AI Battery Charge Control for Predictive Performance Management
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Solution Overview
Problem
Current battery technologies lack effective predictive maintenance and optimization methods, leading to inefficiencies in charging and discharging processes, particularly in varying environmental conditions and usage scenarios.
Innovation Solution
The integration of artificial intelligence and machine learning techniques, including sensor data collection, model training, and rule-based systems, to predict battery performance and failure, optimize charging cycles, and dynamically manage battery operations within switchable battery fabrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional battery management systems are used, then device complexity is reduced, but battery performance optimization and predictive maintenance capabilities deteriorate
Solution Approach 1:
The system collects and stores sensor data from battery operations in advance, building comprehensive historical datasets that enable future predictive analysis. This preliminary data accumulation allows the AI models to make accurate predictions about battery performance and failure modes without adding real-time complexity to the battery management system.
Solution Approach 2:
An AI intermediary layer is introduced between the sensor data and the battery management decisions. This AI layer processes raw sensor data, identifies patterns, and generates predictive insights, effectively mediating between the complex sensor inputs and the simplified decision-making processes in the battery management system.
2Productivity
If AI models are trained with comprehensive sensor data, then charging cycle optimization improves, but data processing time and computational resources increase
Solution Approach 1:
The system implements a phased approach where AI models are initially trained with a subset of sensor data to achieve baseline charging optimization. Additional sensor data is then incorporated iteratively to progressively improve optimization performance, rather than requiring all data to be processed simultaneously, thus reducing initial computational burden and training time.
3Adaptability or versatility
If switchable battery fabric architecture is implemented, then battery cell utilization and energy management flexibility improve, but system complexity and control requirements increase
Solution Approach 1:
The battery fabric architecture implements dynamic switching capabilities that allow battery cells to be reconfigured in real-time based on operational conditions, performance metrics, and charging requirements. This dynamic reconfiguration enables the system to adapt to varying demands while the AI charge controller automatically manages the complexity of switching operations, presenting a simplified interface to users.
Solution Approach 2:
The system incorporates continuous feedback loops where sensor data from battery cells is monitored, analyzed by AI models, and used to automatically adjust charge controller decisions and battery fabric configurations. This feedback mechanism enables the system to self-optimize and manage complexity autonomously, reducing the need for manual intervention and simplifying user interaction.
Data Source
AI summary
Uses of artificial intelligence in battery technology including a method that includes receiving a trained model, receiving sensor data from at least one sensor associated with a battery, and executing the trained model by a processor. Executing the trained model includes providing the sensor data as input to the trained model to generate a model output. The method also includes sending, from the processor to a charge controller coupled to the battery, a control signal that is based on the model output and automatically, by the charge controller, initiating or terminating charging of the battery based on the control signal.