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evpy-1.1.0
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مشاهده بیشترتوضیحات
A phython package to predict the efficiency/size of electronic powertrain components
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | evpy-1.1.0 |
| نام | evpy |
| نسخه کتابخانه | 1.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Dalton Chancellor |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/evpy/ |
| مجوز | MIT |
evpy is a pyhton library used to predict efficiency in electric power trains given high level component specifications. It can be used to predict motor performance, a motors torque/speed/efficeiency contour, motor size given aspect ratio and torque, losses in an ESC given motor performance, ESC size, a battery’s voltage given time under a load, and battery mass given duration and specific energy.
evpy Functions
evpy.batt_pred(I_load, t_hr, Q_Ah, R_int, n_ser=1, n_prll=1, pkrt=1.2)
predict voltage at a given time under a given load
predict the entire pack’s instantaneous terminal voltage under load uses empirical state-of-charge curve fit obtained from Chen and Mora https://doi.org/10.1109/TEC.2006.874229
INPUTS:
I_load : float, Amps
the current draw at the battery terminals
t: float,hours
the instant in time
Q_Ah: float, Amp*hr
the rated capacity of the battery unit
R_int: float, Ohms
the internal resistance of the battery unit in the tens of mili-Ohms range
n_ser: int, non-dim (optional)
the number of battery units in series
n_prll: int, non-dim (optional)
the number of battery units in parallel
pkrt: float, non-dim (optional)
the Peukert constant of the battery
OUTPUTS:
V_term: float, Volts
the output voltage of the battery
dod: float, non-dim
the depth of discharge of the battery (percent depleted)
soc: float, non-dim
the state of charge of the battery (percent remaining)
SPECIAL NOTES:
1.— The applied current (I_load) and the time (t) can be vectors (ndarrays) to calculate the entire discharge curve of the battery. However, BOTH inputs must be vectors of the same length!
2.— Ensure that the inputs have the correct units! Hobby batteries are rated in mili-Amp*hr This code requires you to enter the capacity in Amp*hr
3.— Battery terminology is imprecise. A “pack” and “module” may mean different things to different people.
For example, the Thunder Power TP7700-6SR70 is a 7.7 Ah (Amp*hr) unit which consists of 6 cells in series. To model this battery unit, input 7.7 for Q_Ah and set n_ser = 6 If you had 4 of these units wired in series, set n_ser = 4*6 If you had 4 of these units wired in parallel, set n_prll = 4
4.— Normally, you should not deplete a lithium-ion battery below 3.5 V In extreme circumstances, you can delpete a li-ion battery down to 3.3 V NEVER deplete a battery below 3 V
evpy.batt_size(t_hr, e, rho=2037.0)
predict battery mass, size for a given duration, specific energy
INPUTS:
t: float, hours
time duration of mission or mission phase
e: float, Wh/kg
specific energy (energy/mass) of mission or mission phase
rho: float, kg/m^3 (optional)
the mass density (mass/volume) of a lipo battery
OUTPUTS:
m: float, kg
mass of required battery pack
U: float, m^3
volume of required battery pack
SPECIAL NOTES:
mass density (mass/volume) of typical lipo is ~2000 kg/m^3 or 2 g/cm^3
reasonable usable energy densities:
200 Wh/kg for an extremely well-optimized low-current application 170 Wh/kg for a low-current (fixed-wing) application 140 Wh/kg for a high-current (VTOL) application
evpy.esc_pred(Im, Pm, V, d, f_pwm=8000.0, Ron=0.01, Ton=1e-06)
predict ESC losses given specs and motor performance
INPUTS:
Imn: darray (float), Amps
the current pulled by the motor
Pm: ndarray (float), Watts
the power pulled by the motor
V: float, Volts
the input (DC) voltage to the ESC
d: float, non-dim
the non-dimensional throttle setting (duty ratio)
f_pwm: float, Hertz
the switching frequency of the ESC, about 8-32 kHz
Ron: float, Ohms
the R_ds_ON measure of the MOSFETs in the ESC, about 5-20 mOhms
Ton: float, s (seconds)
the transition period of the MOSFETs, about 1 microsecond
OUTPUTS:
I_dc: ndarray (float), Amps
the current draw of the ESC
P_dc:ndarray (float), Watts
the power draw of the ESC
n: ndarray (float), non-dim
the efficiency of the ESC
evpy.esc_size(P_req, sf=2.0)
predict esc size, mass given cont. power
predict the esc volume and mass using a purely empirical fit empirical data collected from 3 KDE, Castle, and HobbyWing data nearly uniform trends among all three datasets
P = 36.203m where [P] = Watts, [m] = grams P = 52.280U where [P] = Watts, [U] = cm^3
INPUTS:
P_req: float, Watts
the required continuous power output of the ESC
sf: float, non-dim (optional)
a safety factor for the prediction hobby rule of thumb is 2.0
OUTPUTS:
m:float, kg
the mass of the ESC
U: float, m^3
the volume of the ESC
evpy.motor_contour(N_rated, T_rated, kt, R, I0, num_pts=100)
roughly predict the motor’s torque/speed/efficiency contour
predicts motor efficiency within the motor’s rated operating window uses 3 high-level component parameters (Rm, kt, I0) applicable to sensorless, six-step commutation brushless DC motors DOES NOT factor in harmonics!
Note: kt = kv with SI units
INPUTS:
N_rated: float, rev/min
rated motor speed
T_rated: float, N.m
rated motor torque
kt: float, Newton-meter per Amp
torque constant of motor
Rm: float, Ohms
motor resistance (phase to phase)
I0: float, Amps
no-load current of motor
num_pts:int, non-dim
number of data points along each axis
OUTPUTS:
N: 2D ndarray (float), rev/min
a 2D array of the motor’s speed up to N_rated
T: 2D ndarray (float), Newton-meter
a 2D array of the motor’s torque up to T_rated
n: 2D ndarray (float), non-dim
a 2D array of the motor’s non-dimensional motor efficiency grid is over the entire torque/speed window
evpy.motor_pred(w, V, d, kt, Rm, I0)
predict motor performance for given specs
predict torque, power, current, and efficiency over a range of speed uses 3 high-level component parameters (Rm, kt, I0) and throttle applicable to sensorless, six-step commutation brushless DC motors
Note: kt = kv with SI units
INPUTS:
w: ndarray (float), rads/sec
range of motor speed
V: float, Volts
voltage of the DC bus
d: float, non-dim
non-dimensional throttle setting (duty ratio)
Rmf: loat, Ohms
motor resistance (phase to phase)
kt: float, Newton-meter per Amp
torque constant of motor
I0: float, Amps
no-load current of motor
OUTPUTS:
T: ndarray (float), Newton-meter
output torque of motor
P_out: ndarray (float), Watts
output power (mechanical)
I: ndarray (float), Amps
input current to motor
P_in: ndarray (float), Watts
input power (AC)
n: ndarray (float), non-dim
non-dimensional motor efficiency
evpy.motor_size(T, x, shear=5500.0)
Size a motor for a given torque, aspect ratio
Predict mass, diameter, length, figure of merit for given torque, D/L Default shear stress is for sub-500 gram BLDC motors
INPUTS:
T: float, N.m
continuous torque required of motor
x: float, non-dim
stator aspect ratio (D/L)
shear: float, Pa (N/m^2) - OPTIONAL
shear stress used to size the initial volume default value of 5.5 kPa is a conservative est.
OUTPUTS:
m_tot: float, kg
total mass of the motor
U_tot: float, m^3
total volume of the motor
Do: float, m
outer motor diameter
Lo: float, m
outer motor length
km: float, N.m/sqrt(Ohms)
figure of merit (motor constant) of the motor
نیازمندی
| مقدار | نام |
|---|---|
| - | scipy |
| - | numpy |
نحوه نصب
نصب پکیج whl evpy-1.1.0:
pip install evpy-1.1.0.whl
نصب پکیج tar.gz evpy-1.1.0:
pip install evpy-1.1.0.tar.gz