Digital Twin Battery Model for State of Health Assessment
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Solution Overview
Problem
Existing methods for determining the state of health (SoH) of rechargeable batteries are often inaccurate, leading to shortened battery life, increased costs, and environmental impact.
Innovation Solution
A computer-implemented method that receives charge cycle data to determine the initial state of charge of a battery pack using coulomb counting, and employs a digital twin battery model to predict battery voltages and correct states of charge, thereby improving the accuracy of SoH assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SoH prediction methods are used, then the assessment process is simple, but the accuracy of SoH assessment deteriorates
Solution Approach 1:
The patent segments the SoH assessment process into multiple independent modules: initial SoC determination module (using coulomb counting and OCV methods), charge cycle data acquisition module, digital twin modeling module, and SoH calculation module. Each module handles a specific aspect of the assessment, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces a digital twin battery model as an intermediary between raw charge cycle data and SoH assessment results. This virtual model simulates battery behavior and serves as a mediator to translate measured data into accurate SoH predictions, enhancing measurement precision without requiring direct complex interactions between sensors and assessment algorithms.
2Duration of action of stationary object
If accurate SoH prediction is achieved, then battery life is extended, but the assessment complexity increases
Solution Approach 1:
The patent performs preliminary determination of initial state of charge (SoC) using coulomb counting and open-circuit voltage methods before actual charge cycles begin. This preliminary action establishes an accurate baseline for the digital twin model, enabling more accurate SoH predictions throughout the battery's operational life and effectively extending usable battery lifespan through better management.
Solution Approach 2:
The patent implements a feedback mechanism where charge cycle data (current, voltage, temperature) is continuously fed into the digital twin battery model, which then adjusts and refines SoH predictions based on actual battery behavior. This closed-loop feedback system maintains high accuracy over extended battery life while managing complexity through iterative refinement rather than complex upfront modeling.
3Measurement precision
If noise level exceeds threshold, then initial SoC determination using coulomb counting becomes unreliable, but alternative methods increase complexity
Solution Approach 1:
The patent applies dynamic threshold-based logic to select appropriate initial SoC determination methods. When noise level exceeds a first threshold, the system dynamically switches from direct coulomb counting to alternative approaches (such as using charge cycle reversal or combining multiple measurement methods). This dynamic adaptation maintains measurement precision under varying noise conditions while managing complexity through conditional logic rather than always using the most complex method.
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 method enhances the accuracy of battery state of health assessment, leading to better management and maintenance of batteries, improved reliability, extended battery life, and reduced costs and environmental impact.
Implementation Method 1
determining an initial state of charge of the battery pack using coulomb counting by reversing the charge cycle data
Data Source
AI summary
In one aspect, based on at least a received first state of health of a battery pack and an initial state of charge of the battery pack, a method may include determining, by a state of charge estimator of a digital twin battery model, states of charges for the battery pack. Based on the states of charges for the battery pack, the method may include determining, by a voltage predictor of the digital twin battery model, predicted battery voltages. Based on the predicted battery voltages, the method may include determining, by a state of charge corrector of the digital twin battery model, a voltage difference between the predicted battery voltages and measured voltages. Based on the voltage difference, the method may include correcting, by the state of charge corrector, the states of charges to generate corrected states of charges for the battery pack.