Merge pull request #35 from chirranjeevigopal-TRI/MAT-1563_power_wave…

…form_generation

power waveform generation from velocity profiles

beep/generate_protocol.py

@@ -45,7 +45,7 @@
 import time
 import datetime
 import csv
-
+import numpy as np
 import pandas as pd
 import xmltodict
 from docopt import docopt
@@ -358,6 +358,7 @@ def insert_maccor_waveform_discharge(self, proc_dict, waveform_idx, waveform_fil
 
         return proc_dict
 
+
     def insert_resistance_regcyclev2(self, proc_dict, resist_idx, reg_param):
         """
         Inserts resistance into procedure dictionary at given id.
@@ -940,6 +941,57 @@ def insert_rpt_cyclev2(self, proc_dict, start, nominal_capacity, diagnostic_para
 
         return proc_dict
 
+def convert_velocity_to_power_waveform(waveform_file, velocity_units):
+    """
+    Helper function to perform model based conversion of velocity waveform into power waveform.
+
+    For model description and parameters ref JECS, 161 (14) A2099-A2108 (2014)
+    "Model-Based SEI Layer Growth and Capacity Fade Analysis for EV and PHEV Batteries and Drive Cycles"
+
+    Args:
+        waveform_file (str): file containing tab or comma delimited values of time and velocity
+        velocity_units (str): units of velocity. Accept 'mph' or 'kmph' or 'mps'
+
+    returns
+    pd.DataFrame containing time, power and scaled power(between 0 and 1)
+    """
+    df = pd.read_csv(waveform_file, sep="\t", header=0)
+    df.columns = ['t', 'v']
+
+    if velocity_units == 'mph':
+        scale = 1600.0 / 3600.0
+    elif velocity_units == 'kmph':
+        scale = 1000.0 / 3600.0
+    elif velocity_units == 'mps':
+        scale = 1.0
+    else:
+        raise NotImplementedError
+
+    df.v = df.v * scale
+
+    # Define model constants
+    m = 1500  # kg
+    rolling_resistance_coef = 0.01  # rolling resistance coeff
+    g = 9.8  # m/s^2
+    theta = 0  # gradient in radians
+    rho = 1.225  # kg/m^3
+    drag_coef = 0.34  # Coeff of drag
+    frontal_area = 1.75  # m^2
+    v_wind = 0  # wind velocity in m/s
+
+    # Power = Force * vel
+    # Force = Rate of change of momentum + Rolling frictional force + Aerodynamic drag force
+
+    # Method treats the time-series as is and does not interpolate on a uniform grid before computing gradient.
+    power = m * np.gradient(df.v, df.t) + rolling_resistance_coef * m * g * np.cos(
+        theta * np.pi / 180) + 0.5 * rho * drag_coef * frontal_area * (df.v - v_wind) ** 2
+
+    power = -power * df.v  # positive power = charge
+
+    return pd.DataFrame({'time(s)': df.t,
+                         'power_unscaled(W)': power,
+                         'power_scaled': power / max(abs(power))})
+
 
 def generate_protocol_files_from_csv(csv_filename, output_directory, **kwargs):
     """