Battery Anode Potential Estimation for Fast-Charging Plating Control
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Solution Overview
Problem
The challenge of fast charging lithium-ion batteries is the accelerated degradation due to lithium plating, which compromises battery lifetime and efficiency, and existing methods for estimating anode potential are costly, complex, or interfere with battery operations.
Innovation Solution
A machine-learning framework using readily available battery management system data to estimate anode potential, comprising two ML systems: one for SoH indicators and another for real-time plating potential estimation, controlling charging current to prevent lithium plating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If fast charging is applied to lithium-ion batteries, then charging speed is improved, but battery degradation accelerates due to lithium plating
Solution Approach 1:
The system dynamically adjusts charging parameters (current, voltage) based on real-time anode potential estimation and battery state (SoH, temperature) to prevent lithium plating while maintaining fast charging speeds. The charging current is modulated to keep anode potential above the plating threshold.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring battery state (SoH, temperature, current, voltage) and using this information to adjust charging parameters in real-time, preventing lithium plating while enabling fast charging.
2Measurement precision
If sensors are installed to directly measure anode potential, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses machine learning models as intermediaries to estimate anode potential indirectly from easily measurable quantities (current, voltage, temperature, SoH) without requiring direct potential sensors. The ML models translate readily available battery management system data into accurate anode potential estimates.
Solution Approach 2:
The system replaces physical measurement sensors with computational models (machine learning algorithms) that calculate anode potential from electrical and thermal measurements, eliminating the need for complex sensor installations while maintaining measurement accuracy.
3Reliability
If comprehensive machine learning framework is implemented for real-time anode potential estimation, then reliability is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary training of machine learning models offline using extensive battery data, so that during real-time operation, only lightweight inference computations are required. The computationally intensive model development is done beforehand, enabling fast real-time estimation with minimal energy consumption.
Data Source
Figure 1a~1b
Figure 1c~1d
Figure 1e
AI summary
A method for real-time estimation of an anode potential of a battery (20) in connection with a battery charging event, comprising: obtaining at least one quantified State of Health [SoH] indicator of the battery (20) as output from a first machine-learning system (14); and feeding the obtained at least one quantified SoH indicator, together with battery charging data associated with the charging event, as input to a second machine-learning system (15), which provides an estimate of the anode potential of the battery (20) as output.