# Two-Stream Networks for COPERT Correction Model with Time-Frequency Features Fusion

^{1}

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## Abstract

**:**

## 1. Introduction

_{2}and NOx emissions of National IV buses using remote on-board diagnostic (OBD) data. They established a calculation method for CO

_{2}and NOx emissions based on OBD data. In light of the swift progress in artificial intelligence technology, there has been a notable emergence of innovative theoretical perspectives and sophisticated technical approaches. These advancements have brought about substantial changes in the landscape of research and analysis across traditional industries. Recently, there has been a surge in scholarly research focusing on the online monitoring of OBD vehicle networks. Xu et al. [17] proposed a transfer learning-based approach for predicting mobile source pollution in the context of OBD pollution monitoring, specifically focusing on the impact of multiple external factors. The study utilized diesel vehicles as a case study and successfully achieved knowledge transfer across different vehicle models. Molina [18] employed random forest to determine the most influential driver variables based on the best attributes of the training model using OBD II data. Rivera-Campoverde et al. [19] addressed the challenge of estimating vehicle pollutant emission levels in the absence of an accurate model and limited measurement campaigns. They proposed a novel method for pollutant emission estimation by utilizing vehicle driving variables such as vehicle gear, engine speed and gas pedal position as inputs to a neural network model. The results of their approach closely aligned with those obtained from the IVE model and real driving emissions (RDE) test results. Wang et al. [20] proposed a coherent methodology that utilizes the OBD system to collect operational data from heavy-duty diesel vehicles. Subsequently, an artificial neural network is constructed to develop an emission prediction model. This approach enables real-time monitoring of the vehicle’s emission status, providing valuable insights for environmental protection and vehicle management purposes. Chen et al. [21] presented a methodology for gathering vehicle parameters, including speed, RPM, throttle position, engine load, etc., through the OBD interface. Subsequently, they employed the AdaBoost algorithm to classify driving behaviors, achieving an impressive accuracy rate of 99.8% across various driving scenarios.

## 2. Related Works

#### 2.1. COPERT

#### 2.2. Emission Factor Obtaining of NOx Based on OBD

_{1.86}O

_{0.006}; O

_{2}(%) which is the volumetric concentration of O

_{2}in the exhaust gas monitored by the OBD; ${\rho}_{air}$ is the density of ambient air at 0 °C and 101.3 kPa. The value is 1.293 kg/m

^{3}; ${\rho}_{exh}$ for the exhaust density in kg/m

^{3}, according to GB17691-2018 [30]. The exhaust density of heavy-duty vehicles burning diesel is 1.2943 kg/m

^{3}.

#### 2.3. ResNet

#### 2.4. Attention in CNN

#### 2.5. Continuous Wavelet Transform

## 3. Methodology

#### 3.1. Data Description

#### 3.2. Data Processing

#### 3.2.1. Driving Segment Division

#### 3.2.2. Obtaining NOx Emission Factors

#### 3.3. Screening of Correlation Factors

#### 3.4. Two-Stream Model Based on Historical Information

#### 3.4.1. Historical Information Matrix Construction

#### 3.4.2. CWT

#### 3.4.3. Two Stream

## 4. Experiments

#### 4.1. Historical Information Step Setting

#### 4.2. Setting of Correlation Data Volume

#### 4.3. Ablation Experiments

- (a)
- ResNet50: single-stream resnet50 with the historical information matrix as input.
- (b)
- ResNet50 (CBAM): Resnet50 of a single stream combined with CBAM, with the historical information matrix as input.
- (c)
- HI_TTFTS (without CBAM): two-stream model composed of two resnet50, the two-stream inputs are the historical information matrix and the time-frequency matrix, respectively.
- (d)
- HI_TTFTS (left): two-stream model composed of two resnet50, left combined with CBAM, the left input is the historical information matrix and the right input is the time-frequency matrix.
- (e)
- HI_TTFTS (right): two-stream model composed of two resnet50, right combines CBAM, the left input is the historical information matrix, and the right input is the time-frequency matrix.
- (f)
- HI_TTFTS (all): two-stream model composed of two resnet50, respectively, with CBAM, the left input is the historical information matrix and the right input is the time-frequency matrix.

#### 4.4. Comparative Experiment

- (a)
- Support Vector Regression (SVR): only consider the influence of each attribute on the emission factor at the current moment, the input data is only the combination of each attribute and EF${}_{COPERT}$ at the current moment.
- (b)
- Artificial Neural Network (ANN): only consider the influence of each attribute on the emission factor at the current moment, the input data is only the combination of each attribute and EF${}_{COPERT}$ at the current moment.
- (c)
- Convolutional Neural Network (CNN): A general CNN model with time-frequency matrix as input.
- (d)
- TF: ResNet50 model with time-frequency matrix as input.
- (e)
- TSFF: Each column of the historical information matrix is pieced together into a 6*8 matrix [44] and multi-channel superposition is performed with dual-stream inputs of the historical information matrix and the constructed 2D construction matrix, respectively, to a parallel structure combining ResNet50 and CBAM.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Characterization Parameters | Model | Application Scale | Calculation Method | Data Source | Parameter Numbers |
---|---|---|---|---|---|

Average speed | MOBILE [2] | Macro, Meso | Statistical Regression | Bench Test | 27 |

COPERT [4] | Macro, Meso | Statistical Regression | Bench Test | 15 | |

EMFAC [3] | Macro, Meso | Statistical Regression | Bench Test | 16 | |

VSP | IVE [5] | Macro, Meso | Statistical Regression | Bench Test and On-road Emission Test | 19 |

Engine power requirement | CMEM [6] | Macro, Meso, Microscopic | Physical Modeling | On-road Emission Test | 47 |

Average speed, VSP, Traffic volume | MOVES [7] | Macro, Meso, Microscopic | Statistical Regression | Bench Test and On-road Emission Test | - |

**Table 2.**Coefficients of COPERT model [27].

Coefficients | Euro 1 | Euro 2 | Euro 3 | Euro 4 | Euro 5 |
---|---|---|---|---|---|

a | $5.25\times {10}^{-1}$ | $2.84\times {10}^{-1}$ | $9.24\times {10}^{-2}$ | $1.06\times {10}^{-1}$ | $1.89\times {10}^{-1}$ |

b | / | $-2.34\times {10}^{-2}$ | $-1.22\times {10}^{-2}$ | / | $1.57$ |

c | $-1.00\times {10}^{-2}$ | $-8.69\times {10}^{-3}$ | $-1.49\times {10}^{-3}$ | $-1.58\times {10}^{-3}$ | $8.15\times {10}^{-2}$ |

d | / | $4.43\times {10}^{-4}$ | $3.97\times {10}^{-5}$ | / | $2.73\times {10}^{-2}$ |

e | $9.36\times {10}^{-5}$ | $1.14\times {10}^{-4}$ | $6.53\times {10}^{-6}$ | $7.10\times {10}^{-6}$ | $-2.49\times {10}^{-4}$ |

f | / | / | / | / | $-2.68\times {10}^{-1}$ |

Stage 0 | 7 $\times \phantom{\rule{3.33333pt}{0ex}}7$, 64, strider 2 |

3 $\times \phantom{\rule{3.33333pt}{0ex}}3$ max pool, stride 2 | |

Stage 1 | $\left[\begin{array}{c}1\times 1,64\\ 3\times 3,64\\ 1\times 1,256\end{array}\right]\times 3$ |

Stage 2 | $\left[\begin{array}{c}1\times 1,128\\ 3\times 3,128\\ 1\times 1,512\end{array}\right]\times 4$ |

Stage 3 | $\left[\begin{array}{c}1\times 1,256\\ 3\times 3,256\\ 1\times 1,1024\end{array}\right]\times 6$ |

Stage 4 | $\left[\begin{array}{c}1\times 1,512\\ 3\times 3,512\\ 1\times 1,2048\end{array}\right]\times 3$ |

Fuel Type | Diesel Fuel |
---|---|

Displacement | 11.596 L |

Maximum Horsepower | 375 PS |

Maximum Torque | 1800 N· m |

Rated Rotation Speed | 2100 rpm |

Engine Type | WP12.375E51 |

Brand | WEICHAI |

Sampling Interval | 5 s |

Time Period | 8 June 2020–29 November 2020 |

Number of Sampling | 27,562 |

Name | Label | Name | Label |
---|---|---|---|

Engine speed | E${}_{speed}$ | Actual output torque percentage | AOTP |

Engine water temperature | EWT | Engine fuel temperature | EFT |

Post-treatment downstream NOx | NOx | Post-treatment downstream oxygen | O2 |

Atmospheric pressure | AP | Environmental temperature | ET |

Post-treatment exhaust gas mass flow rate | PT${}_{EGM}$ | Urea tank level | UTL |

Urea tank temperature | UTT | Vehicle speed | V${}_{speed}$ |

Gas peddal opening | GPO | Single Driving Miles | SDM |

Engine fuel consumption rate (instantaneous) | EFCR | Average Engine fuel consumption rate | EFCR${}_{avg}$ |

Engine fuel consumption for single driving | EFC${}_{SD}$ | Total engine fuel consumption | EFC${}_{total}$ |

Battery voltage | BV | Fuel tank level | FTL |

Cumulative engine runtime | CER | Longitude | LNG |

Latitude | LAT | / | / |

Metrics | MAE | MAPE | RMSE | |
---|---|---|---|---|

K | ||||

12 | 0.0173 | 19.90% | 0.0292 | |

24 | 0.0112 | 11.49% | 0.0187 | |

36 | 0.0101 | 13.27% | 0.0177 | |

48 | 0.0086 | 8.23% | 0.0159 | |

60 | 0.0092 | 10.96% | 0.0181 | |

72 | 0.0099 | 11.44% | 0.0166 | |

84 | 0.0123 | 18.72% | 0.0208 |

P_{C} | Q_{1} | Q_{2} | Q_{3} | P_{C} | Q_{1} | Q_{2} | Q_{3} | P_{C} | Q_{1} | Q_{2} | Q_{3} | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Feature | Feature | Feature | ||||||||||||

E${}_{speed}$ | ✓ | ✓ | AOTP | ✓ | ✓ | EWT | ✓ | ✓ | ||||||

EFT | ✓ | ✓ | ✓ | NOx | ✓ | ✓ | O_{2} | ✓ | ✓ | ✓ | ||||

AP | ET | ✓ | ✓ | PT${}_{EGM}$ | ✓ | |||||||||

UTL | ✓ | UTT | ✓ | V${}_{speed}$ | ✓ | ✓ | ✓ | |||||||

GPO | ✓ | ✓ | SDM | ✓ | ✓ | ✓ | EFCR | ✓ | ✓ | |||||

EFCR${}_{avg}$ | ✓ | ✓ | EFC${}_{SD}$ | ✓ | ✓ | ✓ | EFC${}_{total}$ | ✓ | ✓ | ✓ | ||||

BV | ✓ | ✓ | FTL | ✓ | ✓ | CER | ✓ | ✓ | ||||||

LNG | ✓ | ✓ | ✓ | LAT | ✓ | ✓ | ✓ | EF${}_{COPERT}$ | ✓ | ✓ | ✓ |

Metrics | MAE | MAPE | RMSE | |
---|---|---|---|---|

P${}_{\mathit{C}}$ | ||||

0 | 0.0086 | 8.23% | 0.0159 | |

Q${}_{1}$ | 0.0089 | 9.03% | 0.0153 | |

Q${}_{2}$ | 0.0077 | 7.38% | 0.0137 | |

Q${}_{3}$ | 0.0086 | 8.82% | 0.0156 |

Metrics | MAE | MAPE | RMSE | |
---|---|---|---|---|

Model | ||||

ResNet50 | 0.0156 | 17.36% | 0.0256 | |

ResNet50 (CBAM) | 0.0098 | 9.37% | 0.0186 | |

HI_TTFTS (Without CBAM) | 0.0138 | 16.69% | 0.0225 | |

HI_TTFTS (Left) | 0.0123 | 13.70% | 0.0198 | |

HI_TTFTS (Right) | 0.0112 | 13.23% | 0.0181 | |

HI_TTFTS (all) | 0.0077 | 7.38% | 0.0137 |

Metrics | MAE | MAPE | RMSE | |
---|---|---|---|---|

Model | ||||

SVR | 0.0864 | 87.18% | 0.1524 | |

ANN | 0.0229 | 138.43% | 0.0275 | |

CNN | 0.01963 | 60.6% | 0.0292 | |

TF | 0.0135 | 16.76% | 0.0216 | |

TSFF | 0.0163 | 20.13% | 0.0274 | |

HI_TTFTS | 0.0077 | 7.38% | 0.0137 |

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## Share and Cite

**MDPI and ACS Style**

Xu, Z.; Wang, R.; Pan, K.; Li, J.; Wu, Q.
Two-Stream Networks for COPERT Correction Model with Time-Frequency Features Fusion. *Atmosphere* **2023**, *14*, 1766.
https://doi.org/10.3390/atmos14121766

**AMA Style**

Xu Z, Wang R, Pan K, Li J, Wu Q.
Two-Stream Networks for COPERT Correction Model with Time-Frequency Features Fusion. *Atmosphere*. 2023; 14(12):1766.
https://doi.org/10.3390/atmos14121766

**Chicago/Turabian Style**

Xu, Zhenyi, Ruibin Wang, Kai Pan, Jiaren Li, and Qilai Wu.
2023. "Two-Stream Networks for COPERT Correction Model with Time-Frequency Features Fusion" *Atmosphere* 14, no. 12: 1766.
https://doi.org/10.3390/atmos14121766