Deep Neural Network for Battery Capacity Prediction
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Solution Overview
Problem
Current battery lifetime prediction models are inaccurate and inefficient, limiting the ability of manufacturers to forecast the lifespan of electric and hybrid vehicles and energy storage devices effectively.
Innovation Solution
A deep learning approach using a deep neural network that collects and analyzes energy consumption data from electricity meters, employing feature engineering, data partitioning, and sequential machine learning techniques to predict battery capacity, incorporating parameters like temperature, charge/discharge rates, and State of Charge (SOC), utilizing Multi Linear Regression, Neural Networks, and Long Short-Term Memory (LSTM) models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural network with sequential machine learning techniques is used, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The prediction model is segmented into three sequential machine learning techniques: Multi Linear Regression for initial prediction, Neural Networks for intermediate processing, and LSTM for temporal pattern recognition. This segmentation allows each component to handle specific aspects of the prediction task, improving overall accuracy while managing computational complexity through modular architecture.
Solution Approach 2:
The patent transitions from traditional single-model approaches to a multi-dimensional sequential processing architecture. By adding temporal dimension handling through LSTM after spatial/feature processing through Neural Networks and Regression, the system captures complex patterns across multiple dimensions of battery data, significantly improving prediction accuracy.
2Reliability
If more energy consumption data is collected and analyzed, then prediction reliability is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary data processing by collecting and preprocessing energy consumption data before it is needed for prediction. Data is stored and pre-analyzed in advance, allowing the sequential machine learning models to process pre-prepared features rather than raw data, significantly reducing real-time processing time while maintaining prediction reliability.
3Productivity
If feature engineering is applied to remove irrelevant features, then model efficiency is improved, but information loss may occur
Solution Approach 1:
The feature engineering process transforms raw energy consumption data into meaningful features by changing parameters and representations. Relevant features are selected and transformed to enhance model efficiency, while the sequential architecture ensures that important information is preserved through multiple processing stages, balancing efficiency improvement with information retention.
Data Source
AI summary
A computer-implemented method predicting a life span of a battery storage unit by employing a deep neural network is presented. The method includes collecting energy consumption data from one or more electricity meters installed in a structure, analyzing, via a data processing component, the energy consumption data, removing one or more features extracted from the energy consumption data via a feature engineering component, partitioning the energy consumption data via a data partitioning component, and predicting battery capacity of the battery storage unit via a neural network component sequentially executing three machine learning techniques.