Battery State Prediction via Temporal Convolutional Neural Networks

The patent applies preliminary action by pre-training the temporal convolutional neural network on historical battery data before actual prediction is needed. The model learns patterns from past battery states, usage conditions, and control protocols during training, so that when prediction is required, the system can quickly generate accurate forecasts without complex real-time calculations. This pre-computation approach enables high accuracy while maintaining operational simplicity.

The patent replaces conventional mechanical/electrical battery monitoring approaches with an information-processing system using temporal convolutional neural networks. Instead of relying solely on physical sensors and simple circuit analysis, the system uses deep learning algorithms to process battery data, identify patterns, and predict future states. This substitution enables more accurate predictions by capturing complex non-linear relationships that traditional methods miss, while the computational nature of the system allows for efficient real-time operation.

The patent applies preliminary action by using the trained neural network to predict future battery states before charging occurs. The system analyzes current battery conditions, proposed charging rates, and historical data to forecast how the battery will respond to fast charging conditions. This prediction capability allows the system to assess the potential impact on battery lifetime before committing to a charging protocol, enabling informed decisions that balance charging speed with battery health preservation.

The patent implements feedback by continuously monitoring actual battery behavior during charging and comparing it against predictions from the neural network. The system uses this feedback to refine its understanding of battery responses to fast charging conditions. Over time, the model learns which charging protocols cause the most degradation and which are safer, allowing it to provide increasingly accurate predictions and recommendations that protect battery lifetime while maximizing charging speed when safe to do so.

The patent applies universality by training a single temporal convolutional neural network to handle multiple types of input data representing different battery histories, usage conditions, and operating scenarios. The model is designed to process various input formats and conditions through its convolutional architecture, which automatically adapts to different data patterns. This universal approach allows the system to maintain high accuracy across diverse conditions without requiring separate specialized models for each scenario, thereby managing complexity while improving prediction precision.

The patent replaces manual analysis of different battery conditions with an automated neural network system. Instead of requiring explicit programming to handle various historical patterns and operating conditions, the system uses deep learning to automatically learn and recognize patterns from training data. The temporal convolutional architecture processes multiple input dimensions simultaneously, extracting relevant features and relationships without human intervention. This substitution reduces the complexity burden on the system designer while enabling accurate predictions across diverse conditions.