Battery User Profile Time Series Model for Consumption Adjustment
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Solution Overview
Problem
Existing electronic devices face inaccuracies in calculating remaining battery life due to performance configurations and user behavior, leading to faster battery dissipation than estimated, especially when users fail to recharge on time or continue using devices below the charge threshold.
Innovation Solution
An electronic device equipped with a processor and storage that uses a time series model to update a user profile based on battery measurements and operational data, adjusting battery consumption by comparing usage patterns to improve accuracy and extend battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the device uses standard battery life calculation methods, then the calculation process is simple, but the accuracy of remaining battery life estimation deteriorates due to user behavior variations and performance configurations
Solution Approach 1:
The system continuously monitors actual battery consumption and compares it with predicted consumption from the time series model. When deviations are detected (such as unusual usage patterns or performance changes), the model is retrained to adapt to new conditions, creating a closed-loop feedback system that maintains high accuracy over time
Solution Approach 2:
The system performs preliminary actions by collecting and storing battery measurement data and operational data during normal device operation. This data is accumulated in advance and used to train the time series model, enabling accurate predictions before battery depletion occurs without requiring complex real-time calculations
2Measurement precision
If the device monitors battery consumption frequently to improve accuracy, then the remaining battery life estimation accuracy improves, but the energy consumption for monitoring increases
Solution Approach 1:
The system applies partial monitoring by selectively measuring battery parameters at strategically chosen time points rather than continuous monitoring. The time series model interpolates between these partial measurements to predict battery life, achieving high accuracy with reduced measurement frequency and lower energy consumption
Solution Approach 2:
The time series model acts as an intermediary that translates sparse battery measurement data into accurate remaining battery life predictions. Instead of requiring frequent direct measurements, the model processes occasional measurements along with operational data to generate continuous accurate estimates, reducing the energy burden of monitoring
3Duration of action of moving object
If the device adjusts battery consumption based on user profile, then the battery life is extended, but the device complexity increases due to profile management
Solution Approach 1:
The system automatically creates and updates user profiles through self-service mechanisms. It monitors usage patterns, performance configurations, and battery consumption over time, automatically generating personalized profiles without requiring manual user input or complex configuration interfaces, thereby extending battery life while maintaining simplicity
Solution Approach 2:
The system dynamically adjusts battery consumption parameters based on the user profile and current operational conditions. The time series model predicts optimal power allocation for different components, automatically changing parameters such as CPU frequency, display brightness, and wireless transmission power to extend battery life while adapting to user behavior patterns
Data Source
AI summary
In some examples, an electronic device comprises a battery; a storage device storing a user profile, the user profile comprising a usage pattern of the battery; and a processor coupled to the battery and the storage device, the processor to: receive a battery measurement of the battery and operational data of a first component of the electronic device; calculate a battery consumption of the first component based on the battery measurement; compare the battery consumption to the usage pattern; update, based on the comparison, the user profile using a time series model, wherein inputs to the time series model include the battery measurement and the operational data; and adjust a battery consumption of the electronic device based on the updated user profile.