PyEVPowerKit

Python Electric Vehicle Power Toolkit (PyEVPowerKit) is a Python toolkit designed for engineers and enthusiasts to facilitate the design and analysis of electrical power-trains for electric vehicles (EVs). The toolkit implements a mechanical vehicle model considering all source acting on the vehicle while driving as well as the power train of the electric vehicle. It calculates the mechanical, electrical, and thermal loads on the different components of the drive-train of the vehicle. Additionally, the thermal stress on the different components is evaluated.

Dependencies

The requirements of the PyEVPowerKit toolkit are summarized in the requirements.txt data file. In detail, the PyEVPowerKit Toolkit was implemented using the following dependencies:

• Python 3.11
• pandas 2.2.1
• scipy 1.12.0
• numpy
• matplotlib

Citation

The symbolic MTPA/MPTV solver has been abstracted from [2].

Limitations

Since the toolkit is still under development there are several things that need to be improved, are not yet implemented or lack verification. In the following a list of known issues and limitations is provided:

• SSM and ASM machine types are not yet implemented
• The model is only defined for positive velocities, i.e. v>0

Architecture

The presented architecture includes a physical vehicle simulation considering all forces acting on the vehicle. In detail, these forces including: air friction (Fa), rolling friction (Fr), climbing forces (Fc), and acceleration forces (Facc). The total force (Ft) is then calculated as the sum of these forces.

$F_{t} = F_{a} + F_{r} + F_{c} + F_{acc}$

In detail, each of these forces can be described using the laws of physics:

$F_{a} = \frac{1}{2} \cdot \rho_{A} \cdot A \cdot c_{w} \cdot v^2$

$F_{r} = c_{r} \cdot m \cdot g \cdot cos(\alpha)$

$F_{c} = m \cdot g \cdot sin(\alpha)$

$F_{acc} = (m + m_{acc}) \cdot \frac{dv}{dt}$

where $\rho_{A}$ is the density of air, $A$ is the vehicle frontal area, $c_{w}$ is the shape factor for the air resistance $v$ is the vehicle speed, $m$ is the vehicle mass, $m_{acc}$ is the equivalent acceleration mass (e.g. due to rotating parts), and $\alpha$ is the slope of the road. Based on the above the required mechanical torque, power, and energy can be determined for a lossless system.

$M_{wheel} = \frac{1}{2} \cdot F_{t} \cdot r_{dyn}$

$P_{veh} = F_{t} \cdot v$

$E_{veh} = \int_{0}^{T_{end}} P_{veh} \cdot dt$

where $M_{wheel}$ is the torque per wheel, $P_{veh}$ the mechanical power, and $E_{veh}$ the mechanical energy of the lossless vehicle. Additionally $r_{dyn}$ is the dynamic wheel radius. Knowing the required torque per wheel and further assuming a gearbox with a constant gear ratio $i_{g}$, each operation point can be described by a set of torque and rotational speed values. The components can than be calculated according to the architecture presented below:

Results

The results provided in this section have been obtained from a Tesla model 3 [1]. In detail, the acceleration to the maximum vehicle speed and the WLTP operation are considered. The parameters and the results are listed below.

Parameters

All parameters can be found under /setup in the different worksheets of the Excel file. An overview of the most important parameters is provided below:

Parameter	Description	Value	Unit
n_max	Maximum rotational speed machine	16000	1/min
n_0	Nominal rotational speed machine	5000	1/min
T_max	Maximum machine torque	420	Nm
I_max	Maximum RMS stator current	971	A
P_max	Maximum machine power	239	kW
Psi	Magnetic flux linkage	0.074	Vs
L_d	Inductance d-axis	110	uH
L_q	Inductance q-axis	340	uH
R_s	Stator resistance 20 degC	4.85	mOhm
p	Number of pole pairs	3	-
J_rot	Inertia rotor	0.12	kgm2

Maximum Acceleration

In this operating mode the vehicle accelerates from standstill ($v=0$), to its maximum speed ($v=v_{max}$).

All other results can be obtained by running startAcceleration.py

WLTP operation

In this operating mode the vehicle is driving the WLTP cycle. The velocity, acceleration, and distance over time is illustrated in the figure below:

All other results can be obtained by running startWLTP.py

Development

As failure and mistakes are inextricably linked to human nature, the toolkit is obviously not perfect, thus suggestions and constructive feedback are always welcome. If you want to contribute to the PyDTS toolkit or spot any mistake, please contact me via: p.schirmer@herts.ac.uk

License

The software framework is provided under the MIT License.

Version History

1. v.0.1: (16.04.2024) Initial version of PyEVPowerKit
2. v.0.2: (21.04.2024) Optimising MTPA and MPTV solver

References

[1] https://motorxp.com/wp-content/uploads/mxp_analysis_TeslaModel3.pdf

[2] Schlüter, Michael, Marius Gentejohann, and Sibylle Dieckerhoff. "Driving Cycle Power Loss Analysis of SiC-MOSFET and Si-IGBT Traction Inverters for Electric Vehicles." 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe). IEEE, 2023.