Moving Vehicle Mass Estimation Through Dynamic Data Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating the mass of a moving vehicle are not robust and produce imprecise results due to variability in driving conditions.
Innovation Solution
A method and system that utilize parameters indicative of the vehicle's dynamic state, such as engine torque, speed, and inclination, to estimate mass by iteratively refining estimates using a sampling algorithm, considering conditions like gear engagement, brake activation, and acceleration thresholds, and averaging multiple samples for improved precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use basic dynamic parameters for mass estimation, then the estimation process is simple, but the precision and robustness of the mass estimate deteriorates under variable driving conditions
Solution Approach 1:
The method performs preliminary filtering of acceleration data by identifying and excluding segments where brake lights are activated or reverse gear is engaged, before computing the mass estimate. This preliminary action removes problematic data points that would otherwise degrade estimation accuracy, allowing simple acceleration-based formulas to produce robust results without requiring complex real-time adjustments
Solution Approach 2:
The system continuously monitors driving conditions (brake activation, gear selection, acceleration patterns) and uses this feedback to dynamically adjust the mass estimation process. When abnormal conditions are detected, the system either excludes those data points or triggers re-estimation, creating a closed-loop system that maintains precision without permanently increasing hardware complexity
2Reliability
If the estimation method accounts for multiple driving conditions and iteratively refines estimates, then the robustness improves, but the processing time and computational complexity increases
Solution Approach 1:
The estimation process is divided into distinct segments: data collection phase, filtering phase (removing brake/reverse periods), and computation phase. This segmentation allows the system to process only relevant data portions, reducing overall processing time while maintaining robustness through systematic handling of different driving condition segments
Solution Approach 2:
The method uses a simplified acceleration-based mass estimation formula rather than full dynamic modeling, applying partial action by focusing only on the most critical parameter (acceleration during coasting) while ignoring less significant factors. This partial approach achieves sufficient robustness for practical applications without the computational burden of complete physics-based models
3Measurement precision
If the system samples data during acceleration phases only, then the precision of mass estimate improves, but the frequency of successful estimations decreases due to limited acceleration opportunities
Solution Approach 1:
The system continuously monitors for acceleration events and periodically attempts mass estimation whenever suitable conditions occur. Rather than using a fixed sampling interval, the system triggers estimation attempts periodically based on detected acceleration phases, maintaining precision by insisting on appropriate conditions while maximizing estimation frequency by capturing every suitable opportunity
Solution Approach 2:
The system changes its operational parameters dynamically - switching between data collection mode, filtering mode, and estimation mode based on detected driving conditions. When acceleration is detected, it transitions to estimation mode with adjusted parameters (excluding brake periods, using specific time windows), allowing precise measurements during suitable phases while remaining productive by continuously monitoring for such phases
Data Source
Figure 1
Figure 2~3
Figure 4~5
AI summary
A method of estimating the mass of a moving vehicle comprises: i) sensing a first parameter indicative of the state of the vehicle, comprising an acceleration of the vehicle; ii) determining (10), as a function of the first parameter, whether the state of the vehicle meets a given condition to verify that the acceleration of the vehicle is higher than a respective threshold value; iii) if the state of the vehicle meets said condition, sampling (12) and storing (16) respective sequences of values of a plurality of second parameters (v[], T[], α[], k[]) that comprises: the speed of the vehicle (v[]), the torque delivered by the engine (T[]), the sensed angle of longitudinal inclination (α[]) and the transmission ratio between the engine and the drive wheels (k[]); iv) computing (18), for each value in the sequences, a respective value of estimated speed as a function of: the values of the second parameters, a previous value of speed, a presumed mass, a presumed difference between the sensed angle of inclination and the real one, and a presumed force of rolling resistance, to produce a sequence of values of estimated speed; v) computing (18) an error parameter as a function of the values of sampled and estimated speed of the vehicle; vi) repeating steps iv) and v) using M different values of the mass to compute M values of the error parameter; and vii) selecting (18), as estimated value of the mass of the vehicle (MEST), the value that produces the lowest of the M computed values of the error parameter.