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Doc: included README reminder to update data path

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Richard Wong 2024-01-15 11:26:27 +09:00
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commit 89557bbd1f
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7
README.md
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@ -5,6 +5,13 @@ an implementation of a custom profile simulator. Refer to
`simulation_with_custom_profile.ipynb` for the usage of the custom profile. The `simulation_with_custom_profile.ipynb` for the usage of the custom profile. The
custom profile is implemented in `battery_profile.py`. custom profile is implemented in `battery_profile.py`.
### Data
Note that the battery data is not included due to its confidential nature.
Download the file to a location on your computer and update line 20 of
`python/battery_profile.py`
### BLAST-Lite ### BLAST-Lite
Battery Lifetime Analysis and Simulation Toolsuite (BLAST) provides a library of battery lifetime and degradation models for various commercial lithium-ion batteries from recent years. Degradation models are indentified from publically available lab-based aging data using NREL's battery life model identification toolkit. The battery life models predicted the expected lifetime of batteries used in mobile or stationary applications as functions of their temperature and use (state-of-charge, depth-of-discharge, and charge/discharge rates). Model implementation is in both Python and MATLAB programming languages. The MATLAB code also provides example applications (stationary storage and EV), climate data, and simple thermal management options. For more information on battery health diagnostics, prediction, and optimization, see [NREL's Battery Lifespan](https://www.nrel.gov/transportation/battery-lifespan.html) webpage. Battery Lifetime Analysis and Simulation Toolsuite (BLAST) provides a library of battery lifetime and degradation models for various commercial lithium-ion batteries from recent years. Degradation models are indentified from publically available lab-based aging data using NREL's battery life model identification toolkit. The battery life models predicted the expected lifetime of batteries used in mobile or stationary applications as functions of their temperature and use (state-of-charge, depth-of-discharge, and charge/discharge rates). Model implementation is in both Python and MATLAB programming languages. The MATLAB code also provides example applications (stationary storage and EV), climate data, and simple thermal management options. For more information on battery health diagnostics, prediction, and optimization, see [NREL's Battery Lifespan](https://www.nrel.gov/transportation/battery-lifespan.html) webpage.

2
python/battery_profile.py
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@ -17,7 +17,7 @@ class BatProfile:
def __init__(self): def __init__(self):
# process dataframe # process dataframe
# file_path = "/home/richard/Projects/06_research/battery_degradation_study/battery-anomaly-detection/ISS_data/EP_Battery.Thing_HMD8310.csv" # file_path = "/home/richard/Projects/06_research/battery_degradation_study/battery-anomaly-detection/ISS_data/EP_Battery.Thing_HMD8310.csv"
file_path = "/home/richard/Projects/06_research/battery_degradation_study/BLAST-Lite/data/EP_Battery.Thing_HMD8310.csv" file_path = "path/EP_Battery.Thing_HMD8310.csv"
fields = ['PACK1_CRIDATA_SOC', 'time'] fields = ['PACK1_CRIDATA_SOC', 'time']
df = pd.read_csv(file_path, skipinitialspace=True, usecols=fields) df = pd.read_csv(file_path, skipinitialspace=True, usecols=fields)
df['time'] = pd.to_datetime(df['time']) df['time'] = pd.to_datetime(df['time'])