Battery Response Prediction Using Temporal Convolutional Networks
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Solution Overview
Problem
Conventional battery state prediction techniques suffer from inaccuracy and are often tailored to specific types of batteries, devices, and operating conditions, making them inadequate for general use in battery management systems.
Innovation Solution
The implementation of a battery value temporal convolutional neural network that receives observed battery system values and prospective control profiles to predict future battery system values, allowing for improved accuracy and adaptability across various battery systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional battery state prediction techniques are used, then the system can operate with simple methods, but the prediction accuracy is poor
Solution Approach 1:
The patent replaces conventional mechanical/mathematical prediction methods with a neural network-based system. The neural network learns complex patterns from historical battery data and provides accurate predictions without requiring explicit mathematical models of battery behavior, thus improving accuracy while managing complexity through software-based solutions.
Solution Approach 2:
The system transforms the prediction approach by changing from fixed conventional parameters to dynamic neural network parameters that adapt to different battery conditions. The neural network processes multiple input parameters (voltage, current, temperature, historical states) and produces optimized predictions, allowing the system to handle complexity through intelligent parameter transformation.
2Adaptability or versatility
If conventional prediction techniques tailored to specific batteries are used, then they may work for that specific case, but they lack adaptability to different battery types and conditions
Solution Approach 1:
The patent implements a universal neural network system that can handle multiple battery types, operating conditions, and prediction tasks through a single framework. The network is trained on diverse data and can adapt to different scenarios without requiring separate models for each battery type, thus achieving both versatility and accuracy simultaneously.
Solution Approach 2:
The system employs dynamic adaptation where the neural network can adjust its behavior based on the specific battery being monitored. The network learns from historical data and can dynamically adapt to different battery chemistries, aging states, and operating conditions, maintaining high accuracy across diverse applications rather than being static and battery-specific.
3Measurement precision
If more complex prediction models are used to improve accuracy, then prediction precision improves, but the computational time and resources increase
Solution Approach 1:
The patent performs preliminary training of the neural network offline using extensive historical battery data. This pre-training phase captures complex patterns and relationships, allowing the network to make accurate predictions in real-time with minimal computational overhead during actual battery monitoring, thus resolving the trade-off between model complexity and prediction speed.
Solution Approach 2:
The system uses the neural network as a computational copy or surrogate model that replicates the complex behavior of batteries without requiring real-time simulation of underlying physical processes. This copied behavioral model provides accurate predictions much faster than detailed physical simulations, reducing computational time while maintaining precision.
Data Source
AI summary
Observed battery system values may be received for one or more battery systems associated with one or more devices. The observed battery system values may characterize operation of the one or more battery systems over time. Prospective control profiles may be determined for the battery systems. The prospective control profiles may correspond to a time-varying patterns of battery system charge and/or discharge for prospective courses of action for the devices over time. Predicted battery system values may be determined for the battery systems by applying a trained temporal convolutional neural network to the prospective control profiles and the observed battery system values. A designated control profile may be selected based on the plurality of predicted battery system values. An instruction may be sent to a designated device to execute a course of action corresponding with the designated control profile.