Battery Status Data Reconstruction for Accurate Aging Estimation
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Solution Overview
Problem
Conventional techniques for estimating the aging status of rechargeable batteries are memory-intensive, computationally demanding, and sensitive to missing or flawed status data, leading to inaccurate aging estimation and operational control.
Innovation Solution
The use of an autoencoder artificial neural network (ANN) to process initial status data, encoding and reconstructing it to derive hidden operating variables, allowing for precise aging estimation and operational control without the need for direct measurement of all variables, and enabling error detection and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional aging estimation techniques are used, then aging status can be determined, but memory requirements and computing capacity demands increase significantly
Solution Approach 1:
The patent extracts and processes only the most relevant features from status data using the autoencoder neural network, rather than analyzing all raw data. The network learns to identify and retain only the critical patterns needed for accurate aging estimation, effectively removing redundant information and reducing memory requirements while maintaining estimation accuracy.
Solution Approach 2:
The patent segments the aging estimation process into distinct computational stages: data preprocessing, autoencoder feature extraction, and final aging calculation. This segmentation allows each stage to operate on optimized data representations, reducing the overall computational burden and memory requirements compared to processing complete raw datasets.
2Measurement precision
If complete status data are collected for accurate aging estimation, then measurement precision improves, but data transmission bandwidth requirements increase
Solution Approach 1:
The autoencoder network extracts essential aging-related features from status data locally at the battery management system, transmitting only these compressed feature representations to remote servers. This extraction approach maintains aging estimation accuracy while dramatically reducing the bandwidth required for data transmission compared to sending complete raw status datasets.
3Measurement precision
If more status data are processed, then aging estimation accuracy improves, but computational demand increases
Solution Approach 1:
The patent performs preliminary feature extraction and data transformation using the autoencoder network before the main aging estimation computation. This preliminary action pre-processes the data into an optimized representation that requires less computational power for the subsequent aging calculation, reducing overall computing capacity demands while preserving accuracy.
Solution Approach 2:
The patent replaces traditional mechanical/computational aging estimation methods with a neural network-based autoencoder system. This substitution enables the system to automatically learn and extract relevant features from status data without requiring complex manual feature engineering or intensive computational algorithms, reducing the overall computing capacity required.
4Productivity
If conventional aging estimation methods are used, then aging status can be determined, but the system becomes sensitive to missing or flawed status data
Solution Approach 1:
The patent converts the presence of flawed or incomplete status data into a benefit by training the autoencoder network to recognize and compensate for such errors. The network learns normal data patterns during training and can identify deviations caused by sensor failures or transmission errors, effectively converting data quality issues into opportunities for error detection and correction, thereby improving reliability.
5Measurement precision
If all operating variables are directly measured, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent introduces the autoencoder neural network as an intermediary that processes and infers unmeasured operating variables from available status data. Instead of directly measuring all operating variables with physical sensors, the network acts as a virtual sensor, calculating hidden variables through pattern recognition and mathematical transformation, thereby reducing device complexity while maintaining measurement precision.
Solution Approach 2:
The patent replaces physical measurement systems with computational inference mechanisms. Rather than installing additional sensors to directly measure all operating variables, the system uses the autoencoder network to computationally derive unmeasured variables from existing measurements, substituting mechanical sensing infrastructure with software-based inference and reducing overall device complexity.
Data Source
AI summary
A method for processing status data of a battery comprises applying an autoencoder artificial neural network to initial status data. Reconstructed status data are obtained therefrom. The method comprises carrying out an aging estimation based on the reconstructed status data in order to obtain a status indicator which is indicative of an aging status of the battery.