US20260055738
2026-02-26
Mechanical engineering
F02D41/2429
The patent application introduces a method for optimizing a motor assembly, which consists of an internal combustion engine and an after-treatment system (ATS). The approach utilizes an artificial intelligence (AI) model to optimize engine control parameters and independent ATS control parameters. By obtaining an operating point for the motor assembly, the AI model predicts a performance score, allowing adjustments to achieve optimal performance. This adaptive strategy aims to enhance fuel efficiency and reduce emissions under real driving conditions.
Modern vehicles rely on after-treatment systems to reduce hazardous emissions from internal combustion engines. The effectiveness of these systems is influenced by factors such as gas composition, temperature, and sensor accuracy. Traditional calibration methods are resource-intensive, requiring numerous experiments and analyses under ideal conditions. However, these methods do not adapt to real-world driving conditions or account for vehicle aging, highlighting the need for an automated, periodic calibration process.
The proposed method involves obtaining a first operating point for the motor assembly and using an AI model to predict performance based on engine and ATS control parameters. A performance model returns a predicted score, guiding the determination of optimal control parameters for both the engine and ATS. These parameters are then adjusted to enhance the motor assembly's performance, addressing the limitations of static calibration methods by adapting to changing conditions.
The method also includes a system for detecting and mitigating faults in the motor assembly. Sensors collect data on the operating point and control parameters, which the AI model uses to predict performance. A performance mismatch between actual and predicted performance triggers fault detection. If the mismatch exceeds a predefined threshold, a fault alert is issued, allowing for timely interventions to maintain optimal vehicle performance.
The system comprises a motor assembly and a computer system equipped with processors to handle the AI model. The model receives inputs of operating points and control parameters to predict performance. The computer system determines optimal control parameters to adjust engine and ATS settings. This configuration ensures continuous optimization and fault detection, enhancing vehicle efficiency and emission control over its lifetime.