Hybrid Transformer Battery Forecasting for EV Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current learning models for electric vehicle (EV) battery usage forecasting have limited learning capabilities and do not leverage large language models (LLMs) for predicting time series battery data, failing to utilize contextualized embeddings from Transformer networks.
Innovation Solution
A hybrid transformer architecture is employed, combining Masked Embedding Models (MEM) and Generative Pre-trained Transformer (GPT) to transform time series battery data into multi-channel images, extract contextual information, and generate forecasts for battery characteristics and usage, enabling task-specific processing to control electrical systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current learning models are used for battery usage forecasting, then the system can perform basic predictions, but the learning capabilities are limited and forecasting accuracy is insufficient
Solution Approach 1:
The patent combines multiple Transformer-based models (BERT and GPT architectures) into a hybrid framework that integrates contextualized embedding extraction with generative forecasting capabilities. This merging of models allows the system to leverage both the contextual understanding strengths of BERT and the sequential prediction strengths of GPT, thereby simultaneously improving forecasting accuracy and learning capabilities without requiring separate systems.
Solution Approach 2:
The hybrid Transformer architecture serves multiple functions: it extracts contextualized embeddings from battery data, performs multi-step time series forecasting, and adapts to various battery characteristics and usage patterns. This multi-functional design enables the single system to improve both measurement precision and adaptability by handling diverse forecasting tasks with a unified model that can learn different patterns across various battery types and operating conditions.
2Loss of information
If traditional Transformer networks are used, then contextualized representations can be extracted, but the models do not leverage large language models for predicting time series battery data
Solution Approach 1:
The system performs preliminary extraction of contextualized embeddings using BERT-based models before conducting the actual forecasting task. By pre-processing the battery data to extract rich contextual representations first, the model retains comprehensive information while the subsequent GPT component can then efficiently generate predictions based on these pre-extracted features, thereby reducing information loss and improving forecasting productivity.
Solution Approach 2:
The patent introduces contextualized embeddings as an intermediary representation between the input battery data and the forecasting output. The BERT model transforms raw battery time series data into rich contextual embeddings, which then serve as input features for the GPT forecasting model. This intermediary step preserves nuanced information from the original data while providing a structured format that enhances the efficiency and accuracy of the subsequent prediction process.
3Ease of operation
If existing learning models are used, then basic forecasting can be performed, but the models are not configured to visualize embeddings trained from a Transformer network
Solution Approach 1:
The system creates visual representations (copies) of the high-dimensional contextualized embeddings extracted by the Transformer model. By projecting these complex embeddings into visual formats that can be analyzed and interpreted, the system makes the internal model states accessible without altering the underlying forecasting mechanism. This allows operators to inspect and understand model behavior while maintaining the robustness of the original forecasting algorithm.
Data Source
AI summary
Technologies and techniques for controlling an electrical system via battery forecasting for an electrical system. Time series data is transformed into a series of battery image data including a multi-channel image representing a plurality of battery characteristics and/or battery usage characteristics derived from the time series battery data. An image vector is generated for each respective battery image data of the series, and each image vector is transformed via a first portion of a transformer architecture for sequential data processing and positional encoding. Contextual information is extracted from the transformed image vectors, where each image vector is transformed via a second portion of the transformer architecture using learned weights from the first portion to generate a forecast of future battery characteristics and/or battery usage characteristics. A control command is generated for task-specific processing to modify operation of the electrical system, based on the forecast.