Metadata-Version: 2.1
Name: evpy
Version: 1.0.7
Summary: A phython package to predict the efficiency/size of electronic powertrain components
Home-page: UNKNOWN
Author: Dalton Chancellor
License: MIT
Description: 
        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 batteryâ€™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
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
