View source for 17918774. Battery Diagnosis Apparatus, Battery Diagnosis Method, Battery Pack, and Vehicle simplified abstract (LG Energy Solution, Ltd.)

=Battery Diagnosis Apparatus, Battery Diagnosis Method, Battery Pack, and Vehicle=


==Organization Name==

[[:Category:LG Energy Solution, Ltd.|LG Energy Solution, Ltd.]]


[[Category:LG Energy Solution, Ltd.]]

==Inventor(s)==

[[:Category:Yong-Chul Sung of Daejeon (KR)|Yong-Chul Sung of Daejeon (KR)]][[Category:Yong-Chul Sung of Daejeon (KR)]]

[[:Category:Yang-Soo An of Daejeon (KR)|Yang-Soo An of Daejeon (KR)]][[Category:Yang-Soo An of Daejeon (KR)]]

==Battery Diagnosis Apparatus, Battery Diagnosis Method, Battery Pack, and Vehicle - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17918774 titled 'Battery Diagnosis Apparatus, Battery Diagnosis Method, Battery Pack, and Vehicle

==Simplified Explanation==

The patent application describes a battery diagnosis apparatus that can diagnose the health of a group of battery cells connected in series. Here are the key points:

* The apparatus includes a voltage sensing circuit that periodically measures the voltage of each battery cell and generates a voltage signal.
* A control circuit processes the voltage signal to generate time series data, showing how the cell voltage of each battery cell changes over time.
* The control circuit calculates two average cell voltages for each battery cell based on the time series data. The first average is a short-term moving average, and the second average is a long-term moving average.
* The control circuit then compares the first and second average cell voltages to detect any abnormal voltage behavior in each battery cell.

Potential applications of this technology:

* Electric vehicles: The battery diagnosis apparatus can be used to monitor the health of the battery cells in electric vehicles, ensuring optimal performance and preventing potential failures.
* Renewable energy storage: The apparatus can be applied to battery systems used for storing energy from renewable sources like solar or wind, helping to maintain the efficiency and reliability of the storage system.
* Portable electronics: Battery diagnosis can be useful in devices like smartphones or laptops, allowing users to monitor the health of their batteries and take appropriate actions to prolong their lifespan.

Problems solved by this technology:

* Early detection of battery cell abnormalities: By analyzing the time series data and comparing short-term and long-term averages, the apparatus can identify any abnormal voltage behavior in battery cells, allowing for timely maintenance or replacement.
* Preventing battery failures: By continuously monitoring the health of battery cells, the apparatus can help prevent unexpected battery failures, reducing the risk of accidents or disruptions in various applications.

Benefits of this technology:

* Improved battery performance: By detecting and addressing abnormal voltage behavior, the apparatus can help maintain the optimal performance of battery cells, ensuring longer battery life and better overall efficiency.
* Enhanced safety: Early detection of abnormalities can prevent battery failures, reducing the risk of safety hazards such as fires or explosions.
* Cost savings: Timely maintenance or replacement of battery cells can help avoid costly repairs or replacements of entire battery systems, saving both time and money.


==Original Abstract Submitted==

A battery diagnosis apparatus for diagnosis of a cell group including a plurality of battery cells connected in series, includes a voltage sensing circuit configured to periodically generate a voltage signal indicating a cell voltage of each battery cell, and a control circuit configured to generate time series data indicating a change in cell voltage of each battery cell over time based on the voltage signal. The control circuit is configured to (i) determine a first average cell voltage and a second average cell voltage of each battery cell based on the time series data, wherein the first average cell voltage is a short term moving average, and the second average cell voltage is a long term moving average, and (ii) detect an abnormal voltage of each battery cell based on a difference between the first average cell voltage and the second average cell voltage.



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