Electric-Assisted Bicycle Route Energy Estimation from Riding Habits
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Solution Overview
Problem
Riders of electric-assisted bicycles face difficulty in accurately determining if the battery has sufficient power to reach their destination due to varying power consumption based on different riding styles and lack of precise estimation methods.
Innovation Solution
A method and system that estimates power consumption and time consumption by using a server device to generate a planned route based on starting and destination locations, incorporating route information and riding habit data, and displays the results through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If riders charge the battery to full capacity to ensure sufficient power, then the reliability of reaching the destination is improved, but the loss of time and energy is increased due to unnecessary charging time
Solution Approach 1:
The system performs preliminary calculation of required battery capacity before the ride by estimating power consumption based on route information and riding habits. This allows riders to determine the exact charging requirement in advance without needing to charge to full capacity, thereby reducing unnecessary charging time while ensuring sufficient power for the journey.
2Ease of operation
If riders rely solely on distance traveled to estimate battery usage, then the ease of operation is improved, but the measurement precision of power consumption is worsened due to varying riding styles
Solution Approach 1:
The system changes the estimation parameters from simple distance-based metrics to a comprehensive set of parameters including route information (elevation, distance, terrain type) and riding habit information (power consumption patterns, riding style). This multi-parameter approach maintains ease of operation through automated calculation while significantly improving measurement precision by accounting for varying riding conditions and styles.
3Measurement precision
If the system calculates power consumption using detailed route information and riding habit data, then the measurement precision of power consumption is improved, but the device complexity is increased
Solution Approach 1:
The system introduces an intermediary calculation layer that processes route information and riding habit data to produce power consumption estimates. This intermediary layer acts as a mediator between the raw input data (route details, riding habits) and the final output (power consumption estimate), simplifying the overall system architecture by breaking down the complex estimation process into manageable computational steps that can be efficiently executed.
Data Source
AI summary
A method and a system for estimating power consumption and time consumption of electric assisted bicycle are provided. The method including the following steps. A start location and a destination location are received. A planned route is generated according to the starting location and the destination location. A riding time consumed by the electric assisted bicycle to travel the planned route is estimated according to route information of the planned route and riding habit information of a rider. A power consumption consumed by the electric assisted bicycle to travel the planned route is estimated according to the riding time, the route information and the riding habit information. The riding time and the power consumption associated with a first riding mode are displayed through a user operation interface.