Vehicle Battery Power Control Using Big Data Driving Patterns
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Solution Overview
Problem
Conventional vehicle power control techniques fail to manage abrupt changes in available power during charging/discharging, leading to instability and negative impacts on driving dynamics due to fixed power maps that cannot adapt to continuous charging/discharging beyond a reference time.
Innovation Solution
A system utilizing big data from distributed cloud servers to generate a driving power pattern, which a vehicle controller uses to dynamically limit battery charging/discharging power based on continuous time or accumulation amounts, adjusting power limitation rates to reflect actual usage and driving habits, thereby preventing abrupt power changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed data map with predetermined available power values is used for battery power control, then the control system is simple and easy to implement, but the system cannot adapt to continuous charging/discharging beyond reference time, causing abrupt power changes and driving instability
Solution Approach 1:
The system pre-calculates and stores power limitation values in advance based on continuous charging/discharging time and accumulation amount of continuous charging/discharging power. These pre-calculated values are stored in memory and directly applied when limits are reached, avoiding complex real-time calculations while maintaining adaptability to prolonged charging/discharging operations
Solution Approach 2:
The system dynamically adjusts available power values based on continuous charging/discharging time and accumulation amount, transitioning from fixed predetermined values to time-dependent dynamic values. This allows the power control to adapt to prolonged charging/discharging operations while maintaining system simplicity through pre-defined adjustment rules
2Reliability
If the high-voltage battery is protected by rapidly limiting vehicle power using actual voltage value when continuous charging/discharging exceeds reference time, then battery protection is achieved, but driving stability and dynamic performance deteriorate due to abrupt power change
Solution Approach 1:
The system calculates and stores power limitation values in advance for various continuous charging/discharging times and accumulation amounts. When battery protection is needed, these pre-calculated cushioning values are applied gradually rather than abrupt limitation, protecting the battery while maintaining driving stability through smooth power transitions
Solution Approach 2:
The system changes the parameter of available power from fixed predetermined values to time-dependent dynamic values based on continuous charging/discharging duration and accumulation amount. This parameter transformation enables gradual power limitation that protects the battery while avoiding abrupt changes that would compromise driving stability
3Adaptability or versatility
If predetermined maximum available charging/discharging power is stored in controller as data map based on state of charge and temperature, then power control is straightforward, but the system cannot optimize power values based on actual driving habits and regional variations
Solution Approach 1:
The system incorporates feedback from actual charging/discharging usage patterns by calculating accumulation amount of continuous charging/discharging power and using this information to dynamically adjust available power values. This feedback mechanism enables the system to adapt to actual usage patterns and optimize power control based on real operating conditions rather than relying solely on predetermined data maps
Data Source
AI summary
A system for controlling vehicle power using big data is provided. The system includes a big data server that receives vehicle driving related data, processes and analyzes the received data to generate a driving power pattern of the vehicle, and stores the driving power pattern. A vehicle controller determines whether to limit charging/discharging power of a battery based on continuous charging/discharging time or an accumulation amount of continuous charging/discharging power of the battery and calculates battery charging/discharging power to be limited based on the driving power pattern received from the big data server when the charging/discharging power of the battery is limited.