Package contents:¶

1. init.py¶

This module is the entry point of the battlib library. It is the top-level package that gathers the tools for battery modeling and state estimation. The __all__ attribute lists modules considered public (Battery, BatteryEKF, intexterp).

2. battery.py¶

This file contains a ‘battery’ class that is used for modeling a battery with specific physical properties and variance parameters, including the essential characteristics of a battery (open circuit voltage, state of charge). The class provides methods for interpolating state of charge (SOC) based on the open circuit voltage (OCV) (using the interp_soc method) and interpolating the OCV based on the SOC (using the intexterp_ocv method).

i) To create an instance of the Battery class:¶

new_battery = Battery(
ocv = np.array[2.0, 2.5, 3.5],
soc = np.array[20,40,80],
q_cap = 2250,
r_int = 0.01,
r_ct = 0.1,
c_ct = 80,
r_d = 0.06,
c_d = 50,
var_z = 0.02,
var_i_ct = 0.002,
var_i_d = 0.0002,
var_sens = 0.0002,
var_in = 0.00002,
coulomb_eta = 0.80 )

ii) To interpolate SOC from OCV:¶

ocv = 4.7
interpolated_soc = new_battery.interp_soc(ocv)

iii) To interpolate OCV from SOC:¶

soc = 80
interpolated_ocv = new_battery.intexterp_ocv(soc)

3. battery_ekf.py¶

This file contains a BatteryEKF class that estimates the state of charge (SOC) of a battery by implementing the Extended Kalman Filter (EKF) algorithm.

An instance of the Battery class must be made before using the methods in the class BatteryEKF. Refer to section 2.i).

i) To create an instance of the BatteryEKF class:¶

initial_voltage = 5.0
new_battery_ekf = BatteryEKF(new_battery, intiial_voltage)

ii) To perform a single step prediction and update using the BatteryEKF¶

time_step = 1.0
current_in = 3.0
measured_voltage = 4.3
new_battery_ekf.step(time_step, current_in, measured_voltage)

iii) To access the estimated state of charge (SOC)¶

estimated_soc = new_battery_ekf.soc

4. utilities.py¶

This file contains a method for interpolating/extrapolating sample data.

i) Example usage¶

x_vals = [1, 2, 3]
y_vals = [20, 30, 40]
interpolated _val = intexterp(2.5, x_vals, y_vals)

Dependencies: