Battery Lifespan Prediction Using Transfer Learning Across Environments
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Solution Overview
Problem
The unpredictable decrease in battery capacity of electric vehicles due to various factors such as non-uniform use environments and initial failures makes it challenging to accurately predict battery lifespan, leading to uncertain battery replacement cycles.
Innovation Solution
An apparatus and method using machine learning to predict battery lifespan by converting a battery lifespan model trained in one environment to another using transfer learning, incorporating a cell lifespan model and a pack lifespan model trained with data from different operating environments, allowing for accurate prediction of battery lifespan and efficient management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery lifespan prediction methods are used, then the prediction process is simple, but the prediction accuracy is low due to uncertain battery capacity decrease from various causes
Solution Approach 1:
The battery lifespan prediction model is segmented into multiple components: a basic lifespan model trained on first battery cell data from a first operating environment, and a transfer learning module that adapts this model using second battery cell data from a second operating environment. This segmentation allows the system to build prediction accuracy progressively while managing complexity through modular architecture.
Solution Approach 2:
A basic lifespan model is pre-trained using first battery cell data collected in a first operating environment before deployment. This preliminary training establishes a foundation model that can later be adapted to specific operating conditions through transfer learning, reducing the need for extensive environment-specific training data.
2Measurement precision
If a battery lifespan model is trained specifically for each operating environment, then the prediction accuracy for that environment is high, but the training data requirement and model adaptation complexity increase
Solution Approach 1:
A transfer learning module acts as an intermediary between the basic lifespan model and environment-specific applications. This module uses second battery cell data from a second operating environment to adapt the pre-trained basic model, enabling the system to achieve environment-specific prediction accuracy while maintaining a single versatile model architecture that can serve multiple operating conditions.
3Measurement precision
If more training data from various environments is collected, then the prediction accuracy improves, but the data collection time and storage requirements increase
Solution Approach 1:
A basic lifespan model is pre-trained using first battery cell data from a first operating environment before deployment. This preliminary training establishes a foundation model that can later be adapted to specific operating conditions through transfer learning, reducing the need for extensive environment-specific training data.
Solution Approach 2:
The model adapts to different operating environments by changing its parameters through transfer learning. Instead of collecting extensive data from each new environment, the system adjusts model parameters using second battery cell data from the target operating environment, achieving accurate predictions with minimal additional data collection time.
Data Source
AI summary
A vehicle may include a display; a battery; a battery sensor configured to acquire battery data of the battery; and a processor configured to acquire an output of a battery lifespan model associated with the battery data, predict a lifespan value of the battery based on the output of the battery lifespan model, and display an indication associated with the lifespan value of the battery on the display. The battery lifespan model may include a cell lifespan model associated with a basic lifespan model trained using first battery cell data collected in a first operating environment. The cell lifespan model may use second battery cell data collected in a second operating environment, and a pack lifespan model may be trained using battery pack data collected in the first operating environment.