Example [simulation]

In [1]:

# coding: utf-8

import numpy as np
import pandas as pd
from pandas import DataFrame as df
from batterylearn.models import OCV, EcmCell
from batterylearn.utilities import ivp
from batterylearn.simulations import Data, Current, Learner,Simulator
import os
import matplotlib.pyplot as plt

# coding: utf-8 import numpy as np import pandas as pd from pandas import DataFrame as df from batterylearn.models import OCV, EcmCell from batterylearn.utilities import ivp from batterylearn.simulations import Data, Current, Learner,Simulator import os import matplotlib.pyplot as plt

this example shows how to simulate battery terminal voltage based on

In [2]:

ocv = [3.3, 3.5, 3.55, 3.6, 3.65, 3.68, 3.70, 3.8, 3.95, 4.0, 4.1]
soc = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

c1 = OCV(name="curve1", ocv=ocv, soc=soc)

c1.display()

ocv = [3.3, 3.5, 3.55, 3.6, 3.65, 3.68, 3.70, 3.8, 3.95, 4.0, 4.1] soc = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100] c1 = OCV(name="curve1", ocv=ocv, soc=soc) c1.display()

In [ ]:

param = {
 "R0": 0.034,
 "R1": 0.022,
 "tau1": 120,
 "R2": 0.019,
 "tau2": 3600,
 "CAP": 15,
 "ce": 0.96,
 "v_limits": [2.5, 4.5],
 "SOC_RANGE": [0.0, 100.0],
}

m1 = EcmCell(name="cell_model1", parameters=param, curve=c1)
m1.display()

param = { "R0": 0.034, "R1": 0.022, "tau1": 120, "R2": 0.019, "tau2": 3600, "CAP": 15, "ce": 0.96, "v_limits": [2.5, 4.5], "SOC_RANGE": [0.0, 100.0], } m1 = EcmCell(name="cell_model1", parameters=param, curve=c1) m1.display()

In [6]:

dt = 0.1
CURR_EXCITATION = 7.5
HOUR = 3600.0
h_steps = [1.75, 0.25, 0.25, 0.25, 1, 1, 1]
current_steps = [
 -CURR_EXCITATION,
 -CURR_EXCITATION / 2,
 -CURR_EXCITATION / 4,
 -CURR_EXCITATION / 8,
 0.0,
 CURR_EXCITATION,
 0.0,
]

initial_soc = 0.0
t_steps = [value * HOUR for value in h_steps]
total_time = sum(t_steps)
samples = int(total_time / dt)

time_np = np.linspace(0.0, total_time, samples)
step_cur = Current(name="current1")

dt = 0.1 CURR_EXCITATION = 7.5 HOUR = 3600.0 h_steps = [1.75, 0.25, 0.25, 0.25, 1, 1, 1] current_steps = [ -CURR_EXCITATION, -CURR_EXCITATION / 2, -CURR_EXCITATION / 4, -CURR_EXCITATION / 8, 0.0, CURR_EXCITATION, 0.0, ] initial_soc = 0.0 t_steps = [value * HOUR for value in h_steps] total_time = sum(t_steps) samples = int(total_time / dt) time_np = np.linspace(0.0, total_time, samples) step_cur = Current(name="current1")

In [7]:

for t_step, current_step in zip(t_steps, current_steps):
 step_cur.add_step(current_step, int(t_step / dt))

data = {"time": time_np, "current": step_cur.current}
df = pd.DataFrame(data)
d1 = Data(name="current_excite", df=df)

for t_step, current_step in zip(t_steps, current_steps): step_cur.add_step(current_step, int(t_step / dt)) data = {"time": time_np, "current": step_cur.current} df = pd.DataFrame(data) d1 = Data(name="current_excite", df=df)

In [8]:

s1 = Simulator(name="simulator1")
s1.attach(m1).attach(d1)
sol = s1.run(
    pair=("cell_model1", "current_excite"),
    x0=np.array([0, 0, initial_soc]),
    config={"solver_type": "adaptive", "solution_name": "sol1"},
)

sol.disp(["current", "soc", "vt"])

s1 = Simulator(name="simulator1") s1.attach(m1).attach(d1) sol = s1.run( pair=("cell_model1", "current_excite"), x0=np.array([0, 0, initial_soc]), config={"solver_type": "adaptive", "solution_name": "sol1"}, ) sol.disp(["current", "soc", "vt"])