diff --git a/test_files/batteryAgingTest.py b/test_files/batteryAgingTest.py
deleted file mode 100644
index 6c1f5836..00000000
--- a/test_files/batteryAgingTest.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import numpy as np
-import math
-import matplotlib.pyplot as plt
-import matplotlib
-
-"""This script was used to test the battery aging model, confirming I can reproduce the cited paper"""
-
-plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
-
-def get_calendar_aging(temp):
-    # TODO: This currently runs the aging for an entire day after each iteration run. Will need to formulate
-    #  learning architecture
-    """Estimates the calendar aging of the battery using Schmalsteig Model (Same as above)"""
-    avg_voltage = 3.699  # mean voltage from paper to replicate results
-    # I THINK THIS MUST BE ESTIMATED IN DAYS? AND I THINK IT HAS TO BE CUMULATIVE? NOT PER TIMESTEP?
-    alpha_cap = (7.543 * avg_voltage - 23.75) * 10 ** 6 * math.exp(-6976 / temp)  # aging factors
-    alpha_res = (5.270 * avg_voltage - 16.32) * 10 ** 5 * math.exp(-5986 / temp)  # temp in K
-    return alpha_cap, alpha_res
-
-def main():
-    """ Run the simple test here """
-    temp_range = (35+273.15, 60+273.15)
-    num_points = 8
-    temperatures = np.linspace(temp_range[0], temp_range[1], num_points)
-    log_ALPHA_CAP_LIST = []
-    log_ALPHA_RES_LIST = []
-    ALPHA_CAP_LIST = []
-    ALPHA_RES_LIST = []
-    for T in temperatures:
-        ALPHA_cap, ALPHA_res = get_calendar_aging(T)
-        log_ALPHA_CAP_LIST.append(np.log(ALPHA_cap))
-        log_ALPHA_RES_LIST.append(np.log(ALPHA_res))
-        ALPHA_CAP_LIST.append(ALPHA_cap)
-        ALPHA_RES_LIST.append(ALPHA_res)
-
-    plt.plot(1/temperatures, log_ALPHA_CAP_LIST)
-    plt.plot(1/temperatures, log_ALPHA_RES_LIST)
-    plt.ylabel(r" \log aging factor")
-    plt.show()
-    print(ALPHA_CAP_LIST)
-    print(ALPHA_RES_LIST)
-
-
-if __name__ == "__main__":
-    main()
-