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An Air-to-Fuel ratio estimation strategy for turbocharged spark-ignition engines based on sparse binary HEGO sensor measures and hybrid linear observers
Control Engineering Practice ( IF 5.4 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.conengprac.2020.104694
Gianfranco Gagliardi , Daniele Mari , Francesco Tedesco , Alessandro Casavola

Abstract An effective Air-to-Fuel Ratio (AFR) control is paramount to ensure a good combustion and high catalyst efficiency. This work addresses the problem of determining continuous-time estimates of AFR in turbocharged Spark Ignition (SI) engines on the basis of binary sparse measurements of the exhaust gas Oxygen. The latter are provided by a HEGO (Heated Exhaust Gas Oxygen) sensor installed at the catalytic converter input in place of a more expensive linear UEGO (Universal Exhaust Gas Oxygen) sensor, as nowadays common in commercial cars. The HEGO sensor outputs two voltage values only, corresponding respectively to low or high concentration of the residual Oxygen in the exhaust gas (on/off behavior). In view of this, it can be classified as a binary sensor generating irregular and sparse measurements in that the useful information is only present at the instants of the on/off and off/on transitions. An estimation scheme based on the use of a recursive least-squares algorithm has been designed by resorting to the theory of linear hybrid observers with quantized inputs. A detailed convergence analysis of the state reconstruction error is also provided. The proposed hybrid observer scheme is employed in a PI control-loop designed to maintain the AFR close to a desired value. The effectiveness of the proposed method is demonstrated by several numerical simulations based on both synthetic and real data.

中文翻译:

基于稀疏二元HEGO传感器测量和混合线性观测器的涡轮增压火花点火发动机空燃比估计策略

摘要 有效的空燃比 (AFR) 控制对于确保良好的燃烧和高催化剂效率至关重要。这项工作解决了基于废气氧的二元稀疏测量确定涡轮增压火花点火 (SI) 发动机中 AFR 的连续时间估计的问题。后者由安装在催化转化器输入端的 HEGO(加热尾气氧)传感器提供,以取代现在商用汽车中常见的更昂贵的线性 UEGO(通用尾气氧)传感器。HEGO 传感器仅输出两个电压值,分别对应于废气中残留氧气的低浓度或高浓度(开/关行为)。鉴于此,它可以归类为生成不规则和稀疏测量的二进制传感器,因为有用的信息仅在开/关和关/开转换的瞬间出现。通过采用具有量化输入的线性混合观测器的理论,已经设计了一种基于使用递归最小二乘算法的估计方案。还提供了状态重建误差的详细收敛分析。提议的混合观测器方案用于 PI 控制回路,旨在将 AFR 保持在接近所需值。基于合成数据和真实数据的几个数值模拟证明了所提出方法的有效性。通过采用具有量化输入的线性混合观测器的理论,已经设计了一种基于使用递归最小二乘算法的估计方案。还提供了状态重建误差的详细收敛分析。提议的混合观测器方案用于 PI 控制回路,旨在将 AFR 保持在接近所需值。基于合成数据和真实数据的几个数值模拟证明了所提出方法的有效性。通过采用具有量化输入的线性混合观测器的理论,已经设计了一种基于使用递归最小二乘算法的估计方案。还提供了状态重建误差的详细收敛分析。提议的混合观测器方案用于 PI 控制回路,旨在将 AFR 保持在接近所需值。基于合成数据和真实数据的几个数值模拟证明了所提出方法的有效性。
更新日期:2021-02-01
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