Neural Network Battery Charging Under Unstable Current Conditions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Unstable charging currents during battery charging lead to safety risks and reduced battery life due to internal structure failure, posing threats to personal safety and battery longevity.
Innovation Solution
A method involving real-time collection of battery charging state data, including current and temperature, which is used to train a neural network model to adjust variable resistances and control charging currents, ensuring stable charging parameters within preset error conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional charging methods are used, then charging speed is maintained, but charging current instability causes safety risks and reduced battery life
Solution Approach 1:
The patent implements a feedback mechanism where the neural network model continuously receives real-time charging state data (current, temperature, voltage) and adjusts charging parameters dynamically. The model outputs predicted future states and compares them with safe thresholds, creating a closed-loop control system that stabilizes charging current while maintaining safety without requiring overly complex hardware modifications.
Solution Approach 2:
The patent replaces traditional mechanical/electrical charging control systems with an intelligent software-based neural network model. Instead of using complex circuitry and multiple sensors to detect and adjust charging parameters, the system uses a trained neural network that processes charging data and predicts future states, substituting physical control mechanisms with computational intelligence to simplify the overall system architecture.
2Stability of the object's composition
If real-time data collection and neural network training are implemented, then charging current stability is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary action by training the neural network model offline before actual charging operations. The model is trained using historical charging data to learn patterns and predict future charging states. This pre-training phase separates the computationally intensive learning process from the time-critical charging process, allowing the model to make rapid predictions during actual charging without requiring real-time training, thus maintaining current stability while minimizing time loss.
3Reliability
If multiple parameters (current and temperature) are monitored and controlled, then battery safety is enhanced, but system complexity and control difficulty increase
Solution Approach 1:
The patent merges multiple monitoring functions (current detection, temperature sensing, voltage measurement) into a single integrated neural network model. Instead of implementing separate control systems for each parameter, the model receives all charging state data as unified input and generates coordinated control outputs. This consolidation simplifies the control architecture by handling multiple parameters through one intelligent system, making the charging process easier to operate while enhancing safety through comprehensive monitoring.
Data Source
AI summary
Disclosed in the present application are a battery charging method and apparatus, and a device and a medium. The method includes; collecting, at a preset time interval, battery charging state data of a rechargeable battery within each of the preset time intervals in real time; when the battery charging state data within any one of the preset time intervals is collected, using the battery charging state data within the preset time interval as a training data set, inputting the training data set to an initial neural network model for training, and during the process of training, updating a preset network parameter on the basis of the difference value between a model output value corresponding to each piece of battery charging state data and a preset threshold until difference value between model output value corresponding to present battery charging state data and the preset threshold meets a preset error condition.