View source for 18393857. Method and Apparatus for Predictive Diagnosis of a Device Battery of a Technical Device Using a Trace Graph Model simplified abstract (Robert Bosch GmbH)

=Method and Apparatus for Predictive Diagnosis of a Device Battery of a Technical Device Using a Trace Graph Model=


==Organization Name==

[[:Category:Robert Bosch GmbH|Robert Bosch GmbH]]


[[Category:Robert Bosch GmbH]]

==Inventor(s)==

[[:Category:Antonia Reiter of Stuttgart (DE)|Antonia Reiter of Stuttgart (DE)]][[Category:Antonia Reiter of Stuttgart (DE)]]

[[:Category:Christian Simonis of Leonberg (DE)|Christian Simonis of Leonberg (DE)]][[Category:Christian Simonis of Leonberg (DE)]]

[[:Category:Cosmin Lazar of Cluj-Napoca (RO)|Cosmin Lazar of Cluj-Napoca (RO)]][[Category:Cosmin Lazar of Cluj-Napoca (RO)]]

[[:Category:Daniel Asztalos-balasy of Targu Mures (RO)|Daniel Asztalos-balasy of Targu Mures (RO)]][[Category:Daniel Asztalos-balasy of Targu Mures (RO)]]

[[:Category:Maria Belen Bescos Del Castillo of Moensheim (DE)|Maria Belen Bescos Del Castillo of Moensheim (DE)]][[Category:Maria Belen Bescos Del Castillo of Moensheim (DE)]]

==Method and Apparatus for Predictive Diagnosis of a Device Battery of a Technical Device Using a Trace Graph Model - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18393857 titled 'Method and Apparatus for Predictive Diagnosis of a Device Battery of a Technical Device Using a Trace Graph Model

==Simplified Explanation==
The patent application describes a monitoring method for batteries based on analyzing operating variables over time to predict faults.

* The method involves creating a trace graph model with nodes representing different operating states and transitions between them.
* An anomaly node is included in the model to represent a fault in the battery.
* By calculating probabilities along paths in the model, the overall likelihood of a fault occurring can be determined.

==Key Features and Innovation==
* Monitoring method for batteries based on operating variables.
* Trace graph model with nodes and transitions to predict faults.
* Calculation of probabilities to assess the likelihood of faults.

==Potential Applications==
* Battery management systems.
* Predictive maintenance for batteries.
* Fault detection in battery systems.

==Problems Solved==
* Early detection of battery faults.
* Improved battery performance and longevity.
* Enhanced safety in battery applications.

==Benefits==
* Increased reliability of battery systems.
* Cost savings through predictive maintenance.
* Reduced risk of unexpected battery failures.

==Commercial Applications==
'''Battery Fault Prediction and Prevention Technology'''
This technology can be utilized in various industries such as automotive, renewable energy, and consumer electronics to enhance battery performance and safety. By implementing this monitoring method, companies can reduce maintenance costs, improve operational efficiency, and ensure uninterrupted power supply.

==Prior Art==
Research on battery fault prediction and monitoring systems in the field of battery management and predictive maintenance can provide insights into similar technologies and approaches.

==Frequently Updated Research==
Stay updated on the latest advancements in battery monitoring systems, predictive maintenance techniques, and fault prediction algorithms to enhance the effectiveness of this technology.

==Questions about Battery Fault Prediction and Prevention Technology==
===What are the key benefits of using this monitoring method for batteries?===
The key benefits include early detection of faults, improved battery performance, and enhanced safety in battery applications.

===How can this technology be applied in different industries?===
This technology can be applied in industries such as automotive, renewable energy, and consumer electronics to enhance battery performance and safety.


==Original Abstract Submitted==

A monitoring method includes ascertaining from a temporal operating variable curve of operating variables of a battery an operating feature point characterizing a battery state and/or an operating history in a time period between a most recent and a second most recent change point time in the curve, providing a trace graph model comprising nodes with respective characteristic operating feature points connected via directed transitions with respective transition probabilities. One node is an anomaly node associated with an operating feature point corresponding to a particular fault of the battery. The ascertained operating feature point is assigned to one of the nodes as a monitoring node. An overall probability of occurrence of a fault is ascertained as a sum of all path probabilities from the monitoring node to the anomaly node. The path probability is a product of the respective transition probabilities along the nodes of the respective path.



[[Category:G01R31/367]]
[[Category:G01R31/382]]
[[Category:G01R31/392]]
[[Category:CPC_G01R31/367]]