Battery Performance Prediction for Adaptive Charging Control
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Solution Overview
Problem
Batteries and battery packs face challenges in predicting future performance and adapting usage to maintain optimal performance over time, as existing methods struggle to accurately assess degradation and adapt charging or usage patterns effectively.
Innovation Solution
A processor-implemented method that collects multi-dimensional battery parameters to predict future performance metrics, such as state of power, state of energy, and remaining useful life, allowing for adaptive control of charging and usage patterns to extend battery life and prevent defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-dimensional battery parameters are collected and analyzed to predict future performance, then prediction accuracy and reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The battery parameter space is segmented into multiple dimensions including voltage, current, temperature, charge-discharge rates, and cycle history. Each dimension is independently monitored and analyzed, allowing the system to build accurate predictions through cumulative dimensional analysis rather than requiring a monolithic complex model
Solution Approach 2:
The system performs preliminary data collection and preprocessing of battery parameters before prediction is needed. Historical data is stored and pre-processed in advance, so when prediction is required, the system can quickly retrieve and analyze prepared data without requiring complex real-time computation
2Duration of action of stationary object
If adaptive control of charging and usage patterns is implemented to extend battery life, then battery durability and remaining useful life are improved, but ease of operation and user convenience are reduced
Solution Approach 1:
The battery management system automatically monitors its own state and adjusts charging parameters and usage patterns without requiring user intervention. The system self-regulates voltage, current, and temperature parameters during charging and discharging to optimize battery life while maintaining normal operational convenience for the user
Solution Approach 2:
The system continuously monitors battery performance parameters and uses this feedback to dynamically adjust charging and discharge rates. Based on real-time state of charge, state of health, and environmental conditions, the controller modifies operational parameters to extend battery life while keeping the user unaware of the adjustments through transparent operation
3Productivity
If real-time data analysis is performed to optimize usage patterns, then productivity and operational efficiency are improved, but use of energy and computational resources increase
Solution Approach 1:
The system performs partial data analysis by focusing only on the most critical battery parameters and prediction models needed for immediate decision-making. Rather than analyzing all possible parameters continuously, the system selectively processes essential data points, reducing computational energy consumption while maintaining sufficient operational efficiency for battery management
Data Source
AI summary
Disclosed herein are techniques for predicting and controlling future battery performance. In some embodiments, the techniques may involve collecting a multi-dimensional set of battery parameters associated with a rechargeable battery. The techniques may further involve generating a multi-dimensional set of predicted future state of performance values of the rechargeable battery based on the multi-dimensional set of battery parameters. The techniques may further involve performing at least one of: (1) identification of a future use pattern of the rechargeable battery based at least in part on the multi-dimensional set of predicted future state of performance values; (2) presentation of an alert indicating a predicted future performance of the rechargeable battery; or (3) identification of a future use pattern of a system to which the rechargeable battery provides power and/or electrical energy based at least in part on the multi-dimensional set of predicted future state of performance values.