This paper presents an adaptive filter implementation on a Manifold Absolute Pressure (MAP) sensor for automotive engine control applications. The proposed adaptive filter is based on the least mean squares (LMS) algorithm and is designed to mitigate the effects of sensor noise and nonlinearity, which can lead to inaccurate readings and subsequent engine performance degradation. The filter is implemented on a model-based system and is able to adapt to changes in the sensor's characteristics over time, making it suitable for use in long-term engine control applications. Experimental results show that the proposed filter effectively reduces sensor noise and improves the accuracy of MAP sensor readings, which has an increase in the Mean Absolute Percentage Error (MAPE) of nearly 10% compared to the conventional lowpass filter. Additionally, the filter's adaptability ensures robust performance over a range of operating conditions and sensor characteristics. Furthermore, the filter has a Signal-to-Noise Ratio (SNR) improvement of also nearly 10% higher than the conventional lowpass filter, leading to improved engine performance and fuel efficiency. Overall, the proposed adaptive filter represents a promising solution for enhancing the performance of MAP sensors in automotive engine control applications. More advancements in the adaptive filter algorithm can still be made with the help of additional research.
Anahtar Kelimeler: signal processing, digital filtering, adaptive filtering, manifold absolute pressure
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