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In this paper, a comprehensive optimization model for decision support on public transportation system electrification is given. The basic assumption is that the transition is being governed centrally. This assumption together with a rather deterministic nature of the public transportation compared to a private enables a holistic approach in transition towards a fully electric system. The model comprises the following components: the electrification of the buses, the building of the charging stations and the electric grid reinforcement. These investment components are combined with the planned operating costs related to the cost of the energy used for charging and together yield a holistic model for transition planning.

Without experiences of electric buses, public transport authorities and bus operators have faced questions about how to implement them in a cost-effective way. Simple cost modelling cannot show how costs for different types of electric buses differ between different routes and timetables. Tools (e.g., HASTUS, PtMS, and optibus) which can analyse such details are complicated, time consuming to use, and provide insufficient insights into the mechanisms that influence the cost. This paper therefore proposes a method for how to calculate total cost of ownership, for different types of electric buses, in a way which can predict how the cost varies based on route and timetable. The method excludes factors which cause minor cost variations in an almost random manor, in order to better show the fundamental mechanisms influencing different costs. The method will help in finding ways to reduce the cost and help to define a few cases which deserve a deep analysis with more complete tools. Testing of the method in a Swedish context showed that the results are in line with other theoretical and practical studies, and how the total cost of ownership can vary depending on the variables. open access

This study investigates the impact of different levels of modeling details on the problem of optimizing the total cost of ownership of a fuel-cell hybrid electric vehicle. In this optimization, the objective function is a weighted sum of operational and component costs over a driving cycle. The former includes the consumed hydrogen and electrical energy, and the latter includes the sum of the battery, fuel-cell, and electric-motor costs. Three methods with different levels of modelling details are investigated; in the first method, the power split between the two power sources together with component sizes are optimized, while assuming nonlinear loss functions for the components. In the second method, the efficiencies of the components are approximated by constant values. In the third method, the problem is simplified further by considering the energy split between the battery and the fuel-cell. As shown in the results, a more detailed model gives more accurate results at the price of increased computation time. However, the simplified models can give similar results as the detailed model in most cases. In some problems though, the model simplifications lead to results that differ notably from those obtained by using the detailed model.

In this paper, the problem of optimal sizing of a series PHEV is studied by formulating a convex program that minimizes the sum of operational and component costs. The solution gives the optimal sizes of the main powertrain components, simultaneously with the vehicle’s optimal energy management. Investigations are performed on driving cycles generated stochastically from real data using Markov chains, with different driving distance distributions and charging patterns. The results show that the optimal component sizing is affected more from the driving distances between charging opportunities, than the speed profile of the driving. With anticipated future battery and petroleum prices, larger battery sizes are obtained.

This paper presents a model based control strategy aimed to reduce noise and wear during gearshifts in conventional and hybrid Dual Clutch Transmissions (DCT and DCTH) and Automated Manual Transmissions (AMT). The control strategy is based on a newly developed dog teeth position sensor layout at China Euro Vehicle Technology AB (CEVT), a detailed simulation model for gear engagement and already existing speed sensors in the transmission. The details of dog teeth position sensor and simulation model are also presented in this paper. During gear shifting, noise is generated because of impacts between the sleeve teeth and the idler gear dog teeth after speed synchronization. Besides noise, these impacts are also responsible for delaying the completion of shift and contribute to wear in the dog teeth, hence reducing the lifespan of the transmission. The simulation model for gear engagement can simulate these impacts. Based on the simulation model and optimal control theory, an ideal dog teeth position trajectory is formulated that avoids the impact between sleeve and idler gear dog teeth, before the start of torque ramp up. The open loop strategy then controls the synchronization torque in the beginning of speed synchronization in such a way that the dog teeth position during shift follows the ideal dog teeth position trajectory. Since the control strategy is based on optimal control theory, its effect on speed synchronization time is minimal. The control strategy is designed in such a way that it can easily be applied in the existing transmission control software. By applying the control strategy on the simulation model, it is shown that the impacts during gear engagement are reduced.

Earlier studies have mainly focused on technology, economy and advantages of electric buses, and they have largely shown that electric buses could be one of the solutions for sustainable public transport. Despite this, the present procurement process for public transport in Sweden is not suitable for including support systems for electric buses. This study was aimed to find a stakeholder collaboration model that would allow electric bus systems to be more effectively included in the procurement process for public transport. The results were achieved by several multi-stakeholder collaboration seminars and meetings that included representatives from regional public transport authorities, bus operators, bus manufacturers, energy companies, municipalities, and experts involved in bus transport. The study primarily developed two stakeholder collaboration models, suggesting that charging infrastructure should be designed separately from the common procurement process. In these models, energy companies, electric grid owners, charging infrastructure operators, regional public transport authorities, and municipalities need to collaborate. The first model is designed for a system that includes chargers at certain locations along a route and/or stakeholders with a low level of experience of electric bus systems, while the second is designed for a system that includes bus charging at the depot and/or stakeholders with a high level of experience of electric bus systems. open access

Fuel saving potential of hybrid electric vehicles (HEVs) depends mainly on driving cycle and on sizing of powertrain components. Since a complete driving cycle, representing whole life usage a vehicle, is very long it is time consuming to predict the fuel saving potential, especially if many different types of HEV's should be analyzed. This paper presents an energy based method to quickly screen different types of HEVs for many and long driving cycles, in order to find interesting candidates for deeper and more accurate analysis. The technique used also allows to derive the fuel consumption analytically, and thus it is a very effective tool to explain the main fuel savings mechanisms of different types of HEVs and how they are influenced by the driving cycle. Some of the simplifications will lead to errors, but since the sign of the main errors are known it is still easy to draw several clear conclusions using the method. (C) 2016, FAG (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

The role of EVs can range from that of passive loads with no difference compared to the traditional ones to active components highly beneficial to the modern power system. An essential part in changing EV storages from passive to active components is the contractual framework. Therefore, we present several different agreements that can be formed between a power supplier and an EV owner. These agreements establish the terms for utilization of storages when an EV is parked and connected to a household charger. We show that these benefits are significant and present a valuable economic opportunity for both EV owners and power suppliers.

This paper proposes a time-efficient method for sub-optimal design of a plug-in hybrid electric vehicle with a parallel powertrain topology. The method finds the optimal design of the vehicle by iteratively using dynamic programming (DP) and convex optimization to minimize sum of operational and component costs over a given driving cycle. In particular, DP is used to optimize energy management, gear shifting and engine on-off for given component sizes, and convex optimization is used to optimize energy management and component sizes using the gear shifting and engine on-off strategies obtained by DP. Next, DP is re-optimized with the component sizes obtained by convex optimization, and the procedure is repeated until the component sizes converge. The result of this iterative method is compared by using DP on a grid of possible component sizes. It is shown that the iterative method gives a result very close to the global optimum in a comparably short time.

This paper introduces a novel method for the simultaneous optimization of energy management and powertrain component sizing of a parallel plug-in hybrid electric vehicle (PHEV). The problem is formulated as a convex optimization problem to minimize an objective function, which is a weighted sum of operational and component costs. The operational cost includes the consumed fossil fuel and electrical energy, whereas the component cost includes the cost of the battery, electric motor (EM), and internal combustion engine (ICE). The powertrain model includes quadratic losses for the powertrain components. Moreover, the combustion engine and the electric motor losses are assumed to linearly scale with respect to the size and the losses of baseline components. The result of the optimization is the variables of the global optimal energy management for every time instant and optimal component sizes. Due to the dependency of the result on the driving cycle, a long real-life cycle with its charging times is chosen to represent a general driving pattern. The method allows the study of the effect of some performance requirements, i.e., acceleration, top speed, and all-electric range, on the component sizes and total cost.

In the past decade, it has been demonstrated that Plug-in Hybrid Electric Vehicles (PHEVs) can significantly reduce petroleum consumptions. However, the extend to which these vehicles can reduce the petroleum consumption highly depends on components size and driving patterns. In other words, PHEVs show the best benefits if the components are dimensioned to match the driver’s driving behavior. In this paper, the effect of different driving patterns on the optimal sizing of three major components of series PHEVs, i.e., battery, electric motor, and engine generator unit is studied. Different driving cycles are generated stochastically from real driving data using Markov chains, to represent life-time driving patterns of different drivers. To find the optimal size of the components, the problem is formulated as a convex optimization problem. The optimization variables (the variables of component size and energy management) are obtained by minimizing a cost function which is the sum of the operational and component costs.

This work discusses the practical limits imposed by magnetic saturation for the force density in low-speed permanent-magnet electric machines. The force density dependence on current density and slot depth is investigated with the aid of finite-element modeling. For saturation reasons, shallow slots are more favorable for achieving high force densities. However, for thermal reasons, deeper slots become favorable. Therefore, an optimum slot depth that maximizes the force density for each current density level exists. The maximum allowable slot depth range for four low-speed applications has been identified for a given maximum motor diameter.

This paper discusses two of the mechanisms that limit the attainable force density in slotted low-speed permanent-magnet (PM) electric machines. Most of the interest is focused on the force density limits imposed by heating of the windings and by stator reactance. The study is based on analytical models for the force and reactance calculations and a lumped parameter thermal model. It is found that in a machine with an indirectly cooled stator, it is difficult to achieve a force density greater than 100 kN/m/sup 2/ due to temperature limits. A high force density is achieved by using deep slots, which lead to high reactance. The high reactance severely increases the converter kilovolt-ampere requirement and total system cost. It is also shown that the cost caused by the high reactance will also limit the force density reached. In machines with one slot per pole per phase, the reactance limited the useful slot depth to approximately 200 mm. However, in machines having a greater number of slots per pole per phase the reactance becomes no longer an important limiting factor for the slot depth and force density.

This paper presents a method to calculate the average efficiency from the turbine shaft to the grid in wind energy converters. The average efficiency of three 500 kW systems are compared. The systems are: a conventional grid-connected four-pole induction generator equipped with a gear, a variable-speed synchronous generator equipped with a gear and a frequency converter, and a directly driven variable-speed generator equipped with a frequency converter. In this paper it is shown that a variable-speed generator system can be almost as efficient as one for constant speed, although it has much higher losses at rated load. The increased turbine efficiency that variable speed leads to has not been included in this paper. It is also found that a directly driven generator can be more efficient than a conventional four-pole generator equipped with a gear.

This paper describes modeling steps to enable fast evaluation of performance and cost effectiveness of a plug-in hybrid electric vehicle. The paper also shows how convex optimization can be used to dimension the vehicle powertrain while simultaneously controlling the energy buffer power. The method allows for optimal control of powertrain components that are subject to thermal constraints. The studied vehicle is a city bus driven along a perfectly known bus line. The bus is equipped with an engine-generator unit and an energy buffer consisting of an ultracapacitor and a battery. The engine generator unit and the energy buffer are modeled with quadratic power losses and are sized for two different charging scenarios. In the first scenario the bus can charge for a couple of seconds while standing still at bus stops, and in the second scenario the bus can charge for a couple of minutes before starting the route. In both scenarios, the ultracapacitor temperature is kept below a certain limit.

This paper presents a method developed to analyze a complete electric bus system including electricity supply infrastructure, charging system, buses' energy system and scheduling. This method can give an understanding of what kind of electric bus system that is the most cost-effective based on local conditions such as transport demand and type of electricity infrastructure available. It is being used as a tool in planning and construction of future public transport systems. Conference code: 125226

This paper is an update on a method to analyze and compare electric bus and charging systems from a totalcost perspective. The method is useful for proposing a suitable combination of bus and charger systemsdepending on the route specifications, timetables and other local conditions. In this update, a user-friendlytool which simplifies the analysis process is presented. The analysis tool enables the user to experimentallyinvestigate and quantify the trade-offs between parameters such as battery size, charging strategies andcharging infrastructure, vehicle fleet and operational costs from a total cost perspective.