Battery Life Prediction Through Spliced Decay Curves
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Solution Overview
Problem
Existing battery life prediction methods, such as electrochemical principle prediction and genetic algorithm prediction, are computationally intensive and suffer from large prediction errors and difficulty in explaining the decay pattern of battery state of health.
Innovation Solution
A battery data processing method that involves obtaining preset life decay curves for different life decay types, extracting curve segments based on predicted operating conditions, and splicing these segments end-to-end to create a predicted life decay curve, thereby reducing prediction errors by considering the decay pattern under various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electrochemical principle prediction is used, then battery life prediction can be achieved, but computational complexity increases and prediction error becomes large
Solution Approach 1:
The patent segments the battery life decay process into multiple distinct decay types (e.g., linear decay, exponential decay, logarithmic decay). Each decay type is modeled separately with its own curve segment, allowing the complex overall decay process to be broken down into simpler, more manageable components that can be processed independently and then combined.
Solution Approach 2:
The patent changes the parameter representation by using different mathematical functions (linear, exponential, logarithmic) to describe different phases of battery decay. Instead of using a single complex electrochemical model, the system switches between multiple simpler parametric models, each suited for specific decay phases, thereby reducing computational complexity while maintaining accuracy.
2Productivity
If genetic algorithm prediction is used, then prediction efficiency improves, but prediction error increases and decay pattern explanation becomes difficult
Solution Approach 1:
The patent divides the battery decay process into multiple decay types, each represented by a separate curve segment. This segmentation allows the system to capture different decay patterns (linear, exponential, logarithmic) that occur at different stages of battery life, improving prediction accuracy while maintaining computational efficiency through the use of simple parametric models for each segment.
Solution Approach 2:
The patent introduces dynamic switching between different decay models based on the battery's current state. The system dynamically selects and transitions between linear, exponential, and logarithmic decay models as the battery progresses through different life stages, allowing the prediction model to adapt to changing decay patterns without requiring complex global optimization algorithms.
3Measurement precision
If multiple life decay types are considered with curve segment splicing, then prediction accuracy improves, but calculation complexity increases
Solution Approach 1:
The patent segments the battery life decay into multiple distinct phases, each modeled by a simple parametric curve (linear, exponential, or logarithmic). By dividing the complex decay process into smaller segments with simpler mathematical representations, the overall calculation becomes more manageable while capturing the nuanced behavior of battery degradation at different stages.
Solution Approach 2:
The patent performs preliminary classification of decay types and pre-defines the mathematical forms for each decay segment. This preliminary action allows the system to prepare the appropriate model structures in advance, reducing the computational burden during actual prediction by avoiding complex real-time model selection and fitting procedures.
Data Source
AI summary
A method includes: obtaining preset life decay curves of a target battery under multiple different life decay types; extracting one curve segment from a respective one of the preset life decay curves; splicing multiple curve segments end-to-end to obtain a predicted life decay curve of the target battery; and determining a predicted life of the target battery based on the predicted life decay curve.