Products
Solutions
Resources
Pricing
Security
Products
IP
Patent Search & Analysis
Novelty Search
FTO Search
Design FTO Search
Patent Drafting & OAR
SEP Assessment
Engineering
Tech Research & Engineering Solutions
Find Solutions with TRIZ
Life Sciences
Drug Discovery & Research
SAR Data Extraction
Lead Compound
Materials R&D
Materials Research & Development
MCP Servers
REST APIs
Skill Hub
UI Widgets
Desktop
Beta
Solutions
Featured Use Cases
Competitor Tech Research
Design Risk Screening
Cosmetic Formulation
Telecom SEP Claim Chart
Resources
Featured Guides
Patentability Search vs. FTO
Find Prior Art
Review FTO Evidence
Discover
Free AI Tools
Benchmark Reports
Blog & Insights
Support
Help Center
Community & Socials
LinkedIn
X
YouTube
Substack
Pricing
Security
Patents
Literature
Patsnap Eureka AI that helps you search prior art, draft patents, and assess FTO risks, powered by patent and scientific literature data.
1
results about How to "Avoid sudden power outages" patented technology
Filter
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
A lithium battery service life prediction method based on a physical neural network
Pending
CN122283479A
Breaking through the limitations of "black box"
Preserve Feature Extraction Capability
Electrical battery
Physical neural network
This invention provides a method for predicting the lifespan of
lithium
batteries based on physical neural networks, comprising: constructing a coupled
ordinary differential equation
of
lithium
battery charge
state and thermodynamic evolution, and establishing a continuous-time dynamic model of
lithium
battery charge
change; constructing a PI-LSTM
hybrid
analysis framework based on
physical information
neural networks, and incorporating the coupled
ordinary differential equation
as a physical constraint term into the
loss function
of the PI-LSTM
hybrid
analysis framework; collecting time-series
sensing data
of
lithium battery
operation, training the PI-LSTM
hybrid
analysis framework, and completing the inversion identification of
lithium battery
physical parameters; based on the trained PI-LSTM hybrid analysis framework, outputting the
state of charge
and time to depletion of the
lithium battery
under the target operating condition, and completing the prediction of lithium battery lifespan; this invention aims to achieve accurate and interpretable prediction of lithium battery SOC and TTE in multiple devices, providing support for battery management of
electrical equipment
such as drones.
View all
Owner:
HUNAN NORMAL UNIVERSITY
Login to View More