AI Battery Charging Control for Failure Prediction
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Solution Overview
Problem
Existing battery technologies lack efficient methods for predicting battery performance and failure, leading to suboptimal charging strategies and potential safety issues.
Innovation Solution
The implementation of artificial intelligence (AI) and machine learning (ML) techniques to collect sensor data from batteries, train models to predict performance and failure, and automate charging rules, enabling predictive and optimized battery management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional battery management methods are used, then device complexity is low, but battery performance prediction and failure prediction capabilities are insufficient
Solution Approach 1:
An AI system serves as an intermediary between sensor data and battery management decisions. The system collects sensor data from batteries, processes it through machine learning models to predict performance and failure, and outputs recommendations to charge controllers and maintenance staff, thereby resolving the contradiction by adding intelligent processing capability without directly complicating the battery itself
Solution Approach 2:
The system implements continuous feedback loops where sensor data from battery operation is constantly collected, analyzed by AI models, and used to adjust charging strategies and predict future performance. This feedback mechanism enables proactive battery management, improving reliability through data-driven decisions while keeping the underlying battery technology unchanged
2Productivity
If automated charging rules are implemented, then charging efficiency is improved, but control system complexity increases
Solution Approach 1:
The AI system enables charging infrastructure to serve itself by automatically analyzing battery data, determining optimal charging parameters, and executing charging decisions without manual intervention. The system self-adjusts charging strategies based on real-time battery state, improving charging efficiency while the complexity is managed through automated rule execution rather than human oversight
Solution Approach 2:
The system dynamically changes charging parameters (current, voltage, timing) based on AI analysis of battery state and predicted performance. By adjusting these parameters automatically according to data-driven insights, the system improves charging efficiency and battery health without requiring complex manual control procedures
3Adaptability or versatility
If switchable battery fabric is used, then battery adaptability is improved, but device complexity increases
Solution Approach 1:
The battery fabric implements dynamic reconfiguration capability where cells can be switched between different connections (series, parallel, individual) based on real-time performance data and AI predictions. This dynamic adaptability allows the system to optimize battery pack performance by selectively activating or deactivating specific cells, improving versatility while managing complexity through automated switching logic
Data Source
AI summary
Uses of artificial intelligence in battery technology including a method that includes receiving input data associated with at least one sensor. The method includes executing at least one trained model by a processor. Executing the trained model comprises providing the input data to the at least one trained model to generate a model output. The method further includes determining, based on the model output, whether to initiate or terminate selectively charging a first battery cell of a battery.