Another initial fit of HI and LO coils

optimization/HIData.csv

@@ -0,0 +1,17 @@
+Voltage,Current,Speed,Torque,Efficiency
+131,0.41,739,0,0
+131,0.73,731,0.6,45.7
+131,2,724,2.7,78.3
+131,3,719,4.4,84.2
+131,4,713,6,87.2
+131,5,707,7.9,88.9
+131,6,703,9.6,89.9
+131,8,692,13.1,90.7
+131,10,683,16.7,91.3
+131,12,673,20.4,91.5
+131,14,664,24.2,91.8
+131,16,657,57.9,91.5
+131,18,649,31.7,91.4
+131,20,641,35.5,90.9
+131,25,625,44.9,89.7
+131,30,611,54,88

optimization/LOData.csv

@@ -0,0 +1,17 @@
+Voltage,Current,Speed,Torque,Efficiency
+131,0.34,638,0,0
+131,0.63,637,0.6,50.1
+131,2,630,3.2,81.8
+131,3,624,5.3,87.9
+131,4,619,7.3,90.4
+131,5,614,9.4,91.9
+131,6,610,11.4,92.3
+131,8,599,15.4,92.3
+131,10,590,19.6,92.4
+131,12,582,23.8,92.4
+131,14,573,28.3,92.5
+131,16,566,32.6,92.5
+131,18,559,36.9,91.5
+131,20,552,41.2,91
+131,25,537,52.1,89.5
+131,30,524,62.6,87.4

optimization/motor_efficiency.py

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+import argparse
+import math
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn import linear_model
+
+class MotorEfficiency:
+
+    def __init__(self, coil):
+        self.coil = coil
+        dataset = pd.read_csv(coil + 'Data.csv')
+
+        # let us determine relationship between speed and torque with voltage*current
+        # we will use this predicted value in place of actual current and voltage
+
+        x = dataset[['Speed', 'Torque']]
+        y = dataset['Voltage'] * dataset['Current']
+        regr = linear_model.LinearRegression()
+        regr.fit(x, y)
+
+        self.speed_k = regr.coef_[0]
+        self.torque_k = regr.coef_[1]
+        self.c = regr.intercept_
+
+    def calc_efficiency(self, speed, torque):
+        # formula: efficiency (%) = [pi/30 * speed * torque] / [voltage * current] * 100
+        return (math.pi/30 * speed * torque) / (self.speed_k * speed + self.torque_k * torque + self.c) * 100
+
+    def graph_default(self):
+        dataset = pd.read_csv(self.coil + 'Data.csv')
+        dataset['CalcEfficiency'] = dataset.apply(lambda x: self.calc_efficiency(x.Speed, x.Torque), axis=1)
+
+        plt.scatter(dataset.index, dataset['Efficiency'], color='blue', label='True')
+        plt.scatter(dataset.index, dataset['CalcEfficiency'], color='red', label='Calculated')
+        plt.legend()
+        plt.title("Efficiency Calculation Comparions (" + self.coil + " Coil)")
+        plt.xlabel("Datapoint #")
+        plt.ylabel("Efficiency (%)")
+        plt.show()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="See coil fit graphs")
+    parser.add_argument('-c', '--coil', help='Select HI or LO coil', default="LO")
+    args = parser.parse_args()
+
+    if args.coil.upper() != "LO" and args.coil.upper() != "HI":
+        print("Choose HI or LO please")
+    else:
+        motor_efficiency_curve = MotorEfficiency(args.coil.upper())
+        motor_efficiency_curve.graph_default()
+