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The paper presents use case simulations of fleets of electric buses in two cities in Europe, one with a warm Mediterranean climate and the other with a Northern European (cool temperate) climate, to compare the different climatic effects of the thermal management strategy and charging management strategy. Two bus routes are selected in each city, and the effects of their speed, elevation, and passenger profiles on the energy and thermal management strategy of vehicles are evaluated. A multi-objective optimization technique, the improved Simple Optimization technique, and a “brute-force” Monte Carlo technique were employed to determine the optimal number of chargers and charging power to minimize the total cost of operation of the fleet and the impact on the grid, while ensuring that all the buses in the fleet are able to realize their trips throughout the day and keeping the battery SoC within the constraints designated by the manufacturer. A mix of four different types of buses with different battery capacities and electric motor specifications constitute the bus fleet, and the effects that they have on charging priority are evaluated. Finally, different energy management strategies, including economy (ECO) features, such as ECO-comfort, ECO-driving, and ECO-charging, and their effects on the overall optimization are investigated. The single bus results indicate that 12 m buses have a significant battery capacity, allowing for multiple trips within their designated routes, while 18 m buses only have the battery capacity to allow for one or two trips. The fleet results for Barcelona city indicate an energy requirement of 4.42 GWh per year for a fleet of 36 buses, while for Gothenburg, the energy requirement is 5 GWh per year for a fleet of 20 buses. The higher energy requirement in Gothenburg can be attributed to the higher average velocities of the bus routes in Gothenburg, compared to those of the bus routes in Barcelona city. However, applying ECO-features can reduce the energy consumption by 15% in Barcelona city and by 40% in Gothenburg. The significant reduction in Gothenburg is due to the more effective application of the ECO-driving and ECO-charging strategies. The application of ECO-charging also reduces the average grid load by more than 10%, while shifting the charging towards non-peak hours. Finally, the optimization process results in a reduction of the total fleet energy consumption of up to 30% in Barcelona city, while in Gothenburg, the total cost of ownership of the fleet is reduced by 9%.

The main research areas on electro mobilityconverge from the same simulation tool as researchmethod, the power flow simulation model. The simulationof electric buses is fast and relatively accurate way forenergy efficiency studies. This is the reaso that a validatedmodel with real data from an electric bus is crucial. Thispaper aims to develop a methodology for validating thepower train model of a vehicle. A sequential approach hasbeen proposed starting from the dynamometer speed andacceleration profiles determination from the dynamometer.Taking the dynamometer data as a reference the backwardmodel wheel forces constants and powertrain efficienciesare determined. Finally, once the bakcward model hasbeen validated the forward model is validated using thebackward model as a reference. © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

A method for optimization of electric vehicle fleets is proposed. The method utilizes a hybrid particle swarm optimization algorithm with both continuous and binary variables. The battery capacity, battery chemistry, charging locations, charging power and number of chargers are obtained as an output of the optimization. A vehicle fleet simulation model is used to evaluate the cost of the system. The energy consumption for various routes and the costs for charging infrastructure, energy and purchase of vehicles are obtained as a result from the simulations. The battery ageing is evaluated based on the state-of-charge cycles. The method is demonstrated on two different cases of electric buses in the Helsinki region.

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.

Battery electric buses have been tested in Helsinki region to gain experience on the actual energy consumption and how well they suit for the Finnish climate. Measurements were performed on a chassis dynamometer with various driving cycles and gross vehicle weights in order to be able to analyse the buses in detail. The measurements reveal significant differences in the energy consumption of the buses. The sensitivity to different cycles and loads also varies greatly. The observed differences can partly be explained by the different curb weights of the buses. Another reason for the differences is the regeneration rate, which was as low as 15% for some buses, while the best bus was able to regenerate over 35% of the energy.

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.

An induction machine model is proposed for the identification of rotor parameters using high-frequency signal injection. The model includes both the magnetic saturation caused by the fundamental-wave components and the frequency dependence encountered in the signal injection method. Both the skin effect in the rotor winding and the eddy current losses in the rotor core are taken into account. Sinusoidal signal injection is used at several frequencies, and the model parameters are fitted to the results. The rotor leakage inductance and the rotor resistance valid at low slip frequencies are also obtained from the model directly. Experimental results for a 45-kW machine are presented. It is shown that the model fits well to the measured data in various operating points, and the accuracy of the identified parameters is good.

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.

Operation of opportunity charged electric buses can be an economically feasible solution for public transport in cities. However, the operation requires more planning than the operation of conventional buses. The data obtained from a pre-commercial electric bus pilot project by Helsinki Region Transport with 12 electric buses are gathered and analysed. The key performance indicators, having both the PTA's and PTO's as well as the passengers in mind, are chosen. Simulations are performed on all bus routes using a simulation tool based on geographical information. The key performance indicators obtained from real operation are compared to the simulated results.

Charging Management Strategy is a critical aspect in electric bus fleets to minimize the impact on the local electricity grid and to minimize the financial cost to the bus operators. To realize a fleet of battery electric public transport buses in a city depends on two major stakeholders, namely the city bus operator and the electricity distribution systems operator. The cost of the electric charging infrastructure, including the high powered ultrafast DC chargers for opportunity charging and lower powered depot chargers for overnight charging is a significant investment for the city bus operator in terms of capital, installation, and grid connection costs, while the distribution system operator has to contend with significant power load on the electricity grid when multiple ultrafast chargers are in operation. This paper investigates a Use Case of an electric bus fleet plying a route, and the optimal selection of chargers, charging duration, and battery State of Charge that will minimize the impact on the local grid and minimize the total cost of ownership. A Simple Optimization algorithm was utilized for this purpose. Results show that the objectives are mutually exclusive, and there need to be a tradeoff to achieve the optimal balance between grid impact and total cost of ownership. Results also show that grid impact and the total cost of ownership are both minimized when opting for low c-rate charging instead of high c-rate charging or when charging only for short durations. Finally, an ECO-charging technique based on utilizing short-duration pulsed charging followed by cool-down periods instead of charging in one continuous longduration pulse was investigated to determine its efficacy in lowering the energy requirements of the bus by reducing the battery heat generation due to high c-rate charging. The optimum charging-to-cooldown ratio and the optimum charging pulse was found using brute force method to determine the lowest cooling energy consumption for a variety of charging rates. Results show that up to 5% reduction in grid impact can be achieved due to implementation of ECO-charging technique. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Due to magnetic saturation, the small-signal admittance of an induction motor is dependent on the direction of the excitation signal. The angle-dependence of the admittance can be used in the estimation of the flux angle, and it should be taken into account when identifying motor parameters. In this paper, the small-signal admittance of a 2.2-kW induction motor is measured at different excitation frequencies and operating points. The measured admittances are compared to data obtained by means of finite element analysis (FEA). A small-signal model of the induction motor is fitted to the admittances to analyze the results. The admittances obtained from FEA and laboratory experiments correspond well to each other, particularly at low excitation frequencies.