The motivation behind this thesis stems from potential gains that are to be made by optimizing the movement profile of Automated Guided Vehicles (AGV) in an industrial setting. By minimizing the energy consumption of an AGV we can achieve increased range, larger recharging intervals, and possibly financial benefits. We built our work on a case at hand at Volvo Group and specifically on the use of Simplex Motion SH100B BLDC motor on AGV in the production line. Previous research has shown that high acceleration rates can have a negative impact on the average energy consumption of an AGV, while different opinions can be found in research on optimizing the cruising velocity.

Using two such identical motors, a test rig is built where one motor acts as a driving motor (DUT, Drive Under Test) and the other as the Brake. The test rig is also equipped with an Arduino microcontroller and an ACS712A current sensor through which power measurements at different rotational speeds and torques. Rotational speeds and torques are provided by the integrated controllers of the two motors. The data collection is automated using Modbus interface with a laptop which runs with python code. From the collected datapoints power models for the cases of the motor being used as a driving motor (e.g.,during AGV acceleration and cruising) or as a Brake motor (during the deceleration phase).

After developing the power model, we then proceed to create custom Python classes that represent both the motor and the AGV. These classes have been equipped with attributes and methods so, jointly, they can be used in bigger simulation experiments to estimate the power consumption of an AGV with custom mechanical characteristics and equipped with any number of motors. Both classes have been written so they can be easily used to represent different motors (with different power models) or AGVs with various mechanical attributes. We demonstrate the use of the classes in trial run with a hypothetical AGV (based largely on rough parameters provided by Volvo) in various cruise velocities, accelerations, and decelerations and at different loads. We then demonstrate how python optimization libraries can be used to estimate the optimal cruise velocity, acceleration, and deceleration for a trapezoid AGV movement, (the most frequently considered profile although the AGV class support also S-curve acceleration and deceleration). Finally, we explore the relationship between AGV load and optimal acceleration and deceleration.