Brief layout sketch of race class

optimization/ASC_opt_data.csv

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+Latitude,Longitude,Temperature,Wind Speed,Wind Direction,Energy Corrected
+38.941960,-92.684750
+39.068140,-92.946070
+39.186270,-94.193590
+39.086830,-94.419420
+39.008310,-94.466870
+38.925354,-94.568639
+38.788531,-94.991618
+38.780561,-95.557203
+38.665916,-96.493941
+38.372787,-97.303086
+38.34501,-98.201727
+38.365832,-98.401643
+38.34863,-98.766032
+38.265291,-98.980929
+38.188527,-99.087184
+37.752798,-100.017079
+37.576617,-101.355375
+37.004007,-101.888395
+36.736418,-102.512726
+36.712246,-103.060209
+36.361145,-104.595267              
+35.907481,-105.013186
+35.820597,-104.997503
+35.593625,-105.224615  
+35.548633,-105.685455
+35.682273,-105.929828

optimization/ASC_optimization.py

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+"""
+do i want to update everything on "get" -> ie. when we run optimization, or before?
+if we're running multiple possibilities does this change anything
+
+let's initially approach this as a prediction _only_ thing
+"""
+import csv
+import pandas as pd
+
+import sys
+import os.path
+sys.path.append(os.path.dirname(__file__), '..')
+
+from dynamics.motor_efficiency.motor_efficiency import MotorEfficiency
+from dynamics.car_model import Car
+
+RACE_FILE = os.path.join(os.path.dirname(__file__), '../routemodel/data_retrieval/new_get_weather.csv')
+
+class ASC_Race:
+
+    # Car information
+    PASSENGERS = 4
+    BATTERY_CAPACITY_kWH = 20
+
+    distance_travelled_segments = []
+    # passengers = []
+    time_elapsed_mins = 0
+
+    def __init__(self, race_data_csv):
+        # TODO: determine format of data passing and interacts
+        # TODO: how to change route? update during race?
+        self.route_df = pd.read_csv(race_data_csv, delimiter=',')
+
+        # find distance -> hopefully a parameter; append to list
+        # if passengers not flagged
+        # self.passengers.append(4)
+
+    @property
+    def D(self):
+        '''
+        Person-Mile Distance
+        '''
+        d = 0
+        # for seg, passenger in zip(self.distance_travelled_segments, self.passengers):
+            # d += seg * passenger
+
+        for seg in self.distance_travelled_segments:
+            d += seg
+
+        return d
+
+    @property
+    def E(self):
+        '''
+        External Energy Usage
+        (n + 1) * Q * M
+        '''
+        nights = 4
+        charging_per_night = 100
+        return nights * charging_per_night
+
+    @property
+    def C(self):
+        '''
+        Completion Factor
+        '''
+        return 1
+
+    @property
+    def T(self):
+        '''
+        Target Speed Derate
+        '''
+        d = 0
+        for seg in self.distance_travelled_segments:
+            d += seg
+
+        return d / self.time_elapsed_mins / 60
+
+    @property
+    def P(self):
+        '''
+        Practicality Score
+        '''
+        return 1
+
+    def update_variables(self, velocity_profile):
+        pass
+
+    def get_score(self, velocity_profile):
+        self.update_variables(velocity_profile)
+        return self.D / self.E * self.C * self.T * self.P
+
+def energy_needed():
+    # between two points, how much energy do we draw?
+    # ok so 
+    # minus: 
+    #   energy needed to travel this distance * -> energy_used, but only between two points? 
+    car_model = Car() # can add speed limit?
+    energy_needed = car_model.energy_used()
+
+    #   efficiency of motors * (draw more energy than necessary) -> motor eff curves
+    low_curve = MotorEfficiency("LO")
+    high_curve = MotorEfficiency("HI")
+    # TODO what the fuck is this
+    # so we have speed, it will guess our torque based on test data
+    power_needed = low_curve.power_draw_needed(energy_needed, speed, torque)
+
+    #   efficiency of soc (how much energy is actually taken from batteries) -> soc 
+    power_loss = power_needed * 0.5
+
+    # auxloss to consider
+    power_loss += 0.5
+
+    # plus: energy gained from solar -> from solar
+
+    power_loss -= 0.5
+
+    return power_loss
+
+    # takeaway from this exercise is that carmodel, lowhigh, and our soc and solar objects can be fairly static? we will need to change the speed limit on the car as we go, which we can do hopefully
+    # how do we want this to happen wiht df -> either make calls per row, or just change the methods so they accept dfs
+
+if __name__ == '__main__':
+    race = ASC_Race(RACE_FILE)
+
+'''
+
+lat, lon, city name, city Id, time, temperature, efficiency correction,
+wind speed, wind direction, precipitation, energy solar, energy corrected
++ mark stops (checkpoints) somehow
+
+routemodel
+speed limits, lat, lon, elevations
+wind
+weather??
+
+solar
+
+
+soc
+??
+auxloss
+
+motor torque
+
+dynamics
+
+'''
+
+
+'''
+also, comparison of fmin
++ https://www.mathworks.com/help/optim/ug/fmincon.html
++ https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
+
+
+Parameters
+fun(x, *args) -> float ==> get_score()  
+initial guess ==> even within speed limits?
+bounds (for each in x) ==> speed limits
+
+'''