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For an efficient operation of a low voltage PMSM an optimized voltage usage is very important. Because of the relation between the low voltage and the high currents in this type of machine, a large voltage reserve is needed to compensate the influence of parameter mismatches and to guarantee a stable current control. As the power is limited by the low voltage in this type of hybrid drive systems, optimizing the voltage usage is also required to maximize the power and the torque availability. This paper describes a closed loop flux control to maximize the voltage usage. The controller feedback is used to estimate and maximize the available torque for each operating point.
For a low voltage IPMSM used in a hybrid drive system of a consumer car, it is of the highest importance to design a torque controller circuit that produces an accurate torque at the shaft. The accurate torque is needed to distribute the load between the combustion engine, or the manual break, and the electrical drive. As the capacitance of the batteries used in this type of car is usually very small, the control of the batteris state of charge and its output current is quite critical. Therefore, a precise torque control is elementary. Temperature changes have a large impact on the IPMSM internal parameters. Especially the permanent magnet flux and the stator resistance are affected by temperature changes. There are techniques to observe and calculate the temperature variation of these parameters. This contribution describes a method to handle the influence of temperature variation on the actual torque at the shaft, by correcting the current commands of the open loop controller.
Future electricity flows and their impact on the power distribution grid on a decentralized level
(2022)
Application and machine learning methods for dynamic load point controls of electric vehicles (xEVs)
(2020)
From the customer's perspective, the appeal of electric vehicles depends on the simplicity and ease of their use, such as flexible access to electric power from the grid to recharge the batteries of their vehicles. Therefore, the expansion of charging infrastructure will be an important part of electric mobility. The related charging infrastructure is a big challenge for the load capacity of the grid connection without additional intelligent charge management: if the control of the charging process is not implemented, it is necessary to ensure the total of the maximum output of all xEVs at the grid connection point, which requires huge costs. This paper proposes to build a prediction module for forecasting dynamic charging load using machine learning (ML) techniques. The module will be integrated into a real charge management concept with optimization procedures for controlling the dynamic load point. The value of load forecasting through practical load data of a car park were taken to illustrate the proposed methods. The prediction performance of different ML methods under the same data condition (e.g., holiday data) are compared and evaluated.