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考虑车辆类型变化的中国乘用车排放特征 被引量:2
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作者 郭栋 闫伟 +3 位作者 谭啸川 高松 高兴邦 张同庆 《中国环境科学》 EI CAS CSCD 北大核心 2021年第7期3138-3152,共15页
通过引入Lotka-Volterra模型预测了中国未来30年的乘用车竞争趋势;通过引入CHG、VOC、CO、SO_(2)、PM_(2.5)、NOx6类污染物更新了全生命周期清单;并据此建立了政策影响模型和敏感性模型评估电动化、轻量化和清洁化政策情景减排效果.结... 通过引入Lotka-Volterra模型预测了中国未来30年的乘用车竞争趋势;通过引入CHG、VOC、CO、SO_(2)、PM_(2.5)、NOx6类污染物更新了全生命周期清单;并据此建立了政策影响模型和敏感性模型评估电动化、轻量化和清洁化政策情景减排效果.结果表明,乘用车市场的主要竞争力来源于新能源与传统能源的竞争,且纯电动与混合动力乘用车呈S型曲线发展,汽油乘用车占比由92%减少到1%;全生命周期中,纯电动乘用车对CHG、VOC、CO减排效益最优,为20%~85%;汽油与天然气乘用车对SO_(2)和PM_(2.5)的减排效益最优,为50.0%;3类情景下税收补贴类政策敏感性最强,CHG、VOC和CO的最优减排情景为电气化情景,PM_(2.5)、NOx的最优减排情景为清洁化情景,而SO_(2)的最优减排情景则为整车轻量化. 展开更多
关键词 排放特征 L-V模型 全生命周期 政策评估 乘用车类型
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Optimal path planning method of electric vehicles considering power supply 被引量:5
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作者 GUO Dong LI Chao-chao +8 位作者 YAN Wei HAO Yu-jiao XU Yi WANG Yu-qiong ZHOU Ying-chao E Wen-juan ZHANG Tong-qing gao xing-bang TAN Xiao-chuan 《Journal of Central South University》 SCIE EI CAS CSCD 2022年第1期331-345,共15页
Because of the limitations of electric vehicle(EV)battery technology and relevant supporting facilities,there is a great risk of breakdown of EVs during driving.The resulting driver“range anxiety”greatly affects the... Because of the limitations of electric vehicle(EV)battery technology and relevant supporting facilities,there is a great risk of breakdown of EVs during driving.The resulting driver“range anxiety”greatly affects the travel quality of EVs.These limitations should be overcome to promote the use of EVs.In this study,a method for travel path planning considering EV power supply was developed.First,based on real-time road conditions,a dynamic energy model of EVs was established considering the driving energy and accessory energy.Second,a multi-objective travel path planning model of EVs was constructed considering the power supply,taking the distance,time,energy,and charging cost as the optimization objectives.Finally,taking the actual traffic network of 15 km×15 km area in a city as the research object,the model was simulated and verified in MATLAB based on Dijkstra shortest path algorithm.The simulation results show that compared with the traditional route planning method,the total distance in the proposed optimal route planning method increased by 1.18%,but the energy consumption,charging cost,and driving time decreased by 11.62%,41.26%and 11.00%,respectively,thus effectively reducing the travel cost of EVs and improving the driving quality of EVs. 展开更多
关键词 electric vehicle vehicle special power charging path multi-objective optimization Dijkstra algorithm
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