Development and Application of Ion Current/Cylinder Pressure Cooperative Combustion Diagnosis and Control System
Development and Application of Ion Current/Cylinder Pressure Cooperative Combustion Diagnosis and Control System
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DOI：
10.3390/en13215656
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发表时间：
2020-10
期刊：
影响因子：
3.2
通讯作者：
Denghao Zhu;J. Deng;Jinqiu Wang;Shuo Wang;Hongyu Zhang;J. Andert;Liguang Li
中科院分区：
文献类型：
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作者：
Denghao Zhu;J. Deng;Jinqiu Wang;Shuo Wang;Hongyu Zhang;J. Andert;Liguang Li
用于发动机效率提高和排放减少的先进技术的应用也增加了不完全燃烧、失火、爆震或提前点火等异常燃烧的发生可能性。新的有前途的燃烧模式，这基本上是占主导地位的化学反应动力学的燃烧控制的主要困难。传统的基于发动机脉谱图的燃烧控制方法无法实时监测各循环的燃烧状态，难以实现精确的燃烧控制，因此需要对缸内燃烧进行实时的循环分解诊断和控制。缸内压力传感器和离子电流传感器作为缸内燃烧诊断和控制中最常用的两种传感器，过去一直处于一种看似竞争的关系，因此所有相关研究都只使用其中一种传感器。然而，这两种传感器都有自己独特的功能。在这项研究中，其想法是联合收割机将从两个传感器获得的信息结合起来。本文首先对两种离子电流检测系统进行了全面的介绍，并从硬件和信号两个层面进行了比较。最有前途的变种（直流电源离子电流检测系统）被选为后续的实验。阐述了离子电流/缸压协同燃烧诊断与控制系统的概念，并在发动机原型控制单元上实现。介绍了该系统用于均质压燃异常燃烧控制及其稳定性提高的应用实例。结果表明，结合离子电流和缸内压力信号可以为燃烧控制提供更丰富的信息。最后，离子电流和缸内压力信号作为人工神经网络（ANN）模型的输入进行燃烧预测。结果表明，当输入是两个信号的组合时，燃烧预测性能更好，而不是只使用其中一个。这种离线分析证明了使用基于人工神经网络的模型的可行性，该模型的输入是离子电流和压力信号的组合，以获得更好的预测精度。
The application of advanced technologies for engine efficiency improvement and emissions reduction also increase the occurrence possibility of abnormal combustions such as incomplete combustion, misfire, knock or pre-ignition. Novel promising combustion modes, which are basically dominated by chemical reaction kinetics show a major difficulty in combustion control. The challenge in precise combustion control is hard to overcome by the traditional engine map-based control method because it cannot monitor the combustion state of each cycle, hence, real-time cycle-resolved in-cylinder combustion diagnosis and control are required. In the past, cylinder pressure and ion current sensors, as the two most commonly used sensors for in-cylinder combustion diagnosis and control, have enjoyed a seemingly competitive relationship, so all related researches only use one of the sensors. However, these two sensors have their own unique features. In this study, the idea is to combine the information obtained from both sensors. At first, two kinds of ion current detection system are comprehensively introduced and compared at the hardware level and signal level. The most promising variant (the DC-Power ion current detection system) is selected for the subsequent experiments. Then, the concept of ion current/cylinder pressure cooperative combustion diagnosis and control system is illustrated and implemented on the engine prototyping control unit. One application case of employing this system for homogenous charge compression ignition abnormal combustion control and its stability improvement is introduced. The results show that a combination of ion current and cylinder pressure signals can provide richer and also necessary information for combustion control. Finally, ion current and cylinder pressure signals are employed as inputs of artificial neural network (ANN) models for combustion prediction. The results show that the combustion prediction performance is better when the inputs are a combination of both signals, instead of using only one of them. This offline analysis proves the feasibility of using an ANN-based model whose inputs are a combination of ion current and pressure signals for better prediction accuracy.