Vehicle mass dynamic estimation system and method

Through composite weighting algorithms and embedded optimization technology, combined with Gaussian weighting method and entropy value method, the accuracy and real-time problems of vehicle quality estimation under complex working conditions are solved, efficient and accurate quality estimation is achieved, and vehicle safety control and logistics management are supported.

CN120542101APending Publication Date: 2025-08-26南宁桂电电子科技研究院有限公司 +1
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Patent Information

Application Number
CN202510663352.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-22
Publication Date
2025-08-26

AI Technical Summary

Technical Problem

The existing vehicle quality estimation technology has insufficient accuracy under complex working conditions, making it difficult to integrate prior information and real-time data, has high computational complexity, cannot meet real-time requirements, and has strict sensor accuracy requirements, making data acquisition difficult.

Method used

The composite weighting algorithm is adopted, combined with Gaussian weighting method and entropy value method, and through embedded optimization technology, vehicle data is collected and screened in real time, quality estimation is updated dynamically, and weight fusion is used to achieve efficient and accurate quality estimation.

Benefits of technology

It improves the accuracy and robustness of vehicle quality estimation, meets the real-time requirements under complex operating conditions, and provides reliable parameter support for vehicle safety control and logistics management.

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Abstract

The invention relates to the technical field of vehicle dynamic parameter estimation, in particular to a vehicle mass real-time estimation method and system based on a composite weighting algorithm and embedded equipment. The method comprises the steps that the speed, acceleration and driving torque data in the vehicle running process are collected; calculating a mass estimation value of each data point through a preset dynamic model; a composite weighting algorithm is constructed by adopting a Gaussian weight method and an entropy method, the Gaussian weight method sets an expected value based on wagon balance data to carry out weight distribution, and the entropy method carries out objective weighting according to a data dispersion degree; carrying out fusion calculation on the two weighting results, and outputting a final quality estimation value; and wagon balance data are updated in real time through serial port communication, and algorithm parameters are dynamically optimized. The method is realized on an STM32 embedded platform, and can be effectively applied to commercial vehicle-mounted quality monitoring and intelligent logistics management systems.
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