# Research on Brake Energy Recovery Strategy Based on Working Condition Identification
## 3 Working Condition Identification Model Construction  
### Data Pre-Processing  
In this study, the data collected came from the driver's random autonomous driving, which is random and more responsive to the road driving characteristics in the region and the driving needs of ordinary drivers. Some of the time-velocity information is shown in Figure 5.  
Figure 4: Cruise complete vehicle model. Figure 5: Partial velocity clip. It is particularly important to pre-process the data to ensure that they are accurate and reliable. In this study, the data were pre-processed as follows:  
When the data distortion was greater than 5 s, the distorted work segment was excluded, and for the part less than or equal to 5 s, the mean value was used to make up the difference. The formula for this is as follows:  
\[v_{t}=\frac{v_{t-1}+v_{t-2}}{2} \tag{12}\]  
where \(v_{t}\) is the velocity at the point of distortion; \(v_{t-1}\) is the velocity one second before the distortion; and \(v_{t-2}\) is the velocity two seconds before the distortion. The complete dataset after the complementary difference adjustment was subjected to wavelet filtering. According to the multi-scale transformation of wavelet filtering, the signal noise was classified into low- and high-frequency signals, and the noise was usually a high-frequency signal. The one-dimensional noise signal can be expressed as:  
\[s(t)=f(t)+\sigma\times e(t)\;\;t=0,1,2\ldots n \tag{13}\]  
where \(s(t)\) is the original signal; \(f(t)\) is the real signal; \(\sigma\) is the noise standard deviation; and \(e(t)\) is the noise. In the wavelet denoising process, the original signal was first decomposed, and then each layer was processed using thresholding; finally, the signal was reconstructed after each layer had been processed. The continuous wavelet transform for any function \(f(t)\) is:  
\[W_{f}(a,b)=|a|^{-\frac{1}{2}}\int_{0}^{R}f(t)\psi\frac{-t-b}{a}dt \tag{14}\]  
where \(a\) is the scaling factor; \(b\) is the translation factor that changes the displacement of the continuous wavelet; \(W_{f}(j,t)\) is the discrete wavelet transform of \(f(t)\); and the discretisation method is \(a=2^{j}\); \(b=k2^{j}\); \(j,k\in Z\), where \(Z\) is the set of integers. In this study, soft-threshold signal processing was chosen, and its expression is as follows:  
\[w\lambda=\left\{ \begin{array}{ll}[sign(w)](|w|-\lambda),&|w|\geq\lambda\\ 0,&|w|<\lambda\end{array} \right. \tag{15}\]  
where \(w\) is the  
driving condition time series were divided into kinematic segment libraries, the data from which were the basis for the working condition classification. The processed vehicle velocity was divided into kinematic segments, and the pre-screening process was as follows:  
Step 1: Due to the large timespan of the collected data, there were a large number of idle segments on the road; idle segments of >200 s were reduced to 200 s to meet the requirements of kinematic segments. Step 2: This paper focuses on the study of commercial vehicles; according to the actual situation, the acceleration and deceleration speed should be less than 5 m/s\({}^{2}\), so it was necessary to omit kinematic segments with acceleration and deceleration speeds greater than 5 m/s\({}^{2}\). The kinematic segment feature parameters can be used to describe themselves; however, due to certain correlations between the feature parameters, too many feature parameters would make the information redundant. Therefore, this study selected parameters based on relevant research [23; 24; 25]. The following were chosen as the characteristic parameters of kinematic segments: acceleration segment average acceleration (\(\overline{a}\)); deceleration segment average deceleration (\(\overline{d}\)); maximum deceleration (\(d_{max}\)); maximum acceleration (\(a_{max}\)); deceleration ratio (\(P_{d}\)); acceleration ratio (\(P_{a}\)); stopping ratio (\(P_{s}\)); motion ratio (\(P_{r}\)); average running velocity (\(\overline{v}_{run}\)); maximum velocity (\(v_{max}\)); and average velocity (\(\overline{v}\)).