In this paper, an improved artificial bee colony algorithm (IABC) for dynamic environment optimization has been proposed. As we compared the IABC with greedy algorithm (GA), Particle swarm optimization (PSO) and origi...
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ISBN:
(纸本)9783038351153
In this paper, an improved artificial bee colony algorithm (IABC) for dynamic environment optimization has been proposed. As we compared the IABC with greedy algorithm (GA), Particle swarm optimization (PSO) and original artificial bee colony algorithm (ABC), the result of dynamic function optimization shows that the IABC can obtain satisfactory solutions and good tracing performance for dynamicfunction in time.
The internal nonlinearity of the lithium-ion battery makes its mathematical modeling a big challenge. In this paper, a novel lithium-ion battery splice-electrochemical circuit polarization (S-ECP) model is proposed, w...
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The internal nonlinearity of the lithium-ion battery makes its mathematical modeling a big challenge. In this paper, a novel lithium-ion battery splice-electrochemical circuit polarization (S-ECP) model is proposed, which integrates the strengths of various lithium-ion battery models and refines the ohm and polarization characteristics of the electrochemical Nernst model and the differences in charge-discharge internal resistance. Moreover, by applying the one-sided limit to the discrete system, a multi-innovation least squares algorithm optimized based on the dynamicfunction (F-MILS) introduced to constrain the original innovation weight is put forward, which tackles the problem of large algorithm errors caused by huge changes in the system input. In order to evaluate the regulating ability of weight constraint factors, the relevant parameter values in the dynamicfunction are discussed as independent variables. Furthermore, parameters of the S-ECP model are identified online by HPPC experimental data combined with the multi-innovation least squares (MILS) algorithm ameliorated by the dynamicfunction, and the convergence speed of parameters in the identification process is analyzed. Finally, an urban dynamometer driving schedule experiment is carried out on the lithium-ion battery under more complex working conditions. It is revealed that the accuracy of F-MILS is 0.5% higher than that of unoptimized MILS, further confirming the accuracy of the S-ECP model and the superiority of the F-MILS algorithm. Highlights A novel lithium-ion battery splice-electrochemical circuit polarization (S-ECP) model is proposed, which integrates the strengths of various lithium-ion battery models and refines the ohm and polarization characteristics of the electrochemical Nernst model and the difference in charge-discharge internal resistance. By introducing a dynamicfunction to constrain the original innovation weight and taking the influence of noise on identification accuracy into
This paper explores the state estimation method of lithium-ion battery pack through theoretical analysis and experimental research. Combining the advantages of the empirical models of various electrochemical models, a...
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This paper explores the state estimation method of lithium-ion battery pack through theoretical analysis and experimental research. Combining the advantages of the empirical models of various electrochemical models, a new type of composite electrochemistry-dual circuit polarization (E-DCP) model is proposed to better reflect the dynamic performance of the power lithium-ion battery under the conditions of meeting its safe and reliable energy supply requirements. Using the multi-innovation least squares (MILS) algorithm to identify the parameters in the E-DCP model online, so that it has the characteristics of high data utilization efficiency and high parameter identification accuracy. The battery charge and discharge efficiency function is introduced to dynamically modify the battery capacity, and the dynamicfunction is used to improve the Kalman gain in the extended Kalman filter (EKF), a new type of based on dynamicfunction improvement and combined with actual capacity correction (FCDEKF) algorithm is applied to the estimation of battery pack operating characteristics, which solves the problem that the traditional EKF algorithm is difficult to estimate errors when the system input change rate is large. The experimental results of urban dynamometer driving schedule (UDDS) and complex charge-discharge cycle test show that the maximum error of terminal voltage does not exceed 0.04V, the accuracy is 99.05%, and the errors of MILS algorithm combined with FC-DEKF algorithm for SOC estimation are all within 1%. The proposed equivalent circuit modeling method and state estimation correction strategy provide a theoretical basis for the reliable application of high-power lithium-ion battery packs.
An improved Guo Tao algorithm （IGT algorithm） is proposed for solving complicated dynamic function optimization problems, and a functionoptimization benchmark problem with constrained condition and two dynamic para...
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An improved Guo Tao algorithm （IGT algorithm） is proposed for solving complicated dynamic function optimization problems, and a functionoptimization benchmark problem with constrained condition and two dynamic parameters has been designed. The results achieved by IGT algorithm have been compared with the results from the Guo Tao algorithm （GT algorithm）. It is shown that the new algorithm （IGT algorithm） provides better results. This preliminarily demonstrates the efficiency of the new algorithm in complicated dynamic environments.
This paper presents an evolutionary approach to the Inverse Kinematics problem. The Inverse Kinematics problem concerns finding the placement of a manipulator that satisfies certain conditions. In this paper apart fro...
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ISBN:
(纸本)9783642289415;9783642289422
This paper presents an evolutionary approach to the Inverse Kinematics problem. The Inverse Kinematics problem concerns finding the placement of a manipulator that satisfies certain conditions. In this paper apart from reaching the target point the manipulator is required to avoid a number of obstacles. The problem which we tackle is dynamic: the obstacles and the target point may be moving which necessitates the continuous update of the solution. The evolutionary algorithm used for this task is a modification of the Infeasibility Driven Evolutionary Algorithm (IDEA) augmented with a prediction mechanism based on the ARIMA model.
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