Adaptive Hidden Layer for Battery State Estimation
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Solution Overview
Problem
Conventional battery state estimation technologies face challenges in accurately predicting the remaining useful life (RUL) of batteries due to overfitting and increased costs associated with iterative learning, especially when dealing with insufficient data and the need for multiple LSTM models.
Innovation Solution
An apparatus and method utilizing a neural network with an adaptive hidden layer that selects the most similar pre-trained predictive model based on input battery data, applying weights proportional to similarity, and minimizing differences between predicted and actual data across cycles, thereby improving prediction accuracy and reducing relearning costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only one LSTM model is used for battery state estimation, then device complexity is reduced, but prediction accuracy deteriorates due to inability to capture diverse battery characteristics
Solution Approach 1:
The patent divides the single LSTM model into multiple specialized LSTM models, each trained on specific battery data characteristics. The adaptive hidden layer segments the prediction task by selecting appropriate models based on input data similarity, thereby improving prediction accuracy without requiring all models to be active simultaneously.
Solution Approach 2:
The patent introduces adaptive parameters (similarity metrics and weights) that dynamically change based on input battery data. The adaptive hidden layer calculates similarity between input data and training data, then adjusts model selection and weight allocation accordingly, allowing the system to adapt to different battery conditions without increasing structural complexity.
2Measurement precision
If multiple LSTM models are increased to improve prediction accuracy, then prediction accuracy improves, but overfitting occurs when battery data is insufficient
Solution Approach 1:
The patent performs preliminary training of multiple LSTM models on diverse battery data characteristics before actual prediction. Each model is pre-trained on specific data patterns, and the adaptive hidden layer learns similarity metrics in advance. This preliminary preparation allows the system to select appropriate pre-trained models for new inputs without requiring extensive real-time learning data.
Solution Approach 2:
The adaptive hidden layer acts as an intermediary between the input battery data and the multiple LSTM models. It calculates similarity metrics and determines weight allocations, effectively mediating the interaction between limited input data and multiple trained models. This intermediary layer prevents direct overfitting by distributing the prediction task across multiple models based on learned similarity rather than forcing a single model to fit all data.
3Measurement precision
If iterative learning is performed each time new battery data is input, then prediction accuracy improves, but cost increases due to repeated training
Solution Approach 1:
The patent performs comprehensive training of multiple LSTM models in advance on diverse battery data before deployment. The adaptive hidden layer also performs preliminary learning of similarity metrics and weight allocation strategies. Once this preliminary training is complete, the system only requires lightweight inference operations (similarity calculation and model selection) for new inputs, eliminating the need for repeated full training cycles.
Solution Approach 2:
The patent creates multiple copies of LSTM models trained on different battery data characteristics during the preliminary phase. These pre-trained model copies can be rapidly selected and applied to new inputs without requiring retraining. The adaptive hidden layer copies the appropriate pre-trained model based on similarity metrics, avoiding the computational cost of iterative learning while maintaining prediction accuracy.
4Adaptability or versatility
If internal parameters are changed according to new battery data, then adaptability improves, but device complexity increases due to parameter management
Solution Approach 1:
The adaptive hidden layer serves as an intermediary that manages parameter changes between input battery data and the LSTM models. Instead of directly modifying complex internal parameters of multiple models, the adaptive hidden layer calculates similarity metrics and determines weight allocations, simplifying the parameter management process while maintaining adaptability to different battery conditions.
Solution Approach 2:
The patent introduces a structured parameter change mechanism where the adaptive hidden layer dynamically adjusts similarity metrics and weight parameters based on input data characteristics. These parameter changes are localized to the adaptive hidden layer rather than propagating through all model parameters, making the adaptation process more manageable and less complex while maintaining versatility.
Data Source
AI summary
An apparatus for estimating a state of a battery includes a memory configured to store a program of a neural network including a plurality of pre-trained predictive models and an adaptive hidden layer; and includes at least one processor configured to execute the program. The program includes an instruction for receiving battery data of a target battery, inputting the battery data to the adaptive hidden layer, selecting one predictive model from the plurality of pre-trained predictive models through the adaptive hidden layer, inputting the battery data to the selected predictive model, and outputting prediction data for a remaining useful life of the target battery through the selected predictive model.