Using portable emissions measurement systems (PEMS) to derive more accurate estimates of fuel use and nitrogen oxides emissions from modern Euro 6 passenger cars under real-world driving conditions
Using portable emissions measurement systems (PEMS) to derive more accurate estimates of fuel use and nitrogen oxides emissions from modern Euro 6 passenger cars under real-world driving conditions
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DOI：
10.1016/j.apenergy.2019.03.047
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发表时间：
2019-05-15
期刊：
影响因子：
11.2
通讯作者：
Boies, Adam M.
中科院分区：
文献类型：
--
作者：
Bishop, Justin D. K.;Molden, N.;Boies, Adam M.
来自便携式排放测量系统 (PEMS) 和其他来源的数据可以识别和量化型式批准与实际燃油经济性和氮氧化物 (NOx) 排放之间的差异。然而，知识差距仍然存在，因为识别这种差异并不能让我们准确预测现实世界的燃油经济性和排放量。我们采用自下而上的方法来解决这一知识空白：PEMS 用于一系列欧 6 汽油和柴油车辆，从中衍生出内部一致的动力系统模型。这些训练车辆模拟了 20 多个真实世界和受监管的驾驶循环。代表驾驶、车辆和环境特征的 26 个指标用于开发三个车辆组的分位数回归 (QR) 模型：带有三元催化剂的直喷汽油车；选择性催化还原柴油车；以及带有稀NO、捕集器的柴油车。 95% 预测区间用于评估一组验证工具的 QR 模型的预测准确性。在所有车辆组中，燃油经济性和氮氧化物排放的 QR 模型更多地取决于驾驶循环的动态，而不是发动机特性或环境条件。燃油经济性的 95% 预测区间包含了 PEMS 测试中的大部分观测值，在大多数情况下与 COPERT 的预测误差相似。 QR 方法的优势对于 NO 排放更为明显，其中大部分 PEMS 观测数据包含在 95% PI 中，中位预测误差比 COPERT 低两倍。
Data from portable emissions measurement systems (PEMS) and other sources have allowed the discrepancy between type approval and real-world fuel economy and nitrogen oxides (NOx) emissions to be both identified and quantified. However, a gap in the knowledge persists because identifying this discrepancy does not allow us to predict real-world fuel economy and emissions accurately. We address this gap in the knowledge using a bottom-up approach: a PEMS is used across a range of Euro 6 petrol and diesel vehicles, from which internally consistent powertrain models are derived. These training vehicles are simulated over 20 real-world and regulated driving cycles. 26 metrics representing driving, vehicle and ambient characteristics are used to develop quantile regression (QR) models for three vehicle groups: direct-injection petrol vehicles with three way catalysts; diesel vehicles with selective catalytic reduction; and diesel vehicles with lean NO, traps. 95% prediction intervals are used to assess the predictive accuracy of the QR models from a set of validation vehicles. Across the vehicle groups, QR models for both fuel economy and NO emissions depended on the dynamics of the driving cycles more than the engine characteristics or ambient conditions. The 95% prediction interval for fuel economy enclosed most of the observed values from the PEMS test, with similar prediction error to COPERT in most cases. The benefits of the QR approach were more pronounced for NO emissions, where the majority of PEMS observed data was enclosed in the 95% PI and median prediction error was up to two times lower than COPERT.