This article proposes a method based on the Kalman filter to improve the accuracy of the CO2 measurement in driving cycles such as worldwide harmonized light vehicles test cycles or real driving cycles which are inherently subject to a loss in accuracy due to the dynamic limitations of the CO2 analysers. The information from the analyser is combined with the electronic control unit estimation of the fuel injection. The characteristics of diesel engines and, in particular, the high efficiency of the combustion process and the diesel oxidation catalyst allows to compute the CO2 emissions from the fuel consumption estimation of the electronic control unit by applying the carbon balance method assuming negligible HC and CO emissions. Then, the assessment of the CO2 analyser response time and accuracy allows to pose an estimation problem that can be solved by a Kalman filter. The application of the method to different driving cycles shows that analyser dynamic limitations may lead to an overestimation of the CO2 figures that can reach 4% in highly dynamic tests such as the worldwide harmonized light vehicles test cycles. The technique thus has further potential application to replicating real driving cycles on the chassis dynamometer for real driving emission testing.

中文翻译：

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通过数据融合技术改进动态驾驶循环中柴油动力系统的 CO2 排放评估

本文提出了一种基于卡尔曼滤波器的方法，以提高驾驶循环中 CO2 测量的准确性，例如全球统一的轻型车辆测试循环或由于 CO2 的动态限制而固有的准确性损失的实际驾驶循环。分析仪。来自分析仪的信息与电子控制单元对燃油喷射的估计相结合。柴油发动机的特性，特别是燃烧过程的高效率和柴油氧化催化剂，允许通过应用碳平衡方法，假设 HC 和 CO 排放可忽略不计，通过电子控制单元的燃料消耗估算来计算 CO2 排放. 然后，CO2 分析仪响应时间和准确度的评估允许提出可以通过卡尔曼滤波器解决的估计问题。该方法在不同驾驶循环中的应用表明，分析仪的动态限制可能导致高估 CO2 数字，在高动态测试（例如全球统一的轻型车辆测试循环）中可达到 4%。因此，该技术具有进一步的潜在应用，可在底盘测功机上复制真实驾驶循环以进行真实驾驶排放测试。