Battery EOL Prediction Using Diagnostic Cycle Electrochemical Data
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Solution Overview
Problem
Current battery life prediction systems for electric vehicles are inefficient, requiring extensive cycles and time frames to estimate end-of-life (EOL), which hampers maintenance, product design, and research due to the reliance on charge and discharge cycles.
Innovation Solution
A prediction system that utilizes diagnostic cycles to measure electrochemical data, determines features associated with battery degradation, and employs a machine learning model to accurately predict EOL, thereby accelerating testing and improving estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerous charge and discharge cycles are used to predict battery life, then prediction accuracy is improved, but testing time and productivity are worsened
Solution Approach 1:
The patent applies preliminary action by performing diagnostic cycles with specific electrochemical reactions before full lifecycle testing to pre-identify degradation features. This preliminary characterization allows the system to establish baseline degradation patterns early, enabling more accurate predictions without requiring thousands of complete charge-discharge cycles, thus resolving the contradiction between prediction accuracy and testing time
Solution Approach 2:
The patent changes testing parameters by using diagnostic cycles with specific electrochemical reactions (such as rate capability tests, impedance spectroscopy, and partial charge-discharge cycles) instead of traditional full lifecycle charge-discharge cycles. These parameter changes enable the extraction of degradation features at different states of charge and rates, improving prediction accuracy while significantly reducing the number of cycles required
2Measurement precision
If extended time frames are used for battery testing, then degradation measurement accuracy is improved, but maintenance and research efficiency are worsened
Solution Approach 1:
The patent replaces the mechanical time-based degradation measurement system with an electrochemical reaction-based measurement system. By using specific electrochemical reactions (such as lithium plating, dendrite formation, and SEI layer growth) as proxies for degradation, the system can measure degradation mechanisms directly through electrochemical signals rather than waiting for long-term capacity fade, thus improving measurement accuracy while reducing testing duration
Solution Approach 2:
The patent introduces electrochemical reactions as intermediary processes that mediate between the battery's internal degradation mechanisms and external measurement. These reactions (such as cyclic voltammetry, electrochemical impedance spectroscopy, and rate capability tests) serve as intermediaries that amplify and make observable the subtle degradation processes occurring inside the battery, enabling accurate degradation measurement without extended time frames
3Reliability
If traditional charge-discharge cycles are used, then battery life prediction is achieved, but system complexity and cost are increased
Solution Approach 1:
The patent extracts and isolates specific degradation features from the complex battery system by using targeted electrochemical reactions. Instead of monitoring all possible parameters throughout thousands of cycles, the system extracts key degradation indicators (such as lithium inventory loss, active material degradation, and impedance changes) through specific electrochemical tests, simplifying the overall testing system while maintaining prediction reliability
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 system enables more precise and rapid estimation of battery life by identifying degradation features through diagnostic cycles, allowing for better prediction of EOL across varying operating conditions, thus enhancing maintenance, research, and reducing costs.
Implementation Method 1
measure electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle
Data Source
AI summary
System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.