Vehicle Range Estimation Using Predicted Speed Limit Offsets
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Solution Overview
Problem
Current range estimation processes for electric vehicles are inaccurate, often differing by up to 20% due to lack of standardization and dynamic environmental factors such as weather and driving habits.
Innovation Solution
A machine learning system that receives sensor data from a vehicle's hardware sensors, identifies speed limits, generates predicted speed limit offset values using a machine learning model, and determines a range estimation based on the vehicle's battery charge and predicted speed values, displaying this information on a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard range estimation processes are used, then the calculation is simple, but the accuracy is low (up to 20% error)
Solution Approach 1:
The system performs preliminary actions by collecting and storing sensor data, speed limit information, and environmental factors before range estimation is needed. The machine learning model is pre-trained with historical data to predict speed limit offsets, enabling accurate real-time range calculations without complex runtime computations.
Solution Approach 2:
The machine learning model acts as an intermediary between raw sensor data and range estimation. It processes sensor data, speed limits, and environmental factors to generate predicted speed limit offset values, which then feed into the energy consumption calculation. This intermediary layer simplifies the overall system while improving accuracy.
2Measurement precision
If dynamic environmental factors are considered, then the range estimation accuracy improves, but the computational complexity increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing sensor data, speed limit information, and environmental factors before range estimation is needed. The machine learning model is pre-trained with historical data to predict speed limit offsets, enabling accurate real-time range calculations without complex runtime computations.
Solution Approach 2:
The system selectively processes only the most relevant environmental factors and sensor data points needed for accurate range estimation, rather than analyzing every possible variable. This partial action approach maintains high accuracy while reducing computational energy requirements.
3Measurement precision
If machine learning models are used to predict speed limit offsets, then the range estimation becomes more accurate, but the processing time increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing sensor data, speed limit information, and environmental factors before range estimation is needed. The machine learning model is pre-trained with historical data to predict speed limit offsets, enabling accurate real-time range calculations without complex runtime computations.
Data Source
AI summary
An example operation may include one or more of receiving sensor data from a hardware sensor of a vehicle, where the sensor data comprises values of a route sensed as the vehicle is travelling on the route, identifying a speed limit of the route, generating a sequence of predicted speed limit offset values for the vehicle at future locations on the route based on execution of a machine learning model on the received sensor data and the identified speed limit of the route, determining a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the generated sequence of predicted speed limit offset values for the vehicle at future locations on the route, and displaying the range estimation on a user interface within the vehicle.