Battery Model Prediction Algorithm for Current Availability
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Solution Overview
Problem
Existing battery technologies fail to accurately predict power and energy availability, leading to premature device shutdowns and unnecessary recharging due to inadequate consideration of physical variables and environmental conditions.
Innovation Solution
A model-based prediction algorithm that updates battery models using time-varying currents and voltage responses to estimate maximum current output, accounting for internal and external variables, including temperature and state of charge, to provide accurate power and energy availability predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a battery indicator estimates remaining charge, then users can monitor battery status, but the indicator is often inaccurate leading to premature shutdowns or unnecessary recharging
Solution Approach 1:
The patent applies parameter changes by transitioning from simple voltage-based charge estimation to a comprehensive model that incorporates multiple parameters including voltage, current, temperature, and time. The battery model dynamically adjusts parameters like internal resistance and capacitance based on operating conditions, enabling accurate prediction of remaining charge and discharge curves, thereby preventing both premature shutdowns and unnecessary recharging events
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring battery voltage, current, and temperature, then using this data to update the battery model in real-time. The system compares predicted charge levels with actual measurements and adjusts the model parameters accordingly, creating a closed-loop system that improves estimation accuracy and prevents unreliable device operation
2Duration of action of moving object
If mobile device designers focus on creating devices that consume less power, then battery life is extended, but processing capabilities are diminished
Solution Approach 1:
The patent applies dynamics by implementing a dynamic battery model that continuously adapts to changing operating conditions rather than using static parameters. The model adjusts internal resistance, capacitance, and discharge rate predictions based on real-time temperature, current, and voltage measurements, enabling the system to optimize power delivery dynamically - allowing higher processing power when battery conditions permit while extending effective battery life through intelligent power management
Solution Approach 2:
The patent applies preliminary action by using the battery model to predict future charge levels and discharge curves before the battery actually depletes. This allows the system to proactively manage power consumption, plan processing tasks, and provide accurate battery life predictions, enabling users to complete important functions before recharging without suddenly losing processing capability
3Adaptability or versatility
If battery monitoring systems analyze more variables related to environment and conditions, then adaptive capabilities are improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the battery monitoring system into distinct functional modules: voltage measurement, current measurement, temperature sensing, and a separate battery model computation engine. Each module handles a specific aspect of battery monitoring, making the overall complex system manageable and maintainable while still analyzing multiple environmental variables comprehensively
Solution Approach 2:
The patent introduces an intermediary battery model that acts as a mediator between raw sensor data (voltage, current, temperature) and the user interface or power management decisions. This model layer processes and integrates multiple input variables, performing the complex analysis of discharge curves and charge predictions while presenting simplified information to the rest of the system, thereby managing complexity effectively
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
The algorithm ensures accurate prediction of maximum current output, reducing unnecessary recharging and device shutdowns by accounting for real-time changes and environmental factors, thereby extending battery life and improving mobile device performance.
Implementation Method 1
providing a time-varying current to the battery and updating a battery model based on a voltage response of the battery to the time-varying current
Data Source
AI summary
This application relates to methods and apparatus for predicting power and energy availability of a battery. The prediction is made based on a given amount of time, which represents a period in which the battery may be required to operate. Additionally, a learning cycle is incorporated to update a battery model of the battery with certain parameters. The battery model is updated by introducing a time-varying current to the battery and analyzing the voltage response of the battery. A model-based predictive algorithm is used in combination with the battery model to predict battery output parameters based on variables derived from the learning cycle and additional inputs supplied to the model-based predictive algorithm. After one or more iterations, or using a simplified model-based equation, the model-based predictive algorithm can provide an accurate prediction for the maximum current that the battery can supply for a predetermined period of time.