Vehicle Range Prediction Using Weather and Traffic Forecasts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional vehicle navigation systems fail to dynamically update routes based on weather and traffic conditions, leading to inefficiencies and safety concerns during travel.
Innovation Solution
A method and system that predicts a vehicle's travel range by integrating GPS data with weather and traffic forecasts, battery charge level, and driving conditions, providing real-time updates and recommendations to optimize route planning and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional vehicle navigation systems use only GPS data for route planning, then the system complexity is low, but the route accuracy and adaptability to weather and traffic conditions deteriorate
Solution Approach 1:
The patent combines multiple data sources including GPS data, weather forecast data, and traffic forecast data into a unified navigation system. The processor integrates these diverse inputs to dynamically calculate and update travel range predictions, resolving the contradiction by merging information streams to achieve higher route accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The navigation system is designed to perform multiple functions: it processes GPS location data, incorporates weather forecast information, integrates traffic forecast data, and dynamically adjusts route recommendations. This multi-functionality allows the system to adapt to various conditions (weather, traffic, battery level) without requiring separate specialized systems, thus improving route accuracy while maintaining reasonable system complexity.
2Adaptability or versatility
If the system dynamically updates routes based on weather and traffic conditions, then the adaptability improves, but the loss of time for data processing increases
Solution Approach 1:
The system obtains weather forecast data and traffic forecast data in advance before the vehicle departs or before route decisions are critical. By having this data pre-available, the system can quickly process route adjustments without real-time delays, thus maintaining high adaptability while minimizing data processing time loss during critical decision moments.
Solution Approach 2:
The system continuously monitors actual vehicle position, battery charge level, and compares it with predicted travel range based on weather and traffic conditions. This feedback mechanism allows the system to dynamically adjust routes in near-real-time, improving adaptability while using efficient processing algorithms that minimize time loss.
3Reliability
If the system integrates multiple data sources for travel range prediction, then the reliability of navigation improves, but the device complexity increases
Solution Approach 1:
The processor is designed as a universal computing unit that can handle multiple types of data (GPS coordinates, weather forecasts, traffic forecasts, battery levels) and perform various calculations (travel range prediction, route optimization, timing estimates). This multi-functional approach improves navigation reliability through comprehensive data integration while avoiding the need for separate specialized hardware components for each function.
Solution Approach 2:
The system automatically processes and integrates multiple data sources without requiring external intervention or complex manual coordination. The processor autonomously combines GPS data, weather forecasts, and traffic forecasts to generate travel range predictions and route recommendations, improving reliability through consistent automated processing while keeping system complexity manageable through self-service operations.
Data Source
AI summary
Provided are systems and methods for predicting a travel range of a vehicle. The method comprises receiving, using at least one processor, travel information associated with the vehicle, receiving, using the at least one processor, a weather forecast and a traffic forecast associated with the travel information, and predicting, using the at least one processor, the predicted travel range based at least in part on a battery charge level of the vehicle, the weather forecast, and the traffic forecast. In embodiments, the predicted travel range may be based on current and historical battery charge levels, current and/or historical battery discharge rates, weather forecast data, and traffic forecast data.