NASA锂电池老化数据集及代码【5套】

# Remaining Useful Life Prediction Using NASA Battery Data Set ## 1️⃣ Overview ### ◽ Objective - [x] NASA에서 제공하는 4개 셀 리튬이온 배터리 데이터를 명세하고 분석하여 데이터를 이해한다. - [x] 데이터 전처리 및 학습에 알맞은 형태로 가공한다. - [x] AI(ML/DL)기반의 다양한 방법론(알고리즘,모델)을 적용한 배터리 잔존수명예측 모델링을 진행한다. --- ### ◽ Development Environment page_type | languages | products :------:|:------:|:------: 　　　　　`Dev`　　　　　|　　`pyspark`　`python`　　|`azure`　`azure-databricks` --- ### ◽ NASA PCoE Battery Data <p align="left"> <img src="https://user-images.githubusercontent.com/88306533/128735382-30fec59a-fcb7-4763-9f89-46c658035fa5.png" width="75%" height="75%"></img></p> > **Download URL** - https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/ --- <br> ## 2️⃣ PreRequirement ### ◽ Understanding Data - SOH : State of Health - RUL : Remaining Useful Life - EOL : End of Life ### ◽ Data Structure - ❶ cycle: top level structure array containing the charge, discharge and impedance operations |　cycle　|`⇦ Complex Data Structure( Nested Data )` | |:------:|:------:| ⏬ - ❷ type: operation type, can be charge, discharge or impedance - ❸ Data: Nested Data Structure(Many Variables) |type | ambient_temperature | time | data| |:------:|:------:|:------:|:------:| |charge|`24℃`|`yyyy-MM-dd HH:mm:ss.SSSSSS`|`data`| |discharge |''|''|''| |impedance |''|''|''| --- <br> ## 3️⃣ Methodology ### ◽ Part1 : Convert .mat to Dataframe > - [Convert .mat to Dataframe.py](https://github.com/YJPark0421/NASA-PCoE-Battery-Analytics/blob/master/Code/Preprocessing-Setup.py) **|** [Convert .mat to Dataframe.ipynb](https://github.com/YJPark0421/NASA-PCoE-Battery-Analytics/blob/master/Code/Preprocessing-Setup.ipynb) ### ◽ Part2 : EDA(Exploratory Data Analysis) > - [EDA-Battery-Data.py](https://github.com/YJPark0421/NASA-PCoE-Battery-Analytics/blob/master/Code/EDA-Battery-Data-v0.1.py) **|** [EDA-Battery-Data.ipynb](https://github.com/YJPark0421/NASA-PCoE-Battery-Analytics/blob/master/Code/EDA-Battery-Data-v0.1.ipynb) ### ◽ Part3 : Modeling > - [LSTM_Based_SOH_Prediction.py](https://github.com/YJPark0421/NASA-PCoE-Battery-Analytics/blob/master/Code/LSTM_Based_SOH_Prediction.py) **|** [LSTM_Based_SOH_Prediction.ipynb](https://github.com/YJPark0421/NASA-PCoE-Battery-Analytics/blob/master/Code/LSTM_Based_SOH_Prediction.ipynb) > - [SVM_Based_SOH_Prediction.py](https://github.com/YJPark0421/NASA-PCoE-Battery-Analytics/blob/master/Code/SVM_Based_SOH_Prediction_v0.1.py) **|** [SVM_Based_SOH_Prediction.ipynb](https://github.com/YJPark0421/NASA-PCoE-Battery-Analytics/blob/master/Code/SVM_Based_SOH_Prediction_v0.1.ipynb) ### ◽ LSTM Based Model Prediction Result Example > <p align="left"> <img src="https://user-images.githubusercontent.com/88306533/128865333-b2d2fc15-a00e-47db-a391-162ba7d65dfe.png" width="55%" height="55%"></img></p> ### ◽ Understanding LSTM(Long Short-Term Memory) > <p align="left"> <img width="55%" height="55%" alt="rnn-lstm-cell" src="https://user-images.githubusercontent.com/88306533/129383011-a8bbea44-8cba-440c-a0de-a80e840689e6.png"></img></p> > **Read a Blog** - http://colah.github.io/posts/2015-08-Understanding-LSTMs/ ### ◽ Understanding SVM(Support Vector Machines) > **Watch a Youtube** - https://www.youtube.com/watch?v=eHsErlPJWUU --- <br> ## 4️⃣ Reference > **URL①** - https://ieeexplore.ieee.org/document/8967059<br>