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Jia Z, Yin J, Cao Z, Wei N, Jiang Z, Zhang Y, Wu L, Zhang Q, Mao H. Large-scale deployment of intelligent transportation to help achieve low-carbon and clean sustainable transportation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 949:174724. [PMID: 39059649 DOI: 10.1016/j.scitotenv.2024.174724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 06/27/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024]
Abstract
Sustained deep emission reduction in road transportation is encountering bottleneck. The Intelligent Transportation-Speed Guidance System (ITSGS) is anticipated to overcome this challenge and facilitate the achievement of low-carbon and clean transportation. Here, we compiled vehicle emission datasets collected from real-world road experiments and identified the mapping relationships between four pollutants (CO2, CO, NOx, and THC) and their influencing factors through machine learning. We developed random forest models for each pollutant and achieved strong predictive performance, with an R2 exceeding 0.85 on the test dataset for all models. The environmental benefits of ITSGS at the urban scale were quantified by combining emission models with large-scale real trajectory data from Zibo, Shandong Province. Based on temporal and spatial analyses, we found that ITSGS has varying degrees of emission reduction potential during the morning peak, flat peak, and evening peak hours. Values can range from 5.71 %-8.16 % for CO2 emissions, 13.63 %-16.25 % for NOx emissions, 13.69 %-16.45 % for CO emissions, and 4.84-7.07 % for THC emissions, respectively. Additionally, ITSGS can significantly expand the area of low transient emission zones. The best time for achieving maximum environmental benefits from ITSGS is during the workday flat peak. ITSGS limits high-speed and aggressive driving behavior, thereby smoothing the driving trajectory, reducing the frequency of speed switches, and lowering road traffic emissions. The results of the ITSGS environmental benefits evaluation will provide new insights and solutions for sustainable road traffic emission reduction. SYNOPSIS: Large-scale deployment of Intelligent Transportation - Speed Guidance System is a sustainable solution to help achieve low-carbon and clean transportation.
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Affiliation(s)
- Zhenyu Jia
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Jiawei Yin
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Zeping Cao
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Ning Wei
- Jinchuan Group Information and Automation Engineering Co. Ltd., Jinchang 737100, China
| | - Zhiwen Jiang
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Yanjie Zhang
- Tianjin Youmei Environment Technology, Ltd., Tianjin 300380, China
| | - Lin Wu
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Qijun Zhang
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
| | - Hongjun Mao
- Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
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Lyu W, Hu Y, Liu J, Chen K, Liu P, Deng J, Zhang S. Impact of battery electric vehicle usage on air quality in three Chinese first-tier cities. Sci Rep 2024; 14:21. [PMID: 38167600 PMCID: PMC10761960 DOI: 10.1038/s41598-023-50745-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/24/2023] [Indexed: 01/05/2024] Open
Abstract
China, the world leader in automobile production and sales, confronts the challenge of transportation emissions, which account for roughly 10% of its total carbon emissions. This study, utilizing real-world vehicle data from three major Chinese cities, assesses the impact of Battery Electric Vehicles (BEVs) on air quality. Our analysis reveals that BEVs, when replacing gasoline vehicles in their operational phase, significantly reduce emissions, with reductions ranging from 8.72 to 85.71 kg of CO2 per vehicle monthly. The average monthly reduction rate is 9.47%, though this effect is less pronounced during winter. Advanced BEVs, characterized by higher efficiency and newer technology, exhibit greater emission reduction benefits. While private BEVs generally contribute positively to environmental outcomes, taxi BEVs, due to their intensive usage patterns, show less environmental advantage and may sometimes worsen air quality. Looking ahead, we project substantial emission reductions from the replacement of gasoline vehicles with electric alternatives over the next decade. Policymakers are urged to adopt proactive measures, focusing on promoting medium to large electric vehicles and fostering the use of private and ride-hailing electric vehicles.
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Affiliation(s)
- Wenjing Lyu
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ying Hu
- School of Humanities and Social Sciences, Beijing Institute of Technology, Beijing, China
| | - Jin Liu
- School of Humanities and Social Sciences, Beijing Institute of Technology, Beijing, China.
| | - Kaizhe Chen
- School of Humanities and Social Sciences, Beijing Institute of Technology, Beijing, China
| | - Peng Liu
- National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing, China
| | - Junjun Deng
- School of Physical Sciences, Beijing Institute of Technology, Beijing, China
| | - Shaojun Zhang
- Institute of Air Pollution and Control, Tsinghua University, Beijing, China
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Yang H, Lam KY, Xiao L, Xiong Z, Hu H, Niyato D, Vincent Poor H. Lead federated neuromorphic learning for wireless edge artificial intelligence. Nat Commun 2022; 13:4269. [PMID: 35879326 PMCID: PMC9314401 DOI: 10.1038/s41467-022-32020-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 07/13/2022] [Indexed: 12/02/2022] Open
Abstract
In order to realize the full potential of wireless edge artificial intelligence (AI), very large and diverse datasets will often be required for energy-demanding model training on resource-constrained edge devices. This paper proposes a lead federated neuromorphic learning (LFNL) technique, which is a decentralized energy-efficient brain-inspired computing method based on spiking neural networks. The proposed technique will enable edge devices to exploit brain-like biophysiological structure to collaboratively train a global model while helping preserve privacy. Experimental results show that, under the situation of uneven dataset distribution among edge devices, LFNL achieves a comparable recognition accuracy to existing edge AI techniques, while substantially reducing data traffic by >3.5× and computational latency by >2.0×. Furthermore, LFNL significantly reduces energy consumption by >4.5× compared to standard federated learning with a slight accuracy loss up to 1.5%. Therefore, the proposed LFNL can facilitate the development of brain-inspired computing and edge AI. Designing energy-efficient computing solution for the implementation of AI algorithms in edge devices remains a challenge. Yang et al. proposes a decentralized brain-inspired computing method enabling multiple edge devices to collaboratively train a global model without a fixed central coordinator.
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Affiliation(s)
- Helin Yang
- Department of Information and Communication Engineering, School of Informatics, Xiamen University, Xiamen, China.,Strategic Centre for Research in Privacy-Preserving Technologies and Systems, Nanyang Technological University, Singapore, Singapore
| | - Kwok-Yan Lam
- Strategic Centre for Research in Privacy-Preserving Technologies and Systems, Nanyang Technological University, Singapore, Singapore. .,School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
| | - Liang Xiao
- Department of Information and Communication Engineering, School of Informatics, Xiamen University, Xiamen, China
| | - Zehui Xiong
- Pillar of Information Systems Technology and Design, Singapore University of Technology and Design, Singapore, Singapore
| | - Hao Hu
- Department of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Dusit Niyato
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - H Vincent Poor
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA
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Deng Z, Hu X, Xie Y, Xu L, Li P, Lin X, Bian X. Battery health evaluation using a short random segment of constant current charging. iScience 2022; 25:104260. [PMID: 35521525 PMCID: PMC9062330 DOI: 10.1016/j.isci.2022.104260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/13/2022] [Accepted: 04/11/2022] [Indexed: 11/30/2022] Open
Abstract
Accurately evaluating the health status of lithium-ion batteries (LIBs) is significant to enhance the safety, efficiency, and economy of LIBs deployment. However, the complex degradation processes inside the battery make it a thorny challenge. Data-driven methods are widely used to resolve the problem without exploring the complex aging mechanisms; however, random and incomplete charging-discharging processes in actual applications make the existing methods fail to work. Here, we develop three data-driven methods to estimate battery state of health (SOH) using a short random charging segment (RCS). Four types of commercial LIBs (75 cells), cycled under different temperatures and discharging rates, are employed to validate the methods. Trained on a nominal cycling condition, our models can achieve high-precision SOH estimation under other different conditions. We prove that an RCS with a 10mV voltage window can obtain an average error of less than 5%, and the error plunges as the voltage window increases. A short random charging segment enables battery health evaluation Two features with high correlations to battery capacity are extracted Three typical machine learning methods are compared for battery health estimation The method is verified by four types of batteries cycled under different conditions
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Affiliation(s)
- Zhongwei Deng
- College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
- Corresponding author
| | - Xiaosong Hu
- College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
- Corresponding author
| | - Yi Xie
- College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
| | - Le Xu
- College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
| | - Penghua Li
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Xianke Lin
- Department of Mechanical Engineering, Ontario Tech University, ON L1G 0C5, Canada
| | - Xiaolei Bian
- Department of Chemical Engineering, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden
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