Abstract
The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE can be correspondingly reduced by 60.41%, 47.26%, 23.04%. The results indicate that the two-layer fusion model proposed in this paper achieves better robustness and accuracy.
Highlights
In recent decades, a new energy technology, which has been rapidly developed and applied in the field of electric vehicles (EV) has attracted the attention of many countries such as China, the United States, Germany, the United Kingdom and Japan [1]
There is a lot of research that has discussed the supercapacitor models, but most of this has focused on the methods to improve the accuracy under a single model
Based on the data obtained from the fusion model, it is divided into 10 segments according to per 10% state of charge (SOC) under the urban dynamometer driving schedule (UDDS) to verify its errors
Summary
A new energy technology, which has been rapidly developed and applied in the field of electric vehicles (EV) has attracted the attention of many countries such as China, the United States, Germany, the United Kingdom and Japan [1]. Li et al proposed a fusion estimation method of SOC based on Gaussian process regression (GPR), which significantly improved the accuracy of the model [30]. Lyu et al proposed a data fusion model method to estimate battery capacity by local charging curve using Gaussian regression, and smoothing incremental capacity curve by local weighted scatter smoothing can effectively improve the model accuracy [32]. A two-layer fusion model based on a multi-model supercapacitor is proposed This fusion method adopts physical data fusion, including three fusion models: a fusion model based on SOC fragments, a fusion model based on a Bayesian algorithm [33], and fusion model based on residual normalization.
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