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Apply isort and ruff code style

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This commit is contained in:
Michael Osthege
2024-10-03 11:05:44 +02:00
committed by Andreas
parent bbaaacaca0
commit 3045d53bd6
23 changed files with 1787 additions and 866 deletions
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247
modules/class_soc_calc.py
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@@ -1,19 +1,27 @@
import mariadb
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import simpson
from datetime import datetime, timedelta
import mariadb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
class BatteryDataProcessor:
def __init__(self, config, voltage_high_threshold, voltage_low_threshold, current_low_threshold, gap, battery_capacity_ah):
def __init__(
self,
config,
voltage_high_threshold,
voltage_low_threshold,
current_low_threshold,
gap,
battery_capacity_ah,
):
self.config = config
self.voltage_high_threshold = voltage_high_threshold
self.voltage_low_threshold = voltage_low_threshold
self.current_low_threshold = current_low_threshold
self.gap = gap
self.battery_capacity_ah = battery_capacity_ah
self.battery_capacity_ah = battery_capacity_ah
self.conn = None
self.data = None
@@ -35,45 +43,55 @@ class BatteryDataProcessor:
"""
self.cursor.execute(query, (start_time,))
rows = self.cursor.fetchall()
self.data = pd.DataFrame(rows, columns=['timestamp', 'data', 'topic'])
self.data['timestamp'] = pd.to_datetime(self.data['timestamp'])
self.data['data'] = self.data['data'].astype(float)
self.data = pd.DataFrame(rows, columns=["timestamp", "data", "topic"])
self.data["timestamp"] = pd.to_datetime(self.data["timestamp"])
self.data["data"] = self.data["data"].astype(float)
def process_data(self):
self.data.drop_duplicates(subset=['timestamp', 'topic'], inplace=True)
self.data.drop_duplicates(subset=["timestamp", "topic"], inplace=True)
data_pivot = self.data.pivot(index='timestamp', columns='topic', values='data')
data_pivot = data_pivot.resample('1T').mean().interpolate()
data_pivot = self.data.pivot(index="timestamp", columns="topic", values="data")
data_pivot = data_pivot.resample("1T").mean().interpolate()
data_pivot.columns.name = None
data_pivot.reset_index(inplace=True)
self.data = data_pivot
def group_points(self, df):
df = df.sort_values('timestamp')
df = df.sort_values("timestamp")
groups = []
group = []
last_time = None
for _, row in df.iterrows():
if last_time is None or (row['timestamp'] - last_time) <= pd.Timedelta(minutes=self.gap):
if last_time is None or (row["timestamp"] - last_time) <= pd.Timedelta(
minutes=self.gap
):
group.append(row)
else:
groups.append(group)
group = [row]
last_time = row['timestamp']
last_time = row["timestamp"]
if group:
groups.append(group)
last_points = [group[-1] for group in groups]
return last_points
def find_soc_points(self):
condition_soc_100 = (self.data['battery_voltage'] >= self.voltage_high_threshold) & (self.data['battery_current'].abs() <= self.current_low_threshold)
condition_soc_0 = (self.data['battery_voltage'] <= self.voltage_low_threshold) & (self.data['battery_current'].abs() <= self.current_low_threshold)
condition_soc_100 = (
self.data["battery_voltage"] >= self.voltage_high_threshold
) & (self.data["battery_current"].abs() <= self.current_low_threshold)
condition_soc_0 = (
self.data["battery_voltage"] <= self.voltage_low_threshold
) & (self.data["battery_current"].abs() <= self.current_low_threshold)
times_soc_100_all = self.data[condition_soc_100][['timestamp', 'battery_voltage', 'battery_current']]
times_soc_0_all = self.data[condition_soc_0][['timestamp', 'battery_voltage', 'battery_current']]
times_soc_100_all = self.data[condition_soc_100][
["timestamp", "battery_voltage", "battery_current"]
]
times_soc_0_all = self.data[condition_soc_0][
["timestamp", "battery_voltage", "battery_current"]
]
last_points_100 = self.group_points(times_soc_100_all)
last_points_0 = self.group_points(times_soc_0_all)
@@ -86,79 +104,100 @@ class BatteryDataProcessor:
def calculate_resetting_soc(self, last_points_100_df, last_points_0_df):
soc_values = []
integration_results = []
reset_points = pd.concat([last_points_100_df, last_points_0_df]).sort_values('timestamp')
reset_points = pd.concat([last_points_100_df, last_points_0_df]).sort_values(
"timestamp"
)
# Initialisieren der SoC-Liste
self.data['calculated_soc'] = np.nan
self.data["calculated_soc"] = np.nan
for i in range(len(reset_points)):
start_point = reset_points.iloc[i]
if i < len(reset_points) - 1:
end_point = reset_points.iloc[i + 1]
else:
end_point = self.data.iloc[-1] # Verwenden des letzten Datensatzes als Endpunkt
end_point = self.data.iloc[
-1
] # Verwenden des letzten Datensatzes als Endpunkt
if start_point['timestamp'] in last_points_100_df['timestamp'].values:
if start_point["timestamp"] in last_points_100_df["timestamp"].values:
initial_soc = 100
elif start_point['timestamp'] in last_points_0_df['timestamp'].values:
elif start_point["timestamp"] in last_points_0_df["timestamp"].values:
initial_soc = 0
cut_data = self.data[(self.data['timestamp'] >= start_point['timestamp']) & (self.data['timestamp'] <= end_point['timestamp'])].copy()
cut_data['time_diff_hours'] = cut_data['timestamp'].diff().dt.total_seconds() / 3600
cut_data.dropna(subset=['time_diff_hours'], inplace=True)
cut_data = self.data[
(self.data["timestamp"] >= start_point["timestamp"])
& (self.data["timestamp"] <= end_point["timestamp"])
].copy()
cut_data["time_diff_hours"] = (
cut_data["timestamp"].diff().dt.total_seconds() / 3600
)
cut_data.dropna(subset=["time_diff_hours"], inplace=True)
calculated_soc = initial_soc
calculated_soc_list = [calculated_soc]
integrated_current = 0
for j in range(1, len(cut_data)):
current = cut_data.iloc[j]['battery_current']
delta_t = cut_data.iloc[j]['time_diff_hours']
delta_soc = (current * delta_t) / self.battery_capacity_ah * 100 # Convert to percentage
current = cut_data.iloc[j]["battery_current"]
delta_t = cut_data.iloc[j]["time_diff_hours"]
delta_soc = (
(current * delta_t) / self.battery_capacity_ah * 100
) # Convert to percentage
calculated_soc += delta_soc
calculated_soc = min(max(calculated_soc, 0), 100) # Clip to 0-100%
calculated_soc_list.append(calculated_soc)
# Integration des Stroms aufaddieren
integrated_current += current * delta_t
cut_data['calculated_soc'] = calculated_soc_list
soc_values.append(cut_data[['timestamp', 'calculated_soc']])
cut_data["calculated_soc"] = calculated_soc_list
soc_values.append(cut_data[["timestamp", "calculated_soc"]])
integration_results.append({
'start_time': start_point['timestamp'],
'end_time': end_point['timestamp'],
'integrated_current': integrated_current,
'start_soc': initial_soc,
'end_soc': calculated_soc_list[-1]
})
integration_results.append(
{
"start_time": start_point["timestamp"],
"end_time": end_point["timestamp"],
"integrated_current": integrated_current,
"start_soc": initial_soc,
"end_soc": calculated_soc_list[-1],
}
)
soc_df = pd.concat(soc_values).drop_duplicates(subset=['timestamp']).reset_index(drop=True)
soc_df = (
pd.concat(soc_values)
.drop_duplicates(subset=["timestamp"])
.reset_index(drop=True)
)
return soc_df, integration_results
def calculate_soh(self, integration_results):
soh_values = []
for result in integration_results:
delta_soc = abs(result['start_soc'] - result['end_soc']) # Use the actual change in SoC
delta_soc = abs(
result["start_soc"] - result["end_soc"]
) # Use the actual change in SoC
if delta_soc > 0: # Avoid division by zero
effective_capacity_ah = result['integrated_current']
effective_capacity_ah = result["integrated_current"]
soh = (effective_capacity_ah / self.battery_capacity_ah) * 100
soh_values.append({'timestamp': result['end_time'], 'soh': soh})
soh_values.append({"timestamp": result["end_time"], "soh": soh})
soh_df = pd.DataFrame(soh_values)
return soh_df
def delete_existing_soc_entries(self, soc_df):
delete_query = """
DELETE FROM pip
WHERE timestamp = %s AND topic = 'calculated_soc'
"""
timestamps = [(row['timestamp'].strftime('%Y-%m-%d %H:%M:%S'),) for _, row in soc_df.iterrows() if pd.notna(row['timestamp'])]
timestamps = [
(row["timestamp"].strftime("%Y-%m-%d %H:%M:%S"),)
for _, row in soc_df.iterrows()
if pd.notna(row["timestamp"])
]
self.cursor.executemany(delete_query, timestamps)
self.conn.commit()
@@ -167,9 +206,9 @@ class BatteryDataProcessor:
self.delete_existing_soc_entries(soc_df)
# Resample `soc_df` auf 5-Minuten-Intervalle und berechnen des Mittelwerts
soc_df.set_index('timestamp', inplace=True)
soc_df_resampled = soc_df.resample('5T').mean().dropna().reset_index()
#soc_df_resampled['timestamp'] = soc_df_resampled['timestamp'].apply(lambda x: x.strftime('%Y-%m-%d %H:%M:%S'))
soc_df.set_index("timestamp", inplace=True)
soc_df_resampled = soc_df.resample("5T").mean().dropna().reset_index()
# soc_df_resampled['timestamp'] = soc_df_resampled['timestamp'].apply(lambda x: x.strftime('%Y-%m-%d %H:%M:%S'))
print(soc_df_resampled)
# Einfügen der berechneten SoC-Werte in die Datenbank
@@ -179,8 +218,11 @@ class BatteryDataProcessor:
"""
for _, row in soc_df_resampled.iterrows():
print(row)
print(row['timestamp'])
record = (row['timestamp'].strftime('%Y-%m-%d %H:%M:%S'), row['calculated_soc'])
print(row["timestamp"])
record = (
row["timestamp"].strftime("%Y-%m-%d %H:%M:%S"),
row["calculated_soc"],
)
try:
self.cursor.execute(insert_query, record)
except mariadb.OperationalError as e:
@@ -188,38 +230,57 @@ class BatteryDataProcessor:
self.conn.commit()
def plot_data(self, last_points_100_df, last_points_0_df, soc_df):
plt.figure(figsize=(14, 10))
plt.subplot(4, 1, 1)
plt.plot(self.data['timestamp'], self.data['battery_voltage'], label='Battery Voltage', color='blue')
plt.scatter(last_points_100_df['timestamp'], last_points_100_df['battery_voltage'], color='green', marker='o', label='100% SoC Points')
#plt.scatter(last_points_0_df['timestamp'], last_points_0_df['battery_voltage'], color='red', marker='x', label='0% SoC Points')
plt.xlabel('Timestamp')
plt.ylabel('Voltage (V)')
plt.plot(
self.data["timestamp"],
self.data["battery_voltage"],
label="Battery Voltage",
color="blue",
)
plt.scatter(
last_points_100_df["timestamp"],
last_points_100_df["battery_voltage"],
color="green",
marker="o",
label="100% SoC Points",
)
# plt.scatter(last_points_0_df['timestamp'], last_points_0_df['battery_voltage'], color='red', marker='x', label='0% SoC Points')
plt.xlabel("Timestamp")
plt.ylabel("Voltage (V)")
plt.legend()
plt.title('Battery Voltage over Time')
plt.title("Battery Voltage over Time")
plt.subplot(4, 1, 2)
plt.plot(self.data['timestamp'], self.data['battery_current'], label='Battery Current', color='orange')
plt.scatter(last_points_100_df['timestamp'], last_points_100_df['battery_current'], color='green', marker='o', label='100% SoC Points')
#plt.scatter(last_points_0_df['timestamp'], last_points_0_df['battery_current'], color='red', marker='x', label='0% SoC Points')
plt.xlabel('Timestamp')
plt.ylabel('Current (A)')
plt.plot(
self.data["timestamp"],
self.data["battery_current"],
label="Battery Current",
color="orange",
)
plt.scatter(
last_points_100_df["timestamp"],
last_points_100_df["battery_current"],
color="green",
marker="o",
label="100% SoC Points",
)
# plt.scatter(last_points_0_df['timestamp'], last_points_0_df['battery_current'], color='red', marker='x', label='0% SoC Points')
plt.xlabel("Timestamp")
plt.ylabel("Current (A)")
plt.legend()
plt.title('Battery Current over Time')
plt.title("Battery Current over Time")
plt.subplot(4, 1, 3)
plt.plot(soc_df['timestamp'], soc_df['calculated_soc'], label='SoC', color='purple')
plt.xlabel('Timestamp')
plt.ylabel('SoC (%)')
plt.plot(
soc_df["timestamp"], soc_df["calculated_soc"], label="SoC", color="purple"
)
plt.xlabel("Timestamp")
plt.ylabel("SoC (%)")
plt.legend()
plt.title('State of Charge (SoC) over Time')
plt.title("State of Charge (SoC) over Time")
# plt.subplot(4, 1, 4)
# plt.plot(soh_df['timestamp'], soh_df['soh'], label='SoH', color='brown')
@@ -228,44 +289,42 @@ class BatteryDataProcessor:
# plt.legend()
# plt.title('State of Health (SoH) over Time')
plt.tight_layout()
plt.show()
if __name__ == '__main__':
if __name__ == "__main__":
# MariaDB Verbindungsdetails
# Parameter festlegen
voltage_high_threshold = 55.4 # 100% SoC
voltage_low_threshold = 46.5 # 0% SoC
current_low_threshold = 2 # Niedriger Strom für beide Zustände
gap = 30 # Zeitlücke in Minuten zum Gruppieren von Maxima/Minima
bat_capacity = 33 * 1000 / 48
bat_capacity = 33 * 1000 / 48
# Zeitpunkt X definieren
zeitpunkt_x = (datetime.now() - timedelta(weeks=100)).strftime('%Y-%m-%d %H:%M:%S')
zeitpunkt_x = (datetime.now() - timedelta(weeks=100)).strftime("%Y-%m-%d %H:%M:%S")
# BatteryDataProcessor instanziieren und verwenden
processor = BatteryDataProcessor(config, voltage_high_threshold, voltage_low_threshold, current_low_threshold, gap,bat_capacity)
processor = BatteryDataProcessor(
config,
voltage_high_threshold,
voltage_low_threshold,
current_low_threshold,
gap,
bat_capacity,
)
processor.connect_db()
processor.fetch_data(zeitpunkt_x)
processor.process_data()
last_points_100_df, last_points_0_df = processor.find_soc_points()
soc_df, integration_results = processor.calculate_resetting_soc(last_points_100_df, last_points_0_df)
#soh_df = processor.calculate_soh(integration_results)
soc_df, integration_results = processor.calculate_resetting_soc(
last_points_100_df, last_points_0_df
)
# soh_df = processor.calculate_soh(integration_results)
processor.update_database_with_soc(soc_df)
processor.plot_data(last_points_100_df, last_points_0_df, soc_df)
processor.disconnect_db()