Electric Scooter Tandem Ride Detection via Mass Estimation
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Solution Overview
Problem
Tandem riding on electric scooters is a significant cause of accidents, and existing technologies lack effective methods to detect and prevent multiple persons from riding simultaneously.
Innovation Solution
A method for detecting a tandem ride condition on an electric scooter involves estimating the mass on the scooter using indirect means, such as movement and motor data, and comparing it to a statistical mass value based on user profile data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If indirect mass estimation using movement and motor data is implemented, then measurement precision for detecting tandem ride condition is improved, but device complexity is reduced by avoiding direct weighing devices
Solution Approach 1:
The patent replaces direct mechanical weighing devices with an indirect estimation system using movement data from inertial measurement units and force/power data from the electric motor. This substitution eliminates the need for complex mechanical scales while achieving accurate mass detection through computational analysis of acceleration, speed, and motor performance data.
Solution Approach 2:
The patent introduces an intermediary estimation process that uses movement data and motor data as mediators to infer mass indirectly. Instead of directly measuring mass, the system uses these intermediate parameters (acceleration, speed, motor force) to calculate and estimate the total mass on the scooter, thereby simplifying the detection system while maintaining precision.
2Measurement precision
If user profile data with statistical mass values is stored and used, then detection accuracy for each user is improved, but loss of time for data processing and comparison occurs
Solution Approach 1:
The patent implements preliminary action by pre-storing statistical mass values in user profiles during the user setup phase. This allows the system to have reference data ready in advance, enabling rapid comparison with real-time estimated mass without requiring complex real-time analysis or extensive data processing during actual tandem ride detection.
Solution Approach 2:
The patent uses partial action by storing only the essential statistical mass value in each user profile, which is the key parameter needed for tandem ride detection. This selective storage approach minimizes data processing requirements while maintaining sufficient accuracy for the detection purpose, avoiding unnecessary data collection and processing.
3Measurement precision
If correction factors for electric scooter mass and driver mass are applied, then measurement precision of estimated mass is improved, but device complexity and calculation complexity increase
Solution Approach 1:
The patent applies parameter changes by introducing correction factors that adjust the estimated mass based on known parameters (electric scooter mass and driver mass). These correction factors modify the raw estimation to account for systematic biases, thereby improving accuracy. The correction is implemented through relatively simple multiplicative or additive adjustments rather than complex algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method allows for accurate detection of tandem ride conditions for each user, enhancing safety by preventing multiple persons from riding the scooter simultaneously, thereby reducing the risk of accidents.
Implementation Method 1
The movement data may include acceleration data measured by an inertial measurement unit of the scooter
Implementation Method 2
the method may comprise comparing the force or power data and the acceleration data, and determining a coefficient based on the comparison
Data Source
AI summary
A method for detecting a tandem ride condition on an electric scooter (10), the scooter (10) comprising an electric motor (16), the method comprising estimating (110) a mass on the scooter (10), and comparing (120) the estimated mass to a statistical mass value determined based on stored data associated with a user profile. Furthermore, an electric scooter control system (30) and an electric scooter (10) are also disclosed.