowler M (2018) Comparative analysis of lithium-ion battery resistance estimation techniques for battery management systems. Energies 11(6):1490","journal-title":"Energies"},{"issue":"10","key":"5430_CR12","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.3390\/en10101503","volume":"10","author":"Z Gao","year":"2017","unstructured":"Gao Z, Chin CS, Chiew JHK, Jia J, Zhang C (2017) Design and implementation of a smart lithium-ion battery system with real-time fault diagnosis capability for electric vehicles. Energies 10(10):1503","journal-title":"Energies"},{"key":"5430_CR13","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.comnet.2019.01.027","volume":"152","author":"S Baskar","year":"2019","unstructured":"Baskar S, Periyanayagi S, Shakeel PM, Dhulipala VS (2019) An energy persistent range-dependent regulated transmission communication model for vehicular network applications. Comput Netw 152:144\u2013153","journal-title":"Comput Netw"},{"issue":"5","key":"5430_CR14","doi-asserted-by":"publisher","first-page":"72","DOI":"10.3390\/electronics7050072","volume":"7","author":"ZB Omariba","year":"2018","unstructured":"Omariba ZB, Zhang L, Sun D (2018) Review on health management system for lithium-ion batteries of electric vehicles. Electronics 7(5):72","journal-title":"Electronics"},{"key":"5430_CR15","doi-asserted-by":"publisher","unstructured":"Manogaran G, Shakeel PM, Priyan RV, Chilamkurti N, Srivas tava (2019) A Ant colony optimization-induced route optimization for enhancing driving range of electric vehicles. Int J Commun Syst, e3964. Early View https:\/\/doi.org\/10.1002\/dac.3964","DOI":"10.1002\/dac.3964"},{"issue":"1","key":"5430_CR16","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3390\/en11010118","volume":"11","author":"C Aksakal","year":"2018","unstructured":"Aksakal C, Sisman A (2018) On the compatibility of electric equivalent circuit models for enhanced flooded lead acid batteries based on electrochemical impedance spectroscopy. Energies 11(1):118","journal-title":"Energies"},{"issue":"2","key":"5430_CR17","first-page":"106","volume":"8","author":"A Appathurai","year":"2018","unstructured":"Appathurai A, Manogaran G, Chilamkurti N (2018) Trusted FPGA-based transport traffic inject, impersonate (I2) attacks beaconing in the Internet of Vehicles. IET Netw 8(2):106\u2013115","journal-title":"IET Netw"},{"key":"5430_CR18","doi-asserted-by":"crossref","unstructured":"Sarrafan K, Muttaqi K, Sutanto D(2020) Real-time State-of-charge tracking embedded in the advanced driver assistance system of electric vehicles. IEEE Trans Intell Veh, Early View","DOI":"10.1109\/TIV.2020.2973551"},{"key":"5430_CR19","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.measurement.2017.11.034","volume":"116","author":"X Li","year":"2018","unstructured":"Li X, Wang Z (2018) A novel fault diagnosis method for lithium-Ion battery packs of electric vehicles. Measurement 116:402\u2013411","journal-title":"Measurement"},{"key":"5430_CR20","doi-asserted-by":"publisher","first-page":"118506","DOI":"10.1016\/j.jclepro.2019.118506","volume":"242","author":"SL Wang","year":"2020","unstructured":"Wang SL, Stroe DI, Fernandez C, Xiong LY, Fan YC, Cao W (2020) A novel power state evaluation method for the lithium battery packs based on the improved external measurable parameter coupling model. J Clean Product 242:118506","journal-title":"J Clean Product"},{"issue":"9","key":"5430_CR21","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.3390\/app8091641","volume":"8","author":"I Cho","year":"2018","unstructured":"Cho I, Bae J, Park J, Lee J (2018) Experimental evaluation and prediction algorithm suggestion for determining SOC of lithium polymer battery in a parallel hybrid electric vehicle. Appl Sci 8(9):1641","journal-title":"Appl Sci"},{"issue":"4","key":"5430_CR22","first-page":"2645","volume":"63","author":"X Hu","year":"2015","unstructured":"Hu X, Jiang J, Cao D, Egardt B (2015) Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling. IEEE Trans Ind Electr 63(4):2645\u20132656","journal-title":"IEEE Trans Ind Electr"},{"key":"5430_CR23","doi-asserted-by":"crossref","unstructured":"Frendo O, Graf J, Gaertner N, Stuckenschmidt H (2020) Data-driven smart charging for heterogeneous electric vehicle fleets. Energy AI, 100007","DOI":"10.1016\/j.egyai.2020.100007"},{"key":"5430_CR24","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/978-3-030-32530-5_10","volume-title":"Internet of Things for Industry 4.0","author":"RS Kumar","year":"2020","unstructured":"Kumar RS, Rajesh PK, Nancy JJ, Abirami S, Murthy KV (2020) IoT-Based monitoring and management of electric vehicle charging systems for DC fast charging facility. In: Internet of Things for Industry 4.0, vol 12. Springer, Cham, pp 147\u2013159"},{"key":"5430_CR25","doi-asserted-by":"publisher","first-page":"114006","DOI":"10.1016\/j.apenergy.2019.114006","volume":"257","author":"L Yang","year":"2020","unstructured":"Yang L, Cai Y, Yang Y, Deng Z (2020) Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles. Appl Energy 257:114006. https:\/\/doi.org\/10.1016\/j.apenergy.2019.114006","journal-title":"Appl Energy"},{"key":"5430_CR26","doi-asserted-by":"crossref","unstructured":"Xu X, Wu D, Yang L, Zhang H, Liu G (2020) State estimation of lithium batteries for energy storage based on dual extended kalman filter. Math Probl Eng","DOI":"10.1155\/2020\/6096834"},{"issue":"4","key":"5430_CR27","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.renene.2015.02.007","volume":"80","author":"A Bouabdallah","year":"2015","unstructured":"Bouabdallah A, Olivier JC, Bourguet S, Machmoum M, Schaeffer E (2015) Safe sizing methodology applied to a stand-alone photovoltaic system. Renew Energy 80(4):266\u2013274","journal-title":"Renew Energy"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05430-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05430-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05430-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T11:48:02Z","timestamp":1766058482000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05430-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,22]]},"references-count":27,"journal-issue":{"issue":"35-36","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["5430"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05430-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2020,10,22]]},"assertion":[{"value":"18 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declared no potential conflicts of interest with respect to the research, authorship and\/or publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}