Car Navigation Energy Prediction via Dynamic Route Optimization
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Solution Overview
Problem
Existing car navigation systems face challenges in accurately predicting energy consumption for vehicle routes, as they either require special knowledge to set vehicle parameters or rely on emission test cycle data that does not accurately reflect real-world landform and traffic conditions, leading to suboptimal energy-efficient route selection.
Innovation Solution
A car navigation system that uses emission test cycle data to calculate test cycle characteristic values, allowing users to input basic vehicle specifications without specialized knowledge, and predicts energy consumption based on traffic and landform data, guiding users along energy-efficient routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the energy consumption is predicted using a physical model with detailed vehicle parameters, then the prediction accuracy is improved, but the ease of operation deteriorates because users need specialized knowledge to set parameters
Solution Approach 1:
The patent introduces an intermediary component (vehicle parameter acquisition unit) that automatically obtains vehicle parameters from external sources such as vehicle manuals, catalogs, or databases. This mediator translates complex technical parameters into easily accessible information, allowing users to input data without specialized knowledge while maintaining prediction accuracy through comprehensive parameter collection.
Solution Approach 2:
The system implements self-service by automatically acquiring vehicle parameters from publicly available sources without requiring user expertise. The navigation device autonomously collects and processes vehicle specification data, eliminating the need for users to manually configure complex engine or motor parameters while ensuring accurate energy consumption predictions.
2Ease of operation
If the energy consumption is predicted based on emission test cycle fuel consumption rate, then the ease of operation is improved, but the measurement precision deteriorates because it cannot accurately reflect landform and traffic conditions
Solution Approach 1:
The patent segments the energy consumption prediction into multiple independent components: basic vehicle parameters, route-specific landform data, and real-time traffic conditions. Each segment is processed separately and then integrated, allowing the system to maintain operational simplicity while achieving high prediction accuracy by considering both vehicle characteristics and environmental factors.
Solution Approach 2:
The system transitions from static emission test cycle data to dynamic energy consumption prediction by continuously incorporating real-time traffic information and landform characteristics. This dynamic approach allows the prediction to adapt to changing road conditions, maintaining simplicity for the user while significantly improving accuracy through updated environmental parameters.
3Measurement precision
If the route search is performed using actual energy consumption data from vehicles on the move, then the measurement precision is improved, but the adaptability deteriorates because the route can only be found for roads with available running results
Solution Approach 1:
The patent creates a universal prediction model that can handle multiple vehicle types and route conditions through a unified physical framework. The system uses standardized vehicle parameters and route characteristics that can be applied across different vehicle models and geographic locations, enabling the navigation to provide accurate energy consumption predictions for any route without requiring pre-collected data for each specific road.
Data Source
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AI summary
A car navigation system solves the problem that the emission test cycle fuel consumption rate indicates the energy consumption per unit distance covered following a prescribed emission test cycle procedure, and therefore, cannot produce a highly accurate prediction value reflecting the landform and the traffics along a route. So, the test cycle characteristic values independent of the vehicle model for vehicle energy consumption in a specified running condition are calculated (S1215), and the vehicle energy consumption parameter of the engine or the motor is estimated from the basic vehicle specifications such as the vehicle weight, the power supply type, the emission test cycle fuel consumption rate, the test cycle characteristic values in the emission test cycle and the vehicle characteristics. Using the basic vehicle specifications and the energy consumption parameter, the energy consumption is predicted (S4508) taking the landform and traffics into consideration.