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 class BatteryDataProcessor: 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.conn = None self.data = None def connect_db(self): self.conn = mariadb.connect(**self.config) self.cursor = self.conn.cursor() def disconnect_db(self): if self.conn: self.cursor.close() self.conn.close() def fetch_data(self, start_time): query = """ SELECT timestamp, data, topic FROM pip WHERE timestamp >= %s AND (topic = 'battery_current' OR topic = 'battery_voltage') ORDER BY timestamp """ 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) def process_data(self): 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.columns.name = None data_pivot.reset_index(inplace=True) self.data = data_pivot def group_points(self, df): 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): group.append(row) else: groups.append(group) group = [row] 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) 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) last_points_100_df = pd.DataFrame(last_points_100) last_points_0_df = pd.DataFrame(last_points_0) return last_points_100_df, last_points_0_df 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') # Initialisieren der SoC-Liste 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 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: 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) 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 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']]) 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) 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 if delta_soc > 0: # Avoid division by zero 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_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'])] self.cursor.executemany(delete_query, timestamps) self.conn.commit() def update_database_with_soc(self, soc_df): # Löschen der vorhandenen Einträge mit demselben Topic und Datum 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')) print(soc_df_resampled) # Einfügen der berechneten SoC-Werte in die Datenbank insert_query = """ INSERT INTO pip (timestamp, data, topic) VALUES (%s, %s, 'calculated_soc') """ 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']) try: self.cursor.execute(insert_query, record) except mariadb.OperationalError as e: print(f"Error inserting record {record}: {e}") 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.legend() 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.legend() 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.legend() 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') # plt.xlabel('Timestamp') # plt.ylabel('SoH (%)') # plt.legend() # plt.title('State of Health (SoH) over Time') plt.tight_layout() plt.show() if __name__ == '__main__': # MariaDB Verbindungsdetails config = { 'user': 'soc', 'password': 'Rayoflight123!', 'host': '192.168.1.135', 'database': 'sensor' } # 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 # Zeitpunkt X definieren 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.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) processor.update_database_with_soc(soc_df) processor.plot_data(last_points_100_df, last_points_0_df, soc_df) processor.disconnect_db()