An Efficient Vehicle Tyre Pressure Monitoring Algorithm Based on Quasi-Distributed Computing and Recursive Least Squares

Indirect tyre pressure monitoring system (ITPMS) can effectively reduce tyre blowouts risk and enhance high-speed vehicle operation safety, gradually emerging as a rapidly popular-ized on-board electronic control safety product. ITPMS imposes stringent requirements on the memory space and computational capabilities of embedded systems, particularly for cost-sensitive products such as Electronic Stability Program (ESP) and Anti-lock Braking System (ABS). This paper proposes a method utilizing Recursive Least Squares (RLS) for natural frequency identification, transforming spatial requirements into iterative signal demands along the time dimension to mitigate memory usage. Additionally, a quasi-distributed computing architecture (QDCA) is proposed to distribute the natural frequency identifi-cation process across different time segments, thereby enhancing computational efficiency. During the trial and validation process, the proposed method is deployed within ABS. Experimental validation demonstrates that this method achieves equivalent monitoring accuracy while reducing memory usage by 11.2% and enabling natural frequency updates within 500ms.