文章摘要
刘子仟,黄 莉,陆婋泉,刘京易,张娅楠.计及驾驶习惯影响的电动汽车碳减排量计算方法[J].电力需求侧管理,2024,26(2):62-69
计及驾驶习惯影响的电动汽车碳减排量计算方法
Carbon emission reduction calculation method of electric vehicle energy substitution considering the influence of driving habits
投稿时间:2023-12-15  修订日期:2024-01-29
DOI:10. 3969 / j. issn. 1009-1831. 2024. 02. 010
中文关键词: 电动汽车  碳减排  碳普惠  驾驶习惯  空调能耗  驾驶模式
英文关键词: electric vehicle  carbon emission reduction  carbon inclusion  driving habits  air conditioning energy consumption  driving Mode
基金项目:国家电网有限公司科技项目(5400-202218423A-2-0-ZN)
作者单位
刘子仟 东南大学 电气工程学院,南京 210096 
黄 莉 东南大学 电气工程学院,南京 210096 
陆婋泉 国网江苏省电力有限公司 营销服务中心,南京 210019 
刘京易 国网江苏省电力有限公司,南京 210000 
张娅楠 东南大学 电气工程学院,南京 210096 
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中文摘要:
      作为实现我国双碳目标的重要措施之一,电动汽车不仅能有效减少二氧化碳排放和改善空气质量,还能利用可再生能源,提高能源安全性,并通过智能充电系统参与电网供需互动,平衡电网负荷,增强电网稳定性。为进一步发挥电动汽车在减少碳排放领域的作用,需要建立合理的碳普惠机制,对用户的低碳行为进行量化和奖励。因此,提出一种新模型,基于电动汽车各系统的机理,分析驾驶习惯对能耗的影响,考虑了空调使用、驾驶模式、行驶路况和载重等多个因素。建立了燃油汽车与电动汽车在多维影响因素下的等效里程改进计算模型,以及基于等效里程转换的电动汽车碳减排量计算模型,有效反映了个体驾驶习惯差异对碳减排的影响。通过长期记录并分析多款电动汽车用户在不同季节、路况、驾驶模式和载重下的驾驶数据,能耗模型的准确率从82.47%提升至96.33%,验证了模型的有效性。
英文摘要:
      As a key strategy to achieve China’s dual carbon targets, electric vehicles(EVs)can not only reduce CO2 emissions and improve air quality but also utilize renewable energy for enhanced energy security. EVs can also help balance and stabilize the power grid through smart charging systems. To maximize EVs’role in carbon reduction, it’s vital to establish a carbon inclusion mechanism for quantifying and rewarding low-carbon behavior. Existing models, often based on macro-data or typical experiences, don’t accurately reflect individual variations. So that, a new model considering factors like air conditioning, driving modes, road conditions and load is proposed based on the mechanics of EV systems. This model provides an improved method for calculating equivalent mileage between fuel and electric vehicles, and a carbon reduction calculation model for EVs, effectively reflecting the impact of individual driving habits on carbon reduction.By analyzing long- term driving data of various EVs under different conditions, the model’s accuracy has improved from 82.47% to 96.33%, demonstrating its effectiveness.
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