Gm global technology operations llc (20240300377). DYNAMIC CONTROL OF BATTERY CHARGING simplified abstract

DYNAMIC CONTROL OF BATTERY CHARGING

Organization Name

gm global technology operations llc

Inventor(s)

Ibrahim Haskara of Macomb MI (US)

Bharatkumar Hegde of Bloomfield Hills MI (US)

Chen-fang Chang of Bloomfield Hills MI (US)

DYNAMIC CONTROL OF BATTERY CHARGING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240300377 titled 'DYNAMIC CONTROL OF BATTERY CHARGING

The abstract describes a system for controlling a battery system in real-time during the charging process. The system includes a processor that acquires charging parameter measurements, estimates a dynamic performance variable related to an electrochemical phenomenon in the battery system, and selects a stored charging profile based on the measurements.

• The processor applies a charging current to the battery system based on the selected profile.
• It inputs the measurements, stored profile, and relation to a learning agent for evaluation through a reward-based learning process.
• The stored charging profile is periodically updated based on the learning process.

Potential Applications: - Electric vehicles - Renewable energy storage systems - Portable electronic devices

Problems Solved: - Optimizing charging processes for improved battery performance - Enhancing battery efficiency and longevity

Benefits: - Increased battery lifespan - Improved charging efficiency - Enhanced overall battery performance

Commercial Applications: Title: "Smart Battery Management System for Enhanced Performance" This technology can be utilized in electric vehicle charging stations, solar energy storage systems, and consumer electronics to optimize battery charging processes and improve overall performance.

Prior Art: Research on battery management systems, dynamic performance estimation in electrochemical systems, and machine learning algorithms for battery optimization can provide insights into similar technologies.

Frequently Updated Research: Stay updated on advancements in battery management systems, electrochemical performance estimation techniques, and machine learning algorithms for battery optimization to enhance the system's capabilities.

Questions about the technology: 1. How does the system adapt to different types of batteries and charging requirements? 2. What are the potential implications of using machine learning in battery management systems?

Original Abstract Submitted

a system for control of a battery system includes a processor connected to the battery system and configured to perform, in real time during a charging process, acquiring a set of charging parameter measurements, estimating a dynamic performance variable related to an electrochemical phenomenon occurring within the battery system during the charging process, and selecting a stored charging profile from a stored relation based on the charging parameter measurements. the processor is further configured to perform applying a charging current to the battery system based on the selected stored charging profile, inputting the charging parameter measurements, the stored charging profile and the stored relation to a learning agent, and evaluating the stored charging profile according to a reward-based learning process, the learning process including estimating a performance value associated with the stored charging profile. the processor is configured to periodically update the stored charging profile based on the learning process.