Littérature scientifique sur le sujet « Battery state-of-health »
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Articles de revues sur le sujet "Battery state-of-health"
Yang, Qingxia, Ke Ma, Liyou Xu, Lintao Song, Xiuqing Li et Yefei Li. « A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health ». Coatings 12, no 8 (24 juillet 2022) : 1047. http://dx.doi.org/10.3390/coatings12081047.
Texte intégralWei, Yupeng. « Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model ». Sensors 23, no 5 (26 février 2023) : 2587. http://dx.doi.org/10.3390/s23052587.
Texte intégralHuang, Kai, Yong-Fang Guo, Ming-Lang Tseng, Kuo-Jui Wu et Zhi-Gang Li. « A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach ». Applied Sciences 8, no 10 (2 octobre 2018) : 1803. http://dx.doi.org/10.3390/app8101803.
Texte intégralO. Hadi, Pradita, et Goro Fujita. « Battery Charge Control by State of Health Estimation ». Indonesian Journal of Electrical Engineering and Computer Science 5, no 3 (1 mars 2017) : 508. http://dx.doi.org/10.11591/ijeecs.v5.i3.pp508-514.
Texte intégralPatel, Nisarg. « A Review of State of Health and State of Charge Estimation Methods ». International Journal for Research in Applied Science and Engineering Technology 9, no 11 (30 novembre 2021) : 259–64. http://dx.doi.org/10.22214/ijraset.2021.38693.
Texte intégralBrunelli Lazzarin, Telles, et Ivo Barbi. « A system for state-of-health diagnosis of lead-acid batteries integrated with a battery charger ». Eletrônica de Potência 17, no 1 (1 février 2012) : 401–8. http://dx.doi.org/10.18618/rep.2012.1.401408.
Texte intégralZhang, Tao, Ningyuan Guo, Xiaoxia Sun, Jie Fan, Naifeng Yang, Junjie Song et Yuan Zou. « A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles ». Sustainability 13, no 9 (5 mai 2021) : 5166. http://dx.doi.org/10.3390/su13095166.
Texte intégralNuroldayeva, Gulzat, Yerkin Serik, Desmond Adair, Berik Uzakbaiuly et Zhumabay Bakenov. « State of Health Estimation Methods for Lithium-Ion Batteries ». International Journal of Energy Research 2023 (3 mars 2023) : 1–21. http://dx.doi.org/10.1155/2023/4297545.
Texte intégralYu, Zhilong, Na Liu, Yekai Zhang, Lihua Qi et Ran Li. « Battery SOH Prediction Based on Multi-Dimensional Health Indicators ». Batteries 9, no 2 (24 janvier 2023) : 80. http://dx.doi.org/10.3390/batteries9020080.
Texte intégralMei, Peng, Hamid Reza Karimi, Fei Chen, Shichun Yang, Cong Huang et Song Qiu. « A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health ». Sensors 22, no 23 (4 décembre 2022) : 9474. http://dx.doi.org/10.3390/s22239474.
Texte intégralThèses sur le sujet "Battery state-of-health"
Grube, Ryan J. « Automotive Battery State-of-Health Monitoring Methods ». Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1229787557.
Texte intégralSöderhielm, Camilla. « Investigation of Battery Parameters for Li-ion Battery State of Health Estimation ». Thesis, KTH, Kemiteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299432.
Texte intégralEnvironmental concerns associated with greenhouse gas emissions from conventional combustion engines have contributed to a transition towards electric mobility. In this transition, lithium-ion (Li-ion) batteries play an important part as an energy storage system. However, Li-ion batteries can pose a safety risk due to their reactive chemistry. The Swedish Armed Forces are approaching a transition towards electric mobility, therefore, understanding Li-ion battery behavior with regard to non-normal use and ageing is critical for safe military applications. This project aimed to identify and evaluate battery parameters (impedance, resistance, capacity and surface temperature) suitable for State of Health (SOH) estimation of Li-ion batteries in military applications. Furthermore, this project aimed to investigate the ambient temperature’s effect on battery parameters, and identify the battery’s end of life (EOL) based on battery parameter tracking. Commercial NMC/graphite Li-ion batteries were exposed to ageing through repeated charge and discharge cycles. A critical application was mimicked, where the batteries operated at 1C charge rate (4 A) and 2.5C discharge rate (10 A) between 100 % and 0 % state of charge, for up to 250 charge/discharge cycles. The ageing process was tracked through regular measurements of impedance, resistance, capacity and surface temperature. In order to investigate the ambient temperature’s effect on the investigated battery parameters, the batteries were aged at either 52 ± 3 °C, 21 ± 3 °C or −15 ± 3 °C. Impedance measured at 980 Hz was the most stable battery parameter with respect to variations in state of charge and temperature, and was therefore regarded as the most suitable parameter for SOH estimation with respect to flexibility. Measurements of resistance and capacity at given temperatures were likely reflecting electrochemical ageing phenomena more accurately, hence the most suitable battery parameters for SOH estimation with respect to accuracy. Tracking of surface temperature provided insufficient information for accurate estimation of the batteries SOH. Decreasing the ambient temperature from 21 °C to −15 °C had a major effect on capacity and resistance; the resistance increased and the capacity decreased, corresponding to a decrease in battery performance. With respect to capacity fade, neither of the batteries aged at 21 °C reached their EOL within 250 cycles, while batteries aged at 52 °C or −15 °C reached their EOL after 150–200 cycles. With respect to resistance, one battery kept at 21 °C reached their EOL after 200 cycles, all batteries kept at 52 °C reached their EOL after 150–200 cycles, and batteries kept at −15 °C reached their EOL between 200–250 cycles. Finally, with respect to impedance measured at 980 Hz, one battery kept at 21 °C reached their EOL after 200 cycles, one battery kept at 52 °C reached their EOL after 150 cycles, and batteries kept at −15 °C reached their EOL between 200–250 cycles.
Kerley, Ross Andrew. « Automotive Lead-Acid Battery State-of-Health Monitoring System ». Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/64870.
Texte intégralMaster of Science
Suozzo, Christopher. « Lead-Acid Battery Aging and State of Health Diagnosis ». The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1212002134.
Texte intégralSamolyk, Mateusz, et Jakub Sobczak. « Development of an algorithm for estimating Lead-Acid Battery State of Charge and State of Health ». Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2937.
Texte intégralI detta papper, är ett laddningstillstånd (SOC) och hälsotillstånd (SOH) skattningsmetod för blybatterier presenteras. I algoritmen mätningarna av batteriets polspänning, ström och temperatur används i processen för SOC beräkning. Avhandlingen är skriven i samarbete med Micropower AB. Algoritmen har utformats för att uppfylla de särskilda kraven för elektriska fordon: ett fel under 5% av SOC, computational enkelhet och möjligheten att genomföras i ett grundläggande programmeringsspråk. Den nuvarande metoden vid Micropower, Coulomb räkning, jämförs med en metod som presenteras av Chiasson och Vairamohan 2005 baserad på modifierad Thevein kretsen under laddning och urladdning av batteriet.
Salyer, Zachary M. « Identification of Optimal Fast Charging Control based on Battery State of Health ». The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587037951166857.
Texte intégralCordoba, Arenas Andrea Carolina. « Aging Propagation Modeling and State-of-Health Assessment in Advanced Battery Systems ». The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1385967836.
Texte intégralKlass, Verena. « Battery Health Estimation in Electric Vehicles ». Doctoral thesis, KTH, Tillämpad elektrokemi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-173544.
Texte intégralQC 20150914
Schmidt, Alexander Patrick [Verfasser]. « A Novel Electrochemical Battery Model For State Of Charge And State Of Health Estimation / Alexander Patrick Schmidt ». Aachen : Shaker, 2010. http://d-nb.info/1084536315/34.
Texte intégralHyun, Ji Hoon. « State of Health Estimation System for Lead-Acid Car Batteries Through Cranking Voltage Monitoring ». Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71796.
Texte intégralMaster of Science
Chapitres de livres sur le sujet "Battery state-of-health"
Fernandez, Carlos, Ji Wu, Lei Chen, Mingfang He, Peng Yu, Pu Ren, Shunli Wang, Xiao Yang, Siyu Jin et Yangtao Wang. « Battery State of Health Estimation ». Dans Multidimensional Lithium-Ion Battery Status Monitoring, 211–46. Boca Raton : CRC Press, 2022. http://dx.doi.org/10.1201/9781003333791-4.
Texte intégralFotouhi, Abbas, Karsten Propp, Daniel J. Auger et Stefano Longo. « State of Charge and State of Health Estimation Over the Battery Lifespan ». Dans Behaviour of Lithium-Ion Batteries in Electric Vehicles, 267–88. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69950-9_11.
Texte intégralSingh, Rupam, V. S. Bharath Kurukuru et Mohammed Ali Khan. « Data-Driven Model for State of Health Estimation of Lithium-Ion Battery ». Dans Computational Methods and Data Engineering, 279–93. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7907-3_21.
Texte intégralSaqli, K., H. Bouchareb, M. Oudghiri et N. K. M’Sirdi. « Critical Review of Ageing Mechanisms and State of Health Estimation Methods for Battery Performance ». Dans Sustainability in Energy and Buildings, 507–18. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9868-2_43.
Texte intégralSun, Bingxiang, Yuzhe Chen, Shichang Ma, Zhengtao Cui et Zhanguo Wang. « Estimating Contrast of State of Health for Lithium-Ion Battery Based on Accumulated Residual Energy ». Dans The Proceedings of the 9th Frontier Academic Forum of Electrical Engineering, 83–96. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6609-1_8.
Texte intégralSudhakar Reddy, Mandeddu, et M. Monisha. « A Survey on Battery State of Charge and State of Health Estimation Using Machine Learning and Deep Learning Techniques ». Dans Lecture Notes in Networks and Systems, 355–67. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6088-8_31.
Texte intégralLi, Fan, Yusheng Wang et Duzhi Wu. « Prognostics for State of Health Estimation of Battery System Under Uncertainty Based on Adaptive Learning Technique ». Dans Advances in Intelligent Systems and Computing, 313–24. Berlin, Heidelberg : Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47241-5_27.
Texte intégralSánchez, Luciano, José Otero, Manuela González, David Anseán et Inés Couso. « Online Estimation of the State of Health of a Rechargeable Battery Through Distal Learning of a Fuzzy Model ». Dans Advances in Intelligent Systems and Computing, 68–77. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20055-8_7.
Texte intégralWang, Shunli, Yongcun Fan, Daniel-Ioan Stroe, Carlos Fernandez, Chunmei Yu, Wen Cao et Zonghai Chen. « Battery state-of-health estimation methods ». Dans Battery System Modeling, 255–311. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-323-90472-8.00007-x.
Texte intégral« Battery State of Health Estimation ». Dans Advanced Battery Management Technologies for Electric Vehicles, 95–130. Chichester, UK : John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119481652.ch4.
Texte intégralActes de conférences sur le sujet "Battery state-of-health"
Bandong, Steven, Muhammad Ihsan et Endra Joelianto. « Chaotic Behavior of Battery State of Health ». Dans 2019 6th International Conference on Electric Vehicular Technology (ICEVT). IEEE, 2019. http://dx.doi.org/10.1109/icevt48285.2019.8993986.
Texte intégralZhu, Xuetao, Qiongbin Lin, Shi You, Sixiong Chen et Yiming Hong. « A Review of Battery State of Health Estimation ». Dans 2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG). IEEE, 2019. http://dx.doi.org/10.1109/igbsg.2019.8886281.
Texte intégralSarikurt, Turev, Murat Ceylan et Abdulkadir Balikci. « An analytical battery state of health estimation method ». Dans 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE). IEEE, 2014. http://dx.doi.org/10.1109/isie.2014.6864855.
Texte intégralRamadan, M. Nisvo, Bhisma A. Pramana, Adha Cahyadi et Oyas Wahyunggoro. « State of health estimation in lithium polymer battery ». Dans PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON SYNCHROTRON RADIATION INSTRUMENTATION – SRI2015. Author(s), 2016. http://dx.doi.org/10.1063/1.4958523.
Texte intégralTairov, Stanislav, et Luiz Carlos Stevanatto. « Impedance measurements for battery state of health monitoring ». Dans 2011 2nd International Conference on Control, Instrumentation, and Automation (ICCIA). IEEE, 2011. http://dx.doi.org/10.1109/icciautom.2011.6356634.
Texte intégralHe, Liang, Eugene Kim, Kang G. Shin, Guozhu Meng et Tian He. « Battery state-of-health estimation for mobile devices ». Dans ICCPS '17 : ACM/IEEE 8th International Conference on Cyber-Physical Systems. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3055004.3055018.
Texte intégralArif, Ameera, Muhammad Hassaan, Mujahid Abdullah, Ahmad Nadeem et Naveed Arshad. « Estimating Battery State of Health using Machine Learning ». Dans 2022 10th International Conference on Smart Grid and Clean Energy Technologies (ICSGCE). IEEE, 2022. http://dx.doi.org/10.1109/icsgce55997.2022.9953596.
Texte intégralNatella, D., et F. Vasca. « Battery State of Health Estimation via Reinforcement Learning ». Dans 2021 European Control Conference (ECC). IEEE, 2021. http://dx.doi.org/10.23919/ecc54610.2021.9655199.
Texte intégralGuo, Qi, Wei Qu, Haoran Deng, Xueyuan Zhang, Yi Li, Xiaowei Wang et Xiangwu Yan. « Estimation of electric vehicle battery state of health based on relative state of health evaluation ». Dans 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2017. http://dx.doi.org/10.1109/iaeac.2017.8054365.
Texte intégralBashash, Saeid, et Hosam K. Fathy. « Battery State of Health and Charge Estimation Using Polynomial Chaos Theory ». Dans ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-4088.
Texte intégral