Fuel Injector Deviation Learning via Minimum Injection Interval

The patent applies dynamics by making the learning method adaptive to different operating conditions. The system dynamically selects between two learning methods: using crankshaft acceleration for low-speed/low-load conditions and using minimum interval between injection periods for high-speed/high-load conditions. This dynamic adaptation resolves the contradiction by ensuring accurate learning across the full operating range of the engine.

The patent changes the parameter used for learning based on operating conditions. At low speeds, it uses crankshaft acceleration parameters; at high speeds, it switches to minimum interval between injection periods. This parameter change allows the system to maintain measurement precision across varying operating conditions, particularly enabling accurate learning under high-speed and high-load conditions where the previous method failed.

The system dynamically switches between two learning methods based on engine operating conditions. At low speeds, it uses crankshaft acceleration measurement which provides accurate learning. At high speeds, it transitions to using the minimum interval between injection periods method. This dynamic switching resolves the contradiction by maintaining learning accuracy across the entire engine speed range.

The patent segments the learning process into two distinct methods applicable to different operating conditions. The first method (crankshaft acceleration) handles low-speed conditions, while the second method (minimum interval measurement) handles high-speed conditions. This segmentation allows each method to be optimized for its specific operating range, resolving the speed range applicability issue.

The patent implements feedback by measuring the actual fuel injection characteristics through crankshaft acceleration or minimum interval measurements, comparing them against target values, and using the deviations to correct fuel injection quantities. This feedback loop compensates for individual variations in fuel injector characteristics, achieving uniform fuel injection across all cylinders while maintaining manufacturing simplicity.

The system performs self-correction by automatically learning and compensating for individual fuel injector variations without requiring manual calibration or precision manufacturing. Each fuel injector system learns its own characteristics and adjusts its operation accordingly, achieving uniformity through self-service rather than through complex manufacturing processes.