The accuracy of lithium-ion battery model is one of the most important factors that affects the applicability of power battery in electrical vehicles. Based on the traditional forgetting factor recursive least square ...
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The accuracy of lithium-ion battery model is one of the most important factors that affects the applicability of power battery in electrical vehicles. Based on the traditional forgetting factor recursive least square (FFRLS) method, the random noise should be subjected to the normal distribution of zero mean and zero covariance, which, however, is very difficult to be satisfied in practical application. In this paper, based on the first-order RC equivalent circuit model, the identification of lithium-ion battery model parameters is performed by using the set-membership identification algorithm with unknown but bounded noise. The model parameters are identified by the set-membership algorithm with the experimental data of UDDS test on the NCM battery module. Experiments and simulation results show that the new method can simulate the dynamics of battery well, it can keep terminal voltage error within 1%, alongside with the root mean square error(RMSE) improved up to 8% compared with the FFRLS, which verifies the feasibility and the effectiveness of the new method, as well as providing data support for accurate estimation of battery state. Copyright (C) 2018 Elsevier Ltd. All rights reserved.
The accuracy of lithium-ion battery model is one of the most important factors that affects the applicability of power battery in electrical vehicles. Based on the traditional forgetting factor recursive least square ...
详细信息
The accuracy of lithium-ion battery model is one of the most important factors that affects the applicability of power battery in electrical vehicles. Based on the traditional forgetting factor recursive least square (FFRLS) method, the random noise should be subjected to the normal distribution of zero mean and zero covariance, which, however, is very difficult to be satisfied in practical application. In this paper, based on the first-order RC equivalent circuit model, the identification of lithium-ion battery model parameters is performed by using the set-membership identification algorithm with unknown but bounded noise. The model parameters are identified by the set-membership algorithm with the experimental data of UDDS test on the NCM battery module. Experiments and simulation results show that the new method can simulate the dynamics of battery well, it can keep terminal voltage error within 1%, alongside with the root mean square error(RMSE) improved up to 8% compared with the FFRLS, which verifies the feasibility and the effectiveness of the new method, as well as providing data support for accurate estimation of battery state.
Nowadays, the use of adaptive filters plays an important role in multiple signal processing applications, such as active noise control, acoustic echo cancellers, system identifiers, channel equalizer, among others. Un...
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Nowadays, the use of adaptive filters plays an important role in multiple signal processing applications, such as active noise control, acoustic echo cancellers, system identifiers, channel equalizer, among others. Until date, many of the existing adaptive algorithms such as affine projection algorithms offer a high convergence speed. However, its computational cost is also high. Currently, several authors make extraordinary efforts to reduce its computational cost to be used in practical applications. In this paper, we propose a new set-membership affine projection algorithm based on the percentage change of the error signal and variable projection order (SMAP-PC-VO). Specifically, we propose two techniques to create this algorithm;1) the new algorithm uses an error bound, which is obtained by calcuting the percentage change of the error signal, to avoid the computation of the variance of additive noise, since in existing approaches this parameter determines the error bound. In practical applications, the computation of the variance of additive noise is infeasible since this signal is not available;2) we propose a new method to dynamically modify the projection order in the new algorithm. As a consequence, its computational cost is reduced. To demonstrate its performance, the proposed algorithm was successfully tested in different environments for system identification and active noise control for headphone applications. The simulation results demonstrate that the proposed algorithm presents good convergence properties. In addition, the proposed algorithm exhibits a low overall computational complexity.
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