Recursive least squares, CAN bus data, Vehicle longitudinal dynamics model
Abstract
Vehicle weight is a key factor affecting active control strategies and safety in modern days. The manuscript presents a refined approach for estimating automobile mass that integrates a model of the car’s longitudinal dynamics along with an iterative least squares technique that incorporates a diminishing factor. A weight estimation model is developed and tested under steady speed conditions. The study introduces a technique for real-time determination of vehicle weight by leveraging data from the control area network (CAN) bus and employing a recursive least squares approach that incorporates a forgetting factor. Real vehicle tests show that the method has a small error at low and medium speeds, but a large error at high speeds. The method utilizes CAN bus data to minimize the need for additional sensors, which helps reduce costs. It also provides good responsiveness and efficiency. This manuscript delves deeper into the method’s practical viability and its successful application in real-world scenarios.
