The knowledge of the temperature and particulate matter mass distribution is essential for monitoring the performance and durability of a catalyzed particulate filter. A catalyzed particulate filter model was developed, and it showed capability to accurately predict temperature and particulate matter mass distribution and pressure drop across the catalyzed particulate filter. However, the high-fidelity model is computationally demanding. Therefore, a reduced order multi-zone particulate filter model was developed to reduce computational complexity with an acceptable level of accuracy. In order to develop a reduced order model, a parametric study was carried out to determine the number of zones necessary for aftertreatment control applications. The catalyzed particulate filter model was further reduced by carrying out a sensitivity study of the selected model assumptions. The reduced order multi-zone particulate filter model with 5 × 5 zones was selected to develop a catalyzed particulate filter state estimator considering its computational time and accuracy. Next, a Kalman filter–based catalyzed particulate filter estimator was developed to estimate unknown states of the catalyzed particulate filter such as temperature and particulate matter mass distribution and pressure drop (ΔP) using the sensor inputs to the engine electronic control unit and the reduced order multi-zone particulate filter model. A diesel oxidation catalyst estimator was also integrated with the catalyzed particulate filter estimator in order to provide estimates of diesel oxidation catalyst outlet concentrations of NO2 and hydrocarbons and inlet temperature for the catalyzed particulate filter estimator. The combined diesel oxidation catalyst–catalyzed particulate filter estimator was validated for an active regeneration experiment. The validation results for catalyzed particulate filter temperature distribution showed that the root mean square temperature error by using the diesel oxidation catalyst–catalyzed particulate filter estimator is within 3.2 °C compared to the experimental data. Similarly, the ΔP estimator closely simulated the measured total ΔP and the estimated cake pressure drop error is within 0.2 kPa compared to the high-fidelity catalyzed particulate filter model.

中文翻译：

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用于模拟和控制柴油催化颗粒过滤器的颗粒物分布的卡尔曼过滤器估计器的开发

温度和颗粒物质质量分布的知识对于监测催化颗粒过滤器的性能和耐用性至关重要。开发了一种催化微粒过滤器模型，它显示出能够准确预测温度和微粒物质质量分布以及催化微粒过滤器的压降。然而，高保真模型在计算上要求很高。因此，开发了降阶多区微粒过滤器模型，以在可接受的精度水平下降低计算复杂性。为了开发降阶模型，进行了参数研究以确定后处理控制应用所需的区域数量。通过对所选模型假设进行敏感性研究，进一步简化了催化微粒过滤器模型。考虑到计算时间和精度，选择具有 5 × 5 区的降阶多区微粒过滤器模型来开发催化微粒过滤器状态估计器。接下来，开发了基于卡尔曼滤波器的催化微粒过滤器估计器，以使用发动机电子控制单元的传感器输入和降阶来估计催化微粒过滤器的未知状态，例如温度和微粒物质质量分布和压降 (ΔP)多区微粒过滤器模型。柴油氧化催化剂估计器也与催化微粒过滤器估计器集成，以便为催化微粒过滤器估计器提供柴油氧化催化剂出口NO 2 和碳氢化合物浓度和入口温度的估计。结合柴油氧化催化剂催化的微粒过滤器估计器在主动再生实验中得到验证。催化微粒过滤器温度分布的验证结果表明，与实验数据相比，使用柴油氧化催化剂催化微粒过滤器估算器的均方根温度误差在 3.2 °C 以内。类似地，ΔP 估计器密切模拟了测量的总 ΔP，并且估计的滤饼压降误差在 0 以内。