Vehicle Battery Temperature Conditioning for Predictive Charging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Eco-friendly vehicle batteries experience reduced charging efficiency and durability due to temperature variations, leading to energy waste and prolonged charging times.
Innovation Solution
A system and method for conditioning vehicle batteries by adjusting temperature based on driver intention, battery condition, and driving information, using a thermal management system controlled by processors that analyze various data factors to determine a target temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If battery temperature conditioning is performed based solely on driver input, then system complexity is reduced, but charging efficiency and battery durability are compromised due to inability to account for actual battery conditions and driving patterns
Solution Approach 1:
The system enables the vehicle to automatically determine driver charging intention by analyzing driving patterns, battery conditions, and environmental data without requiring explicit driver input. The processor executes algorithms that self-assess whether the driver intends to charge based on historical driving data, current battery state, and predicted driving needs, thereby maintaining charging efficiency while reducing system complexity.
Solution Approach 2:
The system performs preliminary battery temperature conditioning before the driver actually needs to charge by predicting charging intention in advance. By analyzing driving patterns and battery state, the system proactively conditions the battery to optimal temperature ranges, ensuring charging efficiency is maintained while the driver may not even need to manually initiate the process.
2Measurement precision
If comprehensive determination factor data is collected and analyzed, then battery conditioning precision is improved, but data processing complexity and energy consumption increase
Solution Approach 1:
The system extracts only the most relevant determination factors from the collected data for actual processing. The processor identifies and prioritizes key parameters such as battery temperature, state of charge, driving pattern matches, and environmental conditions, while filtering out less critical data. This selective extraction maintains high conditioning precision while reducing overall data processing complexity and energy consumption.
Solution Approach 2:
The system applies different levels of data analysis to different determination factors based on their importance. Critical parameters like battery temperature and charging history receive intensive analysis, while less critical factors receive lighter processing. This localized quality approach ensures high precision for key conditioning decisions while minimizing overall computational complexity.
3Reliability
If battery temperature is adjusted to optimal range before charging, then charging efficiency and battery durability are improved, but energy consumption and charging time increase
Solution Approach 1:
The system applies partial temperature conditioning only when necessary to achieve sufficient charging efficiency. Rather than always conditioning the battery to the absolute optimal temperature range, the system determines the minimum necessary adjustment based on current battery state and predicted charging duration. This partial action approach maintains battery durability while minimizing unnecessary energy consumption during temperature adjustment.
Solution Approach 2:
The system performs temperature conditioning in advance during periods when the vehicle is parked or not in use, rather than conditioning during active driving or right before charging. By utilizing idle time for battery temperature adjustment, the system ensures optimal battery conditions are achieved without extending the actual charging time when the driver needs the vehicle.
4Measurement precision
If driver charging intention is predicted using multiple data factors, then conditioning accuracy is improved, but information processing requirements and system complexity increase
Solution Approach 1:
The system merges multiple determination factors into a unified charging intention assessment. The processor combines data from driving patterns, battery conditions, environmental factors, and historical charging behavior into a single integrated evaluation. This merging approach maintains high conditioning accuracy by considering all relevant factors simultaneously while reducing the complexity of managing separate analysis streams for each individual factor.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances charging efficiency, reduces energy waste, and maintains battery durability by optimizing temperature conditions before charging.
Implementation Method 1
controlling, based on the target temperature, a temperature controller to cool or heat the vehicle battery
Implementation Method 2
controlling, based on the target temperature, a temperature controller to cool or heat the vehicle battery
Data Source
AI summary
A system and a method for conditioning a vehicle battery are proposed. The system for conditioning a vehicle battery may include a temperature controller configured to adjust a temperature of the vehicle battery, and one or more processors configured to obtain determination factor data. The determination factor data may include vehicle condition data and/or driving information data. The one or more processors may be further configured to determine a target temperature of the battery based on the determination factor data, and control the temperature controller to adjust the vehicle battery to the target temperature.