Method and system for estimating battery state of charge using Gaussian process regression
A technology of battery status and charging status, which is applied in the direction of measuring electricity, measuring electrical variables, instruments, etc., and can solve problems such as complicated processes
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example 1
[0162] Example 1: Performance of GPR-based SoC estimation method
[0163] refer to Figure 11A , Figure 11B , Figure 11C as well as Figure 11D , these figures are about analyzing the performance of SoC estimation using GPR in terms of RMSE and MAE. specifically, Figure 11A to Figure 11C Actual SoC, estimated SoC values, and 95% confidence intervals are shown for the four covariance functions, where, Figure 11A Showing the squared exponent (SE) covariance function 1103, Figure 11B Showing the Matern covariance function 1106, Figure 11C shows a rational quadratic (RQ) covariance function 1109, and Figure 11D The sum 1111 of the Matern and RQ covariance functions is shown. Shaded areas indicate 95% confidence intervals. The corresponding RMSE and MAE values are listed in Table 1.
[0164]
[0165] Table 1
[0166] On inspection, SoC estimation performance appears to depend heavily on the choice of covariance function. For example, GPR using the SE covaria...
example 2
[0169] Example 2: Performance of SoC estimation method based on combination of GPR and Kalman filter
[0170] refer to Figure 12A , Figure 12B , Figure 12C as well as Figure 12D , these figures are about evaluating the performance of the method for SoC estimation based on the combination of GPR and Kalman filter, and compared to the above section, the output of GPR is fed into the Kalman filter. For example, Figure 12A to Figure 12C Plots showing actual SoC, estimated SoC values, and 95% confidence intervals for different covariance functions, where Figure 12A Showing the squared exponent (SE) covariance function 1204, Figure 12B Showing the Matern covariance function 1208, Figure 12C shows a rational quadratic (RQ) covariance function 1212, and Figure 12D The sum 1216 of the Matern and RQ covariance functions is shown. The resulting RMSE and MAE values are shown in Table 2.
[0171]
[0172] Table 2
[0173]In particular, the Kalman filter is an algori...
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