EV Charging Station Routing Using Battery State Prediction
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Solution Overview
Problem
Existing systems for recommending electric vehicle charging stations fail to accurately predict battery state information due to neglecting factors like the vehicle's condition, driving patterns, weather, and traffic conditions, leading to inaccurate estimates of drivable distance and charging needs.
Innovation Solution
A system that collects and compares vehicle driving information, including battery state, driving conditions, weather, and traffic data, with pre-measured data from various environments to predict battery state information and recommend charging stations with the shortest movement route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If battery state is predicted based only on voltage or current measurement, then the prediction method is simple, but the prediction accuracy is low
Solution Approach 1:
The patent segments the battery state prediction into multiple independent measurement components: voltage measurement, current measurement, driving pattern analysis, weather condition analysis, and traffic condition analysis. Each component is measured and processed separately, then integrated to form the comprehensive battery state prediction, thereby improving accuracy without overwhelming system complexity
Solution Approach 2:
The patent merges multiple measurement data sources (voltage, current, driving patterns, weather, traffic) into a unified battery state prediction system. By combining these diverse data streams through the server's processing capabilities, the system achieves high prediction accuracy while maintaining reasonable complexity through modular architecture
2Measurement precision
If multiple factors (driving pattern, weather, traffic) are considered for battery state prediction, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a server as an intermediary between the electric vehicle and the user terminal. The server receives and processes multiple complex measurement factors (driving patterns, weather, traffic), performs the comprehensive battery state prediction, and returns the simplified result to the user terminal, thereby managing system complexity while maintaining high prediction accuracy
Solution Approach 2:
The patent adds temporal and environmental dimensions to the battery state prediction by incorporating driving patterns over time, weather conditions, and traffic conditions. This multi-dimensional approach enables comprehensive accuracy improvement while the server's processing capability manages the resulting complexity
3Device complexity
If charging station recommendation is provided without considering specific driving routes, then the recommendation system is simple, but the recommendation accuracy is low
Solution Approach 1:
The patent applies local quality by analyzing battery state and recommending charging stations specifically for each driving route segment. The server processes measurements taken at specific locations along the driving route and provides localized charging recommendations, ensuring high accuracy for each specific route while maintaining manageable system complexity through targeted analysis
Data Source
AI summary
A system is for recommending a charging station of an electric vehicle applied specific driving routes. The system includes a vehicle driving information collection unit, a database unit that includes a first vehicle driving information database, a second vehicle driving information database a charging station database, and a driving route database to generate first expected battery state information, and a charging station guide unit that determines whether the battery is required to be charged according to the first expected battery state information and analyzes a charging station with a shortest movement route in the charging station database and transmits the analyzed result to a user terminal.