EV Range Estimation Using Dynamic Likelihood Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Electric vehicle drivers face challenges in estimating the remaining range to their destination due to factors like traffic, weather, and driving style, leading to uncertainty and potential stranding, as conventional range calculation methods are based on historical data rather than real-time influences.
Innovation Solution
A system and method that calculates the likelihood of arriving at a destination by considering vehicle data, current route, and environmental factors, using algorithms to assess energy consumption and adjust vehicle settings or advise drivers to take more efficient routes, with tiered responses to manage energy consumption and prevent stranding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional range calculation methods based on historical data are used, then the system is simple to operate, but the reliability of range estimation deteriorates due to inability to account for real-time influences like traffic, weather, and driving style
Solution Approach 1:
The system continuously monitors actual energy consumption and compares it with predicted consumption, using this feedback to recalculate and update the remaining range estimate in real-time, thereby improving reliability through dynamic adjustment based on actual vehicle conditions and external factors
Solution Approach 2:
The system pre-calculates range estimates based on historical data and typical conditions, then applies real-time adjustments when actual conditions deviate, allowing the system to prepare baseline estimates while adapting to changing conditions without requiring complete real-time recalculation
2Reliability
If the system provides detailed information and multiple alerts to drivers, then the reliability of informing drivers improves, but the ease of operation deteriorates due to information overload
Solution Approach 1:
The system applies a tiered alert approach where it provides minimal warnings only when the likelihood of reaching the destination falls below specific thresholds (e.g., 20% or 10%), rather than continuously informing drivers of all range variations, thus maintaining accuracy while avoiding information overload
Solution Approach 2:
The system adjusts the level of information provided based on the specific situation - providing detailed range information when critical thresholds are approached while maintaining simple operation during normal conditions, thereby tailoring the interface complexity to the actual need
Data Source
AI summary
A system and method for controlling an electric-vehicle is provided. The system and method calculates a likelihood of arriving at a destination based on vehicle data and a current route. The likelihood is compared to least a first threshold and a second threshold. A first action is implemented when the likelihood is less than the first threshold and greater than the second threshold. A second action being different from the first action is implemented when the likelihood is less than the second threshold.