Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Ethelbert Ezemobi"'
Publikováno v:
Applied Sciences, Vol 12, Iss 6, p 2852 (2022)
Models based on steady-state maps estimate fuel consumption to be 2–8% lower than real experimental measured values. This is due to the fact that during transient phases, the engine consumes more fuel than in steady phases. Some literature has addr
Externí odkaz:
https://doaj.org/article/f969233b4d5b4c2c83bc46b5e0bc90ac
Publikováno v:
Energies, Vol 15, Iss 3, p 1234 (2022)
Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estima
Externí odkaz:
https://doaj.org/article/7c0c5b910c134fdc82d152f9ee113df7
Autor:
Ethelbert Ezemobi, Gulnora Yakhshilikova, Sanjarbek Ruzimov, Luis Miguel Castellanos, Andrea Tonoli
Publikováno v:
World Electric Vehicle Journal, Vol 13, Iss 2, p 33 (2022)
The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of c
Externí odkaz:
https://doaj.org/article/81692c4ca740479db1e35416007f661e
Publikováno v:
Applied Sciences, Vol 12, Iss 1, p 226 (2021)
Small capacity and passively cooled battery packs are widely used in mild hybrid electric vehicles (MHEV). In this regard, continuous usage of electric traction could cause thermal runaway of the battery, reducing its life and increasing the risk of
Externí odkaz:
https://doaj.org/article/1863c5b1662249dfaba17c9830b70eaa
Publikováno v:
Energies, Vol 14, Iss 8, p 2243 (2021)
The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning m
Externí odkaz:
https://doaj.org/article/71a059b5b36a4fe5a458adba8c97c2a3
Publikováno v:
Energies, Vol 14, Iss 2243, p 2243 (2021)
Energies; Volume 14; Issue 8; Pages: 2243
Energies; Volume 14; Issue 8; Pages: 2243
The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d18db1519d1617a3ac1ded5bdfb5bf52
http://hdl.handle.net/11583/2947894
http://hdl.handle.net/11583/2947894
Publikováno v:
Applied Sciences, Vol 12, Iss 226, p 226 (2022)
Applied Sciences; Volume 12; Issue 1; Pages: 226
Applied Sciences; Volume 12; Issue 1; Pages: 226
Small capacity and passively cooled battery packs are widely used in mild hybrid electric vehicles (MHEV). In this regard, continuous usage of electric traction could cause thermal runaway of the battery, reducing its life and increasing the risk of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcddb2398cf2616ac3c262811e70331e
http://hdl.handle.net/11583/2947896
http://hdl.handle.net/11583/2947896
Autor:
Shailesh Hegde, Nicola Amati, Stefano Feraco, Ethelbert Ezemobi, Angelo Bonfitto, Andrea Tonoli
Publikováno v:
2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE).
This paper presents an algorithm based on Artificial Neural Networks (ANNs) for the estimation of the State of Health (SOH) in Lithium batteries. The method exploits a feed-forward pattern recognition classifier trained with datasets collected at dif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e581d5af7ebc2247ff3197ae765923ee
https://doi.org/10.23919/eeta.2019.8804567
https://doi.org/10.23919/eeta.2019.8804567