Unregulated Emissions from Natural Gas Taxi Based on IVE Model

Zhao, Hong; Mu, Liang; Li, Yan; Qiu, Junzheng; Sun, Caige; Liu, Xiaotong

doi:10.3390/atmos12040478

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…Intelligent transportation and autonomous driving have significantly advanced with the rapid growth of information technology [1]. Intelligent transportation has emerged as a prominent trend in the automotive industry [2], with a key component being the observation and understanding of the driving environment by self-driving cars [3]. Numerous scholars have developed new algorithms to aid self-driving cars in better understanding their environment.…”

Section: Introductionmentioning

confidence: 99%

Improved Convolutional Neural Network for Traffic Scene Segmentation

Xu,

Luo,

Sun

et al. 2024

CMES

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In actual traffic scenarios, precise recognition of traffic participants, such as vehicles and pedestrians, is crucial for intelligent transportation. This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants. The algorithm incorporates long and shortterm memory networks and the fused attention module (GSAM, GCT, and Spatial Attention Module) to enhance the algorithm's capability to process both global and local information. Additionally, to increase the network's initial operation stability, the original network activation function was replaced with Gaussian error linear unit. Experiments were conducted using the publicly available Cityscapes dataset. Comparing the test results, it was observed that the revised algorithm outperformed the original algorithm in terms of AP 50 , AP 75 , and other metrics by 8.7% and 9.6% for target detection and 12.5% and 13.3% for segmentation.

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Section: Introductionmentioning

confidence: 99%

Improved Convolutional Neural Network for Traffic Scene Segmentation

Xu,

Luo,

Sun

et al. 2024

CMES

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“…New energy vehicles have drawn more and more attention in recent years as energy and environmental issues have become more severe. Zhao et al [1] studied the impact of unregulated emissions from fuel-powered cars on the environment and promoted Qingdao's "Oil-to-Gas" plan. Although pure electric vehicles have many advantages, such as low emissions and high efficiency, due to the limitations of small battery capacity, long charging time, and the small number of charging piles, the driving range of pure electric vehicles is short, which greatly affects the popularity of pure electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

MPC-ECMS Energy Management of Extended-Range Vehicles Based on LSTM Multi-Signal Speed Prediction

Zhao

Liu

et al. 2023

Electronics

Self Cite

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Rule-based energy management strategies not only make little use of the efficient area of engines and generators but also need to perform better planning in the time domain. This paper proposed a multi-signal vehicle speed prediction model based on the long short-term memory (LSTM) network, improving the accuracy of vehicle speed prediction by considering multiple signals. First, various signals were collected by simulating the vehicle model, and a Pearson correlation analysis was performed on the collected multiple signals in order to improve the model’s prediction accurate, and the appropriate signal was selected as the input to the prediction model. The experimental results indicate that the prediction method greatly improves the predictive effect compared with the support vector machine (SVM) vehicle speed prediction method. Secondly, the method was combined with the model predictive control-equivalent consumption strategy (MPC-ECMS) to form a control strategy suitable for power maintenance conditions enabling the equivalent factor to be adjusted adaptively in real-time and the target state of charge (SoC) value to be set. Pontryagin minimum principle (PMP) enables the battery to calculate the range extender output power at each moment. PMP, as the core algorithm of ECMS, is a common real-time optimal control algorithm. Then, taking into account the engine’s operating characteristics, the calculated range extender power was filtered to make the engine run smoothly. Finally, hardware-in-the-loop simulation (HIL) was used to verify the model. The simulation results demonstrate that this method uses less fuel than the equivalent fuel consumption minimum strategy (ECMS) by 1.32%, 9.47% when compared to the power-following control strategy, 15.66% when compared to the SVM-MPC-ECMS, and only 3.58% different from the fuel consumption of the dynamic programming (DP) control algorithm. This shows that this energy management approach can significantly improve the overall vehicle fuel economy.

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“…The current research on emission models is mainly focused on analyzing emission factors of existing models and establishing emission inventories. In terms of analyzing emission factors, Zhao et al [31] localized the IVE model by using the obtained cab-driving conditions and local parameters, combined with Vehicle Mass Analysis System (VMAS) experiments, and measured the annual emissions of cab pollutants. Perugu [22] used modified Indian driving conditions and local light vehicle specific driving conditions to correct the MOVES model for emission rates using an improved VSP-based MOVES model for modeling light-duty vehicle emissions in India.…”

Section: Introductionmentioning

confidence: 99%

Study of Mobile Source Pollutants Based on Multi-Source Data Fusion: A Case Study of Zibo City, China

Zheng,

Liu,

Zhang

et al. 2023

Sustainability

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The primary pollutants emitted from mobile sources have become one of the main sources of urban air pollution. In this study, the primary pollutants CO, HC, NOx and PM from mobile sources in Zibo city, China are studied. Firstly, the localized mobile-source pollutant emission factors are corrected using vehicle emission experiments; secondly, multi-source data fusion is performed using road network data, road traffic data, air quality and meteorological data; then, the corrected pollutant emission factors and multi-source data are used to construct a localized emission measurement model and dispersion simulation model, visualize the emission distribution and propose residual concentration correction to accurately obtain the pollutant sharing rate. The results show that the pollutant emission trends are highly coupled with the distribution of urban residents’ working hours. Based on the localized dispersion model used to analyze the pollutant traceability at air monitoring stations, the emission sharing rate of NOx is the largest, and according to the analysis of the changing trend of mobile-source emission sharing rate, the mobile source pollutants in Zibo are mainly influenced by pollutant emissions from road motor vehicles. This study provides data support and a theoretical basis for the management of the transportation industry to carry out exhaust gas control of motor vehicles and non-road mobile machinery and to make decisions related to improving the air environment (delineating the scope of low emission zones).

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Unregulated Emissions from Natural Gas Taxi Based on IVE Model

Cited by 6 publications

References 35 publications

Improved Convolutional Neural Network for Traffic Scene Segmentation

Improved Convolutional Neural Network for Traffic Scene Segmentation

MPC-ECMS Energy Management of Extended-Range Vehicles Based on LSTM Multi-Signal Speed Prediction

Study of Mobile Source Pollutants Based on Multi-Source Data Fusion: A Case Study of Zibo City, China

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