Lithium-based battery packs consist of numerous battery cells which form an essential component of electric vehicles. Inadequate heat transfer creates various challenges to safety issues in these batteries. Hence, ass...
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Lithium-based battery packs consist of numerous battery cells which form an essential component of electric vehicles. Inadequate heat transfer creates various challenges to safety issues in these batteries. Hence, assessment of the thermal performance of battery packs is an integral part of the design phase. In this study, a numerical approach of the system is implemented using a three-dimensional cylindrical model of a lithium Manganese dioxide battery cell. The model incorporates the application of the metal foam of different geometrical parameters saturated with air for the thermal cooling of batteries. The model considers the cooling phenomenon during discharge process of a cell with a Crate of 1C respectively. The change in internal resistance of the cell with temperature during the process is estimated using the Generalized reducedgradient (GRG) algorithm. The system is considered to be adiabatic by maintaining a flow of liquid through convective tubes, to maintain a constant temperature. The Battery Thermal Management System design is proposed using the Ansys-Fluent software assuming finite volume analysis. The results are analyzed in terms of the maximum battery temperature attained and with the maximum surface temperature of the battery and the metal foams acquired during the process.
In this paper, an "Adaptive Receding Horizon Controller (ARHC)" is exemplified in the suboptimal control of a Furuta pendulum. A dynamic model of strongly overestimated inertia and friction parameters is use...
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ISBN:
(纸本)9781665444996
In this paper, an "Adaptive Receding Horizon Controller (ARHC)" is exemplified in the suboptimal control of a Furuta pendulum. A dynamic model of strongly overestimated inertia and friction parameters is used in an RHC controller to track the nominal trajectory under cost terms penalizing the control forces. The so obtained "optimized" trajectory is tracked by an adaptive controller that uses a realistic approximate dynamic model of the controlled system. Since the approximate and the actual model contain considerably smaller inertia and friction parameters than that used for optimization the cautiously optimized trajectory can be precisely tracked by the actual system without suffering from heavy force burdens. The adaptivity is guaranteed by a "Fixed Point Iteration"-based approach that in this manner easily can be combined with the mathematical framework of optimal controllers. Instead of using Lagrange multipliers, the optimization happens through explicitly applying the dynamic model in forward Euler integration. The operation of the method is exemplified via numerical simulations.
In this paper, we proposed an implementation of stochastic perturbation of reducedgradient and bisection (SPRGB) method for optimizing a non-convex differentiable function subject to linear equality constraints and n...
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In this paper, we proposed an implementation of stochastic perturbation of reducedgradient and bisection (SPRGB) method for optimizing a non-convex differentiable function subject to linear equality constraints and non-negativity bounds on the variables. In particular, at each iteration, we compute a search direction by reducedgradient, and optimal line search by bisection algorithm along this direction yields a decrease in the objective value. SPRGB method is desired to establish the global convergence of the algorithm. An implementation and tests of SPRGB algorithm are given, and some numerical results of large-scale problems are presented, which show the efficient of this approach.
This paper extends the direct sensitivity analysis of Shi and Lukas [2005, Sensitivity analysis of constrained linear L-1 regression: perturbations to response and predictor variables. Comput. Statist. Data Anal. 48, ...
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This paper extends the direct sensitivity analysis of Shi and Lukas [2005, Sensitivity analysis of constrained linear L-1 regression: perturbations to response and predictor variables. Comput. Statist. Data Anal. 48, 779-802] of linear L-1 (least absolute deviations) regression with linear equality and inequality constraints on the parameters. Using the same active set framework of the reduced gradient algorithm (RGA), we investigate the effect on the L-1 regression estimate of small perturbations to the constraints (constants and coefficients). It is shown that the constrained estimate is stable, but not uniformly stable, and in certain cases it is unchanged. We also consider the effect of addition and deletion of observations and determine conditions under which the estimate is unchanged. The results demonstrate the robustness of L-1 regression and provide useful diagnostic information about the influence of observations. Results characterizing the (possibly non-unique) solution set are also given. The sensitivity results are illustrated with numerical simulations on the problem of derivative estimation under a concavity constraint. (c) 2006 Elsevier B.V. All rights reserved.
The active set framework of the reduced gradient algorithm is used to develop a direct sensitivity analysis of linear L-1 (least absolute deviations) regression with linear equality and inequality constraints on the p...
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The active set framework of the reduced gradient algorithm is used to develop a direct sensitivity analysis of linear L-1 (least absolute deviations) regression with linear equality and inequality constraints on the parameters. We investigate the effect on the L-1 regression estimate of a perturbation to the values of the response or predictor variables. For observations with nonzero residuals, we find intervals for the values of the variables for which the estimate is unchanged. For observations with zero residuals, we find the change in the estimate due to a small perturbation to the variable value. The results provide practical diagnostic formulae. They quantify some robustness properties of constrained L, regression and show that it is stable. but not uniformly stable. The level of sensitivity to perturbations depends on the degree of collinearity in the model and, for predictor variables, also on how close the estimate is to being nonunique. The results are illustrated with numerical simulations on examples including curve fitting and derivative estimation using trigonometric series. (C) 2004 Elsevier B.V. All rights reserved.
An implementation of the reduced gradient algorithm is proposed to solve the linear L, estimation problem (least absolute deviations regression) with linear equality or inequality constraints, including rank deficient...
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An implementation of the reduced gradient algorithm is proposed to solve the linear L, estimation problem (least absolute deviations regression) with linear equality or inequality constraints, including rank deficient and degenerate cases. Degenerate points are treated by solving a derived L, problem to give a descent direction. The algorithm is a direct descent, active set method that is shown to be finite, It is geometrically motivated and simpler than the projected gradientalgorithm (PGA) of Bartels, Conn and Sinclair, which uses a penalty function approach for the constrained case. Computational experiments indicate that the proposed algorithm compares favourably, both in reliability and efficiency, to the PGA, to the algorithms ACM551 and AFK (which use an LP formulation of the L-1 problem) and to LPASL1 (which is based on the Huber approximation method of Madsen, Nielsen and Pinar). Although it is not as efficient as ACM552 (Barrodale-Roberts algorithm) on large scale unconstrained problems, it performs better on large scale problems with bounded variable constraints. (C) 2002 Elsevier Science B,V. All rights reserved.
The censored linear $l_1 $ approximation problem is to minimize the nonconvex piecewise linear function $F(x) = \sum _{i = 1}^m |y_i - \max (z_i ,x^T a_i )|$. The problem arises in regression models where the range o...
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The censored linear $l_1 $ approximation problem is to minimize the nonconvex piecewise linear function $F(x) = \sum _{i = 1}^m |y_i - \max (z_i ,x^T a_i )|$. The problem arises in regression models where the range of the dependent variable is restricted. Unlike the maximum likelihood and least squares estimators the censored $l_1 $ estimator provides a consistent estimator without an assumption that the errors are normally distributed.
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