Zobrazeno 1 - 10
of 3 857
pro vyhledávání: '"Battery management system"'
Autor:
Lorenzo Breglio, Arcangelo Fiordellisi, Giovanni Gasperini, Giulio Iodice, Denise Palermo, Manuela Tufo, Fabio Ursumando, Agostino Mele
Publikováno v:
Modelling, Vol 5, Iss 3, Pp 911-935 (2024)
This paper presents a novel integrated control architecture for automotive battery management systems (BMSs). The primary focus is on estimating the state of charge (SoC) and the state of health (SoH) of a battery pack made of sixteen parallel-connec
Externí odkaz:
https://doaj.org/article/e63df1b4c12b422f9d427f3c1be55dac
Publikováno v:
Energy Conversion and Economics, Vol 5, Iss 4, Pp 224-242 (2024)
Abstract Lithium‐ion battery state‐of‐health (SOH) monitoring is essential for maintaining the safety and reliability of electric vehicles and efficiency of energy storage systems. When the SOH of lithium‐ion batteries reaches the end‐of‐
Externí odkaz:
https://doaj.org/article/224b22945123474cb495fb4f891176a5
Publikováno v:
Izvestiâ Vysših Učebnyh Zavedenij i Ènergetičeskih ob Edinennij SNG. Ènergetika, Vol 67, Iss 3, Pp 209-227 (2024)
Due to the development of electric transport and the growth of “green” energy, electric energy storage systems (ESS) are increasingly being used in the world. The growth of the battery market in the last decade has been 20-30%. One of the ways to
Externí odkaz:
https://doaj.org/article/8bae4d12cbc24a20b47172846f45a41c
Publikováno v:
Kongzhi Yu Xinxi Jishu, Iss 3, Pp 53-58 (2024)
The all-vanadium redox flow technology has garnered significant attention in the energy storage field, due to its attributes of high safety, high reliability, environmental friendliness, and power-capacity decoupling. Within all-vanadium redox flow e
Externí odkaz:
https://doaj.org/article/af0078da2eda425ca63df357dcf37f6d
Autor:
Zibo Ye, Xingfeng Fu
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract In order to address the issue of suppressing thermal runaway (TR) in power battery, a thermal generation model for power batteries was established and then modified based on experimental data. On the basis of simulation calculations, a schem
Externí odkaz:
https://doaj.org/article/c49b42b8d50c4e67a754bb06c989eca2
Publikováno v:
International Journal of Thermofluids, Vol 24, Iss , Pp 100859- (2024)
Nowadays, electric vehicles (EVs) significantly affect transportation as they provide a more environmentally friendly alternative to traditional fossil-fueled automobiles. Electric vehicles, which depend on energy stored in batteries, significantly c
Externí odkaz:
https://doaj.org/article/bc139bdd7a884e4e81614b8f353e063d
Autor:
Jiaxuan Ren, Rassol Hamed Rasheed, Mohsen Bagheritabar, Hadeel Kareem Abdul-Redha, Mohammed Al-Bahrani, Sandeep Singh, Soheil Salahshour, D. Toghraie
Publikováno v:
Case Studies in Thermal Engineering, Vol 61, Iss , Pp 104987- (2024)
Maintaining a stable temperature within a battery is essential for optimizing the performance of battery thermal management systems. Phase change materials (PCMs) have demonstrated potential in achieving this stability. This study investigates the us
Externí odkaz:
https://doaj.org/article/72bde3a1170445468565ca4dac9a75c6
Publikováno v:
Results in Engineering, Vol 23, Iss , Pp 102532- (2024)
This study investigates the challenge of cell balancing in battery management systems (BMS) for lithium-ion batteries. Effective cell balancing is crucial for maximizing the usable capacity and lifespan of battery packs, which is essential for the wi
Externí odkaz:
https://doaj.org/article/66d87260b87a4663892b80b4142a828b
Publikováno v:
Elkha: Jurnal Teknik Elektro, Vol 16, Iss 1, Pp 8-14 (2024)
This study focuses on conceptualization and development of a battery management system (BMS) with two main functions, battery monitoring and management, in the context of brushless direct current motors (BLDCs). The main challenge in variable estimat
Externí odkaz:
https://doaj.org/article/4a061bcaac1a47058c0b12954101e51c
Publikováno v:
Systems and Soft Computing, Vol 6, Iss , Pp 200157- (2024)
The main goal in this research is to train various machine learning models to predict charging cycles in EV Electric Vehicles) battery systems. The considered models are gradient boosting, random forests, decision trees, and linear regression. Each o
Externí odkaz:
https://doaj.org/article/3d49368e75a741ae8fc2410731c3c27f