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This paper demonstrates the potential of a developed simulation tool for electric vehicle systems to replicate the real life operational activity of a bus line using techno-economic studies. The system level concept represents holistic view of the bus operation and charging infrastructure, including different operational conditions such as current traffic, stop frequencies and stopping times. The proposed simulation tool is built in a modular way, including modules for vehicle dynamics, routes, charging infrastructure and related conditions. The modules interact with various parameters in a simulation loop to illustrate the operation of electric vehicle systems. To evaluate the performance of the designed system tool a test simulation example of an actual bus line running in Helsinki, Finland is employed. The obtained results show that the simulated transient energy consumptions and powertrain power, as well as actualised bus schedules, are very close to the empirical measurements, which prove the efficiency of the tool and its great potential to simulate complex road transportation scenarios.

The induction machine model parameters need to be estimated with good accuracy to ensure a good performance of the drive. Due to the magnetic saturation, the inductances vary as a function of the flux level. The magnetizing curve can be identified at standstill, but more accurate results are obtained if the identification is performed as the machine is running. In this paper, the magnetic saturation is modeled using a power function, and adaptation laws for the function parameters are proposed. The adaptation method is implemented in the control system of a sensorless drive. Experimental results on a 2.2-kW machine show that the identification of the stator inductance is rapid and the accuracy is good.

This paper applies a dynamic space-vector model to loss-minimizing control in induction motor drives. The induction motor model, which takes hysteresis losses and eddy-current losses as well as the magnetic saturation into account, improves the flux estimation and rotor-flux-oriented control. Based on the corresponding steady-state loss function, a method is proposed for solving the loss-minimizing flux reference at each sampling period. A flux controller augmented with a voltage feedback algorithm is applied for improving the dynamic operation and field weakening. Both the steady-state and dynamic performance of the proposed method is investigated using laboratory experiments with a 2.2-kW induction motor drive. The method improves the accuracy of the loss minimization and torque production, it does not require excessive computational resources, and it shows fast convergence to the optimum flux level.

A small-signal model is derived for saturated induction machines. Inductances are allowed to saturate as a function of their own current (or flux), and the mutual saturation effect originating mainly from skewed or closed rotor slots is also included in the model. The model fulfills the reciprocity conditions, and it can be applied to parameter identification and to the analysis and development of flux-angle estimation methods. As application examples, the parameters of a 2.2-kW induction machine were identified using the data obtained from time-stepping finite-element analysis and locked-rotor measurements. The proposed model fits well to the data, and the fitted parameters are physically reasonable.

This paper proposes a method for including both hysteresis losses and eddy current losses in the dynamic space vector model of induction machines. The losses caused by the rotation and magnitude changes of the flux vector are taken into account. The model can be applied, for example, to time-domain simulations and real-time applications such as drive control. Finite element analysis, simulations, and laboratory experiments of a 45-kW motor are used for the investigation. It is shown that the model can predict the iron losses in a wide frequency range. The accuracy is significantly improved as compared to earlier models.