Made in China Good Quality S60 R5237320 Diesel Fuel Injector Common Rail Injector Engine Parts

Abstract:

Accurate fuel injection control is essential for improving combustion efficiency, reducing emissions, and ensuring the stable performance of modern diesel engines. However, under dynamic operating conditions—such as high-speed load transitions, varying fuel temperatures, and injection pressure fluctuations—the actual injected fuel quantity often deviates from the target value due to delays, nonlinearities, and wear in the injector system. To address these challenges, this study proposes a dynamic fuel injection quantity compensation algorithm based on deep learning, enabling real-time prediction and correction of injection deviations.

A large-scale dataset was constructed from high-pressure common-rail injector bench tests, including multi-dimensional parameters such as rail pressure, energizing time, coil current, injector temperature, and fuel viscosity. The corresponding actual injected quantities were measured using a precision flow meter. After normalization and feature engineering, the data were used to train a hybrid deep learning model combining a Convolutional Neural Network (CNN) for feature extraction and a Long Short-Term Memory (LSTM) network for temporal dependency learning.

The proposed model predicts injection deviations with a mean absolute error (MAE) below 1.5%, outperforming conventional polynomial and adaptive PID compensation methods by over 40% in accuracy. Furthermore, the algorithm dynamically adjusts injector control parameters—such as energizing duration and current amplitude—in real time based on feedback predictions, achieving consistent injection quantity control even under rapid pressure changes or component degradation.

A real-time embedded implementation was validated on an engine control unit (ECU) prototype, showing stable inference within 2.3 ms per cycle, suitable for real-time control applications. The model demonstrates strong generalization ability across different injectors and fuel types, with self-learning capabilities for long-term drift compensation.

This research establishes a novel data-driven intelligent control framework for diesel injectors. By integrating deep learning into the injection control loop, it provides a practical path toward adaptive, self-compensating fuel injection systems that enhance efficiency, reliability, and environmental performance in next-generation engines.