EV Energy Consumption Modeling for Real-World DTE Accuracy
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Solution Overview
Problem
The energy consumption efficiency of electric vehicles is not optimized in various test conditions, leading to reduced accuracy on actual roads and inability to optimize energy consumption efficiency control factors.
Innovation Solution
A system and method for modeling energy consumption efficiency of electric vehicles, which involves receiving learning data from multiple vehicles, learning an energy consumption efficiency model, and transmitting this model to the vehicles to update their existing models, thereby optimizing energy consumption efficiency control factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy consumption efficiency is determined through tests on chassis dynamometer in constant temperature laboratory, then test conditions are controlled and reproducible, but the accuracy of energy consumption efficiency in various actual road conditions is lowered
Solution Approach 1:
The system collects energy consumption data from multiple electric vehicles under various actual road conditions before determining the final energy consumption efficiency. By gathering preliminary data from diverse real-world scenarios (different temperatures, road grades, driving patterns), the system builds a comprehensive dataset that accounts for actual operating conditions, thereby improving measurement precision while maintaining reliability through systematic data collection and analysis
Solution Approach 2:
The system adjusts and optimizes energy consumption efficiency control factors based on learned patterns from multiple vehicles and conditions. By dynamically changing parameters such as power distribution ratios, regenerative braking amounts, and thermal management settings based on learned models, the system adapts to various actual road conditions, improving accuracy across different scenarios rather than relying on fixed laboratory conditions
2Device complexity
If basic energy consumption efficiency model is used without continuous learning and updating, then system complexity is reduced, but energy consumption efficiency control factor optimization is compromised
Solution Approach 1:
The system implements a feedback mechanism where energy consumption data from multiple electric vehicles is continuously collected, analyzed, and used to update the energy consumption efficiency model. The learned model is then transmitted back to vehicles to update their basic models, creating a closed-loop system that continuously improves accuracy. This feedback-driven approach enables optimization of control factors while managing complexity through iterative learning rather than complex real-time calculations
Solution Approach 2:
The system enables electric vehicles to self-update their energy consumption efficiency models by receiving learned models from the server. Each vehicle autonomously integrates the learned model with its basic model, allowing the system to improve overall accuracy without requiring complex centralized control for each individual vehicle, thus balancing complexity and precision
Data Source
AI summary
A system for modeling energy consumption efficiency of an electric vehicle and a method thereof are disclosed. The system includes a communication device that communicates with a plurality of electric vehicles and includes a controller that receives learning data from the plurality of electric vehicles, learns an energy consumption efficiency model based on the received learning data, and transmits the energy consumption efficiency model in which the learning is completed to the plurality of electric vehicles.