EV Battery Charging Prediction Controller Using Temperature and Driving Data
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Solution Overview
Problem
Current methods for predicting charging information of electric vehicle batteries are inaccurate due to neglecting temperature, driving habits, and environmental conditions, leading to unreliable estimates of charging time and energy requirements.
Innovation Solution
A system and method utilizing a controller in the electric vehicle to collect data on target moving distance, estimate required energy, and calculate predicted charging information by considering driving habits, environmental conditions, and battery state, including temperature, to provide a more accurate prediction of charging time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional charging prediction methods are used, then the prediction process is simple, but the prediction accuracy is low due to neglecting temperature, driving habits, and environmental conditions
Solution Approach 1:
The prediction system is segmented into multiple independent modules: a data acquisition module that collects temperature, driving habit, and environmental condition data; an energy consumption calculation module that processes this data; and a charging time prediction module that outputs the final prediction. This segmentation allows each module to handle specific aspects of the complex prediction task, improving overall accuracy while maintaining manageable system complexity.
Solution Approach 2:
The system performs preliminary data collection and analysis before the actual charging prediction. It pre-acquires temperature data, driving habit patterns, and environmental conditions, then pre-calculates energy consumption based on these factors before determining the final charging time. This preliminary action ensures all relevant parameters are considered, enhancing prediction accuracy without overwhelming system complexity.
2Measurement precision
If multiple factors (temperature, driving habits, environmental conditions) are considered in prediction, then prediction accuracy improves, but calculation complexity increases
Solution Approach 1:
The controller serves multiple functions: it acts as a data acquisition device that collects temperature, driving habit, and environmental condition data; as a calculation device that processes energy consumption; and as a prediction device that determines charging time. This multi-functionality consolidates what would otherwise require separate specialized devices, reducing overall system complexity while maintaining comprehensive data collection for accurate predictions.
Solution Approach 2:
The system utilizes data that is already being collected by the electric vehicle's existing sensors and control systems. The controller leverages available temperature data, driving pattern information, and environmental sensors that are already part of the vehicle's operational infrastructure, rather than requiring entirely new measurement systems. This self-service approach reduces data collection complexity while enabling comprehensive prediction analysis.
Data Source
AI summary
A system and a method for predicting charging information of a battery of an electric vehicle are disclosed. The method is for an electric vehicle driven by energy stored in the battery and includes steps configured to be executed by a controller provided in the electric vehicle. The method includes: collecting a target moving distance from a start location to a target location of the electric vehicle; estimating required energy required for the battery to move the target moving distance; and obtaining required charging information of the battery based on the estimated required energy.