Battery Aging Assessment Using State-Space Discharge Trajectories
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Solution Overview
Problem
Existing battery aging prediction models face challenges such as high computational costs, complex parameterization, poor interpretability, and limited predictive accuracy due to reliance on black-box modeling and insufficient consideration of environmental and operational factors.
Innovation Solution
A battery aging assessment method based on multi-source and multi-scale high-dimensional state space modeling, utilizing time series data, state transition paths, and recurrent neural networks to predict State of Health (SOH) by analyzing discharge processes and calculating distances between state transition paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based models or multi-physics coupling models are used for battery aging prediction, then the predictive capability and insight into internal processes are improved, but the computational complexity and cost increase significantly
Solution Approach 1:
The patent replaces complex physics-based mechanical/electrochemical modeling with a data-driven machine learning approach. Specifically, it uses neural networks trained on operational data to predict battery aging, substituting the need for complex differential equations and physical parameter measurements with statistical learning models that achieve comparable accuracy with lower computational overhead.
Solution Approach 2:
The patent creates simplified surrogate models (neural networks) that copy the input-output behavior of complex physics-based models without replicating their internal complexity. These surrogate models are trained on data from physics-based simulations or experiments and then used for rapid prediction, capturing essential aging patterns without requiring the full physical model infrastructure.
2Measurement precision
If deep learning models with black-box approaches are used, then the ability to learn complex nonlinear relationships is improved, but the interpretability and transferability deteriorate
Solution Approach 1:
The patent incorporates attention mechanisms that provide feedback about which input features are most important for predictions. The attention weights serve as interpretability signals, indicating which operational parameters (temperature, charge rate, voltage) most influence aging predictions at different battery states, thus maintaining interpretability while using deep learning architectures.
Solution Approach 2:
The patent segments the battery aging prediction task into multiple components: feature extraction from operational data, state representation learning, and aging prediction. By breaking down the black-box model into interpretable stages with meaningful intermediate representations, it enables analysis of how different operational factors contribute to aging at different phases of battery life.
3Ease of manufacture
If statistical model-based methods are used, then the simplicity and ease of implementation are improved, but the consideration of physical mechanisms and predictive accuracy deteriorate
Solution Approach 1:
The patent creates a composite modeling approach that combines data-driven statistical methods with physics-informed features. The model integrates operational data analysis with domain knowledge about battery degradation mechanisms, creating a hybrid approach that maintains implementation simplicity while improving predictive accuracy through physically meaningful feature engineering and model architecture design.
Data Source
AI summary
Disclosed is a battery aging assessment method based on multi-source and multi-scale high-dimensional state space modeling in the field of energy storage in renewable power systems. The method includes: acquiring a time series of each discharge process within a preset number of discharge cycles of a sample battery; determining a first state transition path and a second state transition path based on discharge parameters corresponding to the time series; establishing a benchmark working-state transition path; calculating multiple sample distances between the second state transition path and the benchmark working-state transition path; training a battery aging assessment model using the sample distances as input and corresponding target state-of-health values as output; calculating a target distance between a state transition path of a to-be-predicted target battery and the benchmark working-state transition path.