Summary of Onboard Health Estimation Using Distribution Of Relaxation Times For Lithium-ion Batteries, by Muhammad Aadil Khan et al.
Onboard Health Estimation using Distribution of Relaxation Times for Lithium-ion Batteries
by Muhammad Aadil Khan, Sai Thatipamula, Simona Onori
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach for estimating the state-of-health (SOH) of real-life batteries under various operating conditions. The authors utilize electrochemical impedance spectroscopy (EIS) data from calendar-aged and cycling-aged cells to develop a long short-term memory (LSTM)-based neural network model. By deconvoluting EIS curves using the distribution of relaxation times (DRT) technique, the model maps the curves onto a function that represents different resistances inside the cell. The authors validate the model’s performance on ten test sets, achieving an average root mean squared percentage error (RMSPE) of 1.69%. This work has implications for improving the accuracy of onboard health estimation models used in battery management systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to make batteries last longer by figuring out how healthy they are. Right now, there’s a problem with estimating how good or bad a battery is based on how it’s been used and its environment. The researchers use special tests called EIS (electrochemical impedance spectroscopy) to look at the battery’s insides and then use this information to train a super smart computer model to predict how healthy the battery is. They test their model with lots of different scenarios and get pretty good results! This means that in the future, we might be able to make batteries last even longer. |
Keywords
» Artificial intelligence » Lstm » Neural network
