Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Jiangwei Shen"'
Autor:
Simin Wu, Zheng Chen, Shiquan Shen, Jiangwei Shen, Fengxiang Guo, Yonggang Liu, Yuanjian Zhang
Publikováno v:
IET Intelligent Transport Systems, Vol 17, Iss 8, Pp 1560-1574 (2023)
Abstract Vehicles in the platoon can sufficiently incorporate the information via V2X communication to plan ecological speed trajectories and pass the intersection smoothly. Most existing eco‐driving studies mainly focus on the optimal control of a
Externí odkaz:
https://doaj.org/article/a7ff35b3036d4757bbb3027e09773b6e
Autor:
Shiquan Shen, Shun Gao, Yonggang Liu, Yuanjian Zhang, Jiangwei Shen, Zheng Chen, Zhenzhen Lei
Publikováno v:
IEEE Access, Vol 10, Pp 131076-131089 (2022)
Plug-in hybrid electric vehicles (PHEVs) have been validated as a preferable solution to transportation due to its great advantages in fuel economy promotion, harmful emission reduction and mileage anxiety mitigation. While, designing an effective en
Externí odkaz:
https://doaj.org/article/dbca72be829642c885394ec73577e75a
Publikováno v:
STAR Protocols, Vol 3, Iss 2, Pp 101272- (2022)
Summary: Accurate estimates of State of Health (SoH) are critical for characterizing the aging of lithium-ion batteries. This protocol combines feature extraction and a representative machine learning algorithm (i.e., least-squares support vector mac
Externí odkaz:
https://doaj.org/article/a91bd83cd72b44c9a1e5c8a132fad7d1
Publikováno v:
IEEE Access, Vol 9, Pp 777-788 (2021)
In this paper, an improved online particle swarm optimization (PSO) is proposed to optimize the traditional search controller for improving the operating efficiency of the permanent magnet synchronous motor (PMSM). This algorithm combines the advanta
Externí odkaz:
https://doaj.org/article/2c76de2b8f7040e1aa0084f53dd9ce27
Publikováno v:
IEEE Access, Vol 8, Pp 28533-28547 (2020)
Precise estimation of state of health (SOH) are of great importance for proper operation of lithium-ion batteries equipped in electric vehicles. For real applications, it is however difficult to estimate battery SOH due to stochastic operation, which
Externí odkaz:
https://doaj.org/article/48f8060e509f4c29a4de18ae885d05a4
Publikováno v:
IEEE Access, Vol 8, Pp 13924-13936 (2020)
In this study, an implicit proportional-integral-based generalized predictive controller (PIGPC) is proposed to effectively control temperatures of industrial systems with time-varying delay. The controller is designed to optimize the target function
Externí odkaz:
https://doaj.org/article/91ec6cc24f6a4b34827525732ccdbdca
Publikováno v:
IEEE Access, Vol 8, Pp 172783-172798 (2020)
Capacity prediction of lithium-ion batteries represents an important function of battery management systems. Conventional machine learning-based methods for capacity prediction are inefficient to learn long-term dependencies during capacity degradati
Externí odkaz:
https://doaj.org/article/6b48ba759b2a4ad5aec19efb08512bcf
Publikováno v:
iScience, Vol 24, Iss 11, Pp 103265- (2021)
Summary: Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches h
Externí odkaz:
https://doaj.org/article/d9e93810ba434890864e31059f126034
Publikováno v:
IEEE Access, Vol 7, Pp 102662-102678 (2019)
This paper proposes a fusion model based on the autoregressive moving average (ARMA) model and Elman neural network (NN) to achieve accurate prediction for the state of health (SOH) of lithium-ion batteries. First, the voltage and capacity degradatio
Externí odkaz:
https://doaj.org/article/6a83314eae114d459b767d8b0fdb04b5
Publikováno v:
IEEE Access, Vol 6, Pp 33261-33274 (2018)
In this paper, a hierarchical energy management strategy is proposed to achieve optimal energy distribution in plug-in hybrid electric vehicles by dividing the energy management algorithm into two layers. Between two control layers, a novel velocity-
Externí odkaz:
https://doaj.org/article/46982b8a57c24ab69987646018911ba5