As part of the present work, we create a simulation model of a generic electric
vehicle with which we virtually drive on randomly created roads, with different
inclination profiles and vehicle speeds. Later, we use the data of the ride itself
to train learning models that optimize the calculation of route efficiency for any
route. The work consists of 7 chapters. In the first part, we describe driving-
measuring cycles such as NEDC and WLTP and how to create a random speed
profile for the needs of the master’s thesis, which profile is a hybrid between NEDC
and WLTP. In the second chapter, we describe the balance of forces on the road,
ie the forces that affect the personal vehicle, the propulsion, and others that act
in the opposite direction. In the third part, we describe the battery model and
its implementation in Simulink. Here we include some chronologically important
data and the technologies used to make the batteries. The most extensive chapter
or section is the fourth, where we present a synchronous motor, which is largely
represented by look-up tables. We also include the corresponding graphs of the
simulation results. In the fifth chapter, we describe the purpose of control, and
the PI regulator used in our vehicle model for torque control, where the input of
the controller gets the actual speed and reference speed. In the sixth chapter, we
describe machine learning algorithms and create learning models such as neural
network, support vector machine, and k-nearest neighbors. In the last seventh
chapter, we analyze the results and describe further work.
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