PDF Archive search engine
Last database update: 18 September at 08:08 - Around 220000 files indexed.
Results for «estimation»:
Total: 2000 results - 0.042 seconds
2394-3661, Volume-4, Issue-6, June 2017 Robust Least Squares Dummy Variable Estimation Of Dynamic Panel Models In The Presence Of Outliers Okeke Joseph Uchenna, Okeke Evelyn Nkiruka, Obi Jude Chukwura Abstract— This research is focused on the consistent, robust least squares dummy variable (LSDVR) estimator which is predicated on the correction of the bias of the inconsistency of the least squares dummy variable estimator of the parameters of the dynamic panel data model, as an extension of earlier results.
22311963 ESTIMATION OF STRESS-STRENGTH MODEL FOR GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION USING RANKED SET SAMPLING M.
sparse inverse covariance Sparse inverse covariance with the graphical Lasso January 19, 2017 Sitbon Pascal Abstract This paper reviews the estimation of sparse graphical model by Lasso estimations and its implementation (Friedman et al., 2007).
2321-0869, Volume-1, Issue-8, October 2013 Function Point Analysis S.Sowmya, N.Vignesh Section III deals with Full Function points being used for estimation of real time software.
Translate Shark Human Translation Translate Shark-Human Translation Published by:
Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm 89%
Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm Article Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm Xiangwei Guo 1,2,3, Longyun Kang 1,2,*, Yuan Yao 1,2, Zhizhen Huang 1,2 and Wenbiao Li 1,2 New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China; email@example.com (X.G.); HeinzYao@outlook.com (Y.Y.); firstname.lastname@example.org (Z.H.); email@example.com (W.L.) 2 Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China 3 College of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China * Correspondence: firstname.lastname@example.org; Tel.: +86‐137‐2809‐8863 1 Academic Editor: Sheng S. Zhang Received: 6 October 2015; Accepted: 22 January 2016; Published: 8 February 2016 Abstract: An estimation of the power battery state of charge (SOC) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium‐ion power battery is used in an electric vehicle, the SOC displays a very strong time‐dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second‐order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm. Keywords: least square method with a forgetting factor; AUKF; joint estimation 1. Introduction In an electric vehicle, the power battery State of Charge (SOC), an important parameter of the battery state, is used to directly reflect the remaining capacity of the battery and provide a basis for the formulation of an optimal energy management strategy for the vehicle control system. An inaccurate SOC will result in a reduced performance of the vehicle and lead to potential damage to the battery system; therefore, it is critical to develop algorithms that can accurately estimate the battery SOC in