View source for 18327395. METHOD AND ELECTRONIC DEVICE FOR FORECASTING REMAINING USEFUL LIFE (RUL) OF BATTERY simplified abstract (Samsung Electronics Co., Ltd.)

=METHOD AND ELECTRONIC DEVICE FOR FORECASTING REMAINING USEFUL LIFE (RUL) OF BATTERY=


==Organization Name==

[[:Category:Samsung Electronics Co., Ltd.|Samsung Electronics Co., Ltd.]]


[[Category:Samsung Electronics Co., Ltd.]]

==Inventor(s)==

[[:Category:Subhasmita Sahoo of Bangalore (IN)|Subhasmita Sahoo of Bangalore (IN)]][[Category:Subhasmita Sahoo of Bangalore (IN)]]

[[:Category:Krishnan S. Hariharan of Bangalore (IN)|Krishnan S. Hariharan of Bangalore (IN)]][[Category:Krishnan S. Hariharan of Bangalore (IN)]]

[[:Category:Samarth Agarwal of Bangalore (IN)|Samarth Agarwal of Bangalore (IN)]][[Category:Samarth Agarwal of Bangalore (IN)]]

[[:Category:Subramanian Swernath Brahmadathan of Bangalore (IN)|Subramanian Swernath Brahmadathan of Bangalore (IN)]][[Category:Subramanian Swernath Brahmadathan of Bangalore (IN)]]

[[:Category:Sangheon Lee of Suwon-si (KR)|Sangheon Lee of Suwon-si (KR)]][[Category:Sangheon Lee of Suwon-si (KR)]]

==METHOD AND ELECTRONIC DEVICE FOR FORECASTING REMAINING USEFUL LIFE (RUL) OF BATTERY - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18327395 titled 'METHOD AND ELECTRONIC DEVICE FOR FORECASTING REMAINING USEFUL LIFE (RUL) OF BATTERY

==Simplified Explanation==

The abstract of the patent application describes a method for forecasting the remaining useful life (RUL) of a battery in an electronic device. The method involves estimating the battery capacity using either a battery capacity estimation model or a data-driven model. The RUL of the battery is then forecasted based on the estimated capacity. The method also includes comparing the estimated capacity with the actual capacity values obtained during charging and discharging cycles. If the estimated capacity is lower than the actual capacity values, the forecasting RUL is corrected by feeding back the actual capacity values to the battery capacity estimation model or the data-driven model.

* The method forecasts the remaining useful life (RUL) of a battery in an electronic device.
* It estimates the battery capacity using a battery capacity estimation model or a data-driven model.
* The RUL of the battery is forecasted based on the estimated capacity.
* The method compares the estimated capacity with the actual capacity values obtained during charging and discharging cycles.
* If the estimated capacity is lower than the actual capacity values, the forecasting RUL is corrected by feeding back the actual capacity values to the battery capacity estimation model or the data-driven model.

Potential applications of this technology:
* Battery management systems in electronic devices.
* Predictive maintenance of batteries in electric vehicles.
* Optimization of battery usage in renewable energy systems.

Problems solved by this technology:
* Accurate estimation of the remaining useful life (RUL) of a battery.
* Early detection of battery degradation or failure.
* Improved battery management and maintenance strategies.

Benefits of this technology:
* Increased reliability and performance of electronic devices.
* Cost savings through optimized battery usage and maintenance.
* Extended battery lifespan and reduced environmental impact.


==Original Abstract Submitted==

A method for forecasting remaining useful life (RUL) of a battery by an electronic device is provided. The method includes forecasting the RUL of the battery based on at least one capacity value of the battery estimated by at least one of a battery capacity estimation model and a data driven model, determining whether the at least one capacity value for the charging cycle and the discharging cycle is lower than the at least one capacity value estimated by at least one of the battery capacity estimation model and the data driven model and correcting the forecasting RUL of the battery by feeding back the at least one capacity value to at least one of the battery capacity estimation model and the data driven model.



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[[Category:G01R31/392]]
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