Minimizing residential distribution system operating costs through intelligently scheduled plug-in hybrid electric vehicle charging

Rising fuel prices and environmental concerns are threatening the stability of current electrical grid systems. These factors are pushing the automobile industry towards more effcient, hybrid vehicles. Current trends show petroleum is being edged out in favor of electricity as the main vehicular motive force. The proposed methods create an optimized charging control schedule for all participating Plug-in Hybrid Electric Vehicles in a distribution grid. The optimization will minimize daily operating costs, reduce system losses, and improve power quality. This requires participation from Vehicle-to-Grid capable vehicles, load forecasting, and Locational Marginal Pricing market predictions. Vehicles equipped with bidirectional chargers further improve the optimization results by lowering peak demand and improving power quality.

Dual Active Bridge based Battery Charger for Plug-in Hybrid Electric Vehicle with Charging Current Containing Low Frequency Ripple

High power density is strongly preferable for the on-board battery charger of Plug-in Hybrid Electric Vehicle (PHEV). Wide band gap devices, such as Gallium Nitride HEMTs are being explored to push to higher switching frequency and reduce passive component size. In this case, the bulk DC link capacitor of AC-DC Power Factor Correction (PFC) stage, which is usually necessary to store ripple power of two times the line frequency in a DC current charging system, becomes a major barrier on power density. If low frequency ripple is allowed in the battery, the DC link capacitance can be significantly reduced. This paper focuses on the operation of a battery charging system, which is comprised of one Full Bridge (FB) AC-DC stage and one Dual Active Bridge (DAB) DC-DC stage, with charging current containing low frequency ripple at two times line frequency, designated as sinusoidal charging. DAB operation under sinusoidal charging is investigated. Two types of control schemes are proposed and implemented in an experimental prototype. It is proved that closed loop current control is the better. Full system test including both FB AC-DC stage and DAB DC-DC stage verified the concept of sinusoidal charging, which may lead to potentially very high power density battery charger for PHEV.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

New integrated multilevel converter for switched reluctance motor drives in plug-in hybrid electric vehicles with flexible energy conversion

This paper presents an integrated multilevel converter of switched reluctance motors (SRMs) fed by a modular front-end circuit for plug-in hybrid electric vehicle (PHEV) applications. Several operating modes can be achieved by changing the on-off states of the switches in the front-end circuit. In generator driving mode, the battery bank is employed to elevate the phase voltage for fast excitation and demagnetization. In battery driving mode, the converter is reconfigured as a four-level converter, and the capacitor is used as an additional charge capacitor to produce multilevel voltage outputs, which enhances the torque capability. The operating modes of the proposed drive are explained and the phase current and voltage are analyzed in details. The battery charging is naturally achieved by the demagnetization current in motoring mode and by the regenerative current in braking mode. Moreover, the battery can be charged by the external AC source or generator through the proposed converter when the vehicle is in standstill condition. The SRM-based PHEV can operate at different speeds by coordinating the power flow between the generator and battery. Simulation in MATLAB/Simulink and experiments on a three-phase 12/8 SRM confirm the effectiveness of the proposed converter topology.

Distributed Energy Resources Management using Plug-in Hybrid Electric Vehicles as a Fuel-Shifting Demand Response Resource

In the smart grids context, distributed energy resources management plays an important role in the power systems’ operation. Battery electric vehicles and plug-in hybrid electric vehicles should be important resources in the future distribution networks operation. Therefore, it is important to develop adequate methodologies to schedule the electric vehicles’ charge and discharge processes, avoiding network congestions and providing ancillary services. This paper proposes the participation of plug-in hybrid electric vehicles in fuel shifting demand response programs. Two services are proposed, namely the fuel shifting and the fuel discharging. The fuel shifting program consists in replacing the electric energy by fossil fuels in plug-in hybrid electric vehicles daily trips, and the fuel discharge program consists in use of their internal combustion engine to generate electricity injecting into the network. These programs are included in an energy resources management algorithm which integrates the management of other resources. The paper presents a case study considering a 37-bus distribution network with 25 distributed generators, 1908 consumers, and 2430 plug-in vehicles. Two scenarios are tested, namely a scenario with high photovoltaic generation, and a scenario without photovoltaic generation. A sensitivity analyses is performed in order to evaluate when each energy resource is required.

An MILP model for the plug-in electric vehicle charging coordination problem in electrical distribution systems

A new mixed-integer linear programming (MILP) model is proposed to represent the plug-in electric vehicles (PEVs) charging coordination problem in electrical distribution systems. The proposed model defines the optimal charging schedule for each division of the considered period of time that minimizes the total energy costs. Moreover, priority charging criteria is taken into account. The steady-state operation of the electrical distribution system, as well as the PEV batteries charging is mathematically represented; furthermore, constraints related to limits of voltage, current and power generation are included. The proposed mathematical model was applied in an electrical distribution system used in the specialized literature and the results show that the model can be used in the solution of the PEVs charging problem.

New SR drive with integrated charging capacity for plug-in hybrid electric vehicles (PHEVs)

Plug-in hybrid electric vehicles (PHEVs) provide much promise in reducing greenhouse gas emissions and, thus, are a focal point of research and development. Existing on-board charging capacity is effective but requires the use of several power conversion devices and power converters, which reduce reliability and cost efficiency. This paper presents a novel three-phase switched reluctance (SR) motor drive with integrated charging functions (including internal combustion engine and grid charging). The electrical energy flow within the drivetrain is controlled by a power electronic converter with less power switching devices and magnetic devices. It allows the desired energy conversion between the engine generator, the battery, and the SR motor under different operation modes. Battery-charging techniques are developed to operate under both motor-driving mode and standstill-charging mode. During the magnetization mode, the machine's phase windings are energized by the dc-link voltage. The power converter and the machine phase windings are controlled with a three-phase relay to enable the use of the ac-dc rectifier. The power converter can work as a buck-boost-type or a buck-type dc-dc converter for charging the battery. Simulation results in MATLAB/Simulink and experiments on a 3-kW SR motor validate the effectiveness of the proposed technologies, which may have significant economic implications and improve the PHEVs' market acceptance.

Performance assessment of vehicular connectivity in hybrid electric vehicles for fuel and emissions reduction

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of global warming. Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. In order to address these issues, the present thesis presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. However, even if hybrid electric vehicles have been recognized as a valid solution in response to increasingly tight regulations, the reduced engine load and the repeated engine starts and stops may reduce substantially the temperature of the exhaust after-treatment system (EATS), leading to relevant issues related to pollutant emission control. In this context, electrically heated catalysts (EHCs) represent a promising solution to ensure high pollutant conversion efficiency without affecting engine efficiency and performance. This work aims at studying the advantages provided by the introduction of a predictive EHC control function for a light-duty Diesel plug-in hybrid electric vehicle (PHEV) equipped with a Euro 7-oriented EATS. Based on the knowledge of future driving scenarios provided by vehicular connectivity, engine first start can be predicted and therefore an EATS pre-heating phase can be planned.

An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.

A multi-agent system with a percolation approach to simulate the driving pattern of Plug-In ELectric Vehicles

A multi-agent system with a percolation approach to simulate the driving pattern of Plug-In Electric Vehicle (PEV), especially suited to simulate the PEVs behavior on any distribution systems, is presented. This tool intends to complement information about the driving patterns database on systems where that kind of information is not available. So, this paper aims to provide a framework that is able to work with any kind of technology and load generated of PEVs. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with corresponding load level, and their relationships with the neighboring zones are represented as network probabilities. A percolation approach is used to characterize the autonomy of the battery of the PVEs to move through the city. The methodology is tested with data from a mid-size city real distribution system. The result shows the sub-area where the battery of PEVs will need to be recharge and gives the planners of distribution systems the necessary input for a medium to long term network planning in a smart grid environment. © 2012 IEEE.

IMPACT OF PLUG-IN ELECTRIC VEHICLES AND WIND GENERATORS ON HARMONIC DISTORTION OF ELECTRIC DISTRIBUTION SYSTEMS

Harmonic distortion on voltages and currents increases with the increased penetration of Plug-in Electric Vehicle (PEV) loads in distribution systems. Wind Generators (WGs), which are source of harmonic currents, have some common harmonic profiles with PEVs. Thus, WGs can be utilized in careful ways to subside the effect of PEVs on harmonic distortion. This work studies the impact of PEVs on harmonic distortions and integration of WGs to reduce it. A decoupled harmonic three-phase unbalanced distribution system model is developed in OpenDSS, where PEVs and WGs are represented by harmonic current loads and sources respectively. The developed model is first used to solve harmonic power flow on IEEE 34-bus distribution system with low, moderate, and high penetration of PEVs, and its impact on current/voltage Total Harmonic Distortions (THDs) is studied. This study shows that the voltage and current THDs could be increased upto 9.5% and 50% respectively, in case of distribution systems with high PEV penetration and these THD values are significantly larger than the limits prescribed by the IEEE standards. Next, carefully sized WGs are selected at different locations in the 34-bus distribution system to demonstrate reduction in the current/voltage THDs. In this work, a framework is also developed to find optimal size of WGs to reduce THDs below prescribed operational limits in distribution circuits with PEV loads. The optimization framework is implemented in MATLAB using Genetic Algorithm, which is interfaced with the harmonic power flow model developed in OpenDSS. The developed framework is used to find optimal size of WGs on the 34-bus distribution system with low, moderate, and high penetration of PEVs, with an objective to reduce voltage/current THD deviations throughout the distribution circuits. With the optimal size of WGs in distribution systems with PEV loads, the current and voltage THDs are reduced below 5% and 7% respectively, which are within the limits prescribed by IEEE.

Sähköautojen akkuteknologioiden tilannekatsaus

Tämän kandidaatintyön tavoitteena oli tutkia sähköautoissa käytettäviä akkuteknologioita ja verrata niiden ominaisuuksia keskenään sekä sähköautojen asettamien akkuvaatimusten kanssa. Akkuteknologiakartoituksen ja ominaisuusvertailun avulla tutkimuksessa oli tarkoitus selvittää sähköautojen akkujen kehitystä menneestä nykyhetkeen ja luoda katsaus akkuteknologian tulevaisuuteen. Tutkimuksessa painotettiin akkujen suorituskykynäkökulmaa, mutta tutkimuksessa otettiin kantaa myös eri akkuteknologioiden turvallisuuteen, ympäristötekijöihin ja hintaan. Työ toteutettiin kirjallisuustutkimuksena ja lähteinä käytettiin alan kirjallisuutta, IEEE artikkeleita, tutkimusraportteja ja verkkodokumentteja. Lisäksi tutkimuksessa hyödynnettiin akku- ja sähköautovalmistajilta saatavaa tietoa, johon suhtauduttiin varauksin. Tutkimuksessa kävi ilmi, että erilaisia litiumioniakkuteknologioita käytetään tällä hetkellä eniten sekä täyssähköautoissa että pistokehybrideissä. Huomattiin, että akkujen suorituskyvyn kehittyminen on nopeutunut viime vuosina. Erityisesti akkujen energianvarastointikykyyn vaikuttavat ominaisenergiatasot ovat kasvaneet selkeästi. Nykyisen kehittyneen litiumioniakkuteknologian todettiin täyttävän jo osittain lähivuosien suorituskykytavoitteet. Tutkimuksessa tultiin siihen tulokseen, että litiumrikkiakkuteknologia voi korvata litiumioniakkuteknologian ainakin täyssähköautoissa parempien ominaisenergiatasojen ja halvempien valmistuskustannuksien takia. Myös litiumilma-akkuteknologialla havaittiin olevan mahdollisuuksia haastaa muut litiumakkuteknologiat seuraavalla vuosikymmenellä. Tutkimuksen johtopäätöksenä todetaan, että sähköautot voivat kaupallistua laajemmin lähivuosina akkujen suorituskykyominaisuuksien kehittyessä jatkuvasti. Suorituskykyominaisuuksien parantuminen tulee todennäköisesti johtamaan siihen, että täyssähköautot yleistyvät enemmän ja pistokehybridit tulevat jäämään sähköautojen välivaiheeksi. Uusien akkuteknologioiden käyttöönotto kaupallisiin sähköautoihin voi viedä kuitenkin odotettua kauemmin, sillä akut tarvitsevat huolellista testausta ja käyttöönotto edellyttää, että kaikki ominaisuudet ovat vaaditulla tasolla.

Hybrid Power System Intelligent Operation and Protection Involving Plug-in Electric Vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.

Hybrid power system intelligent operation and protection involving plug-in electric vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. ^ The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. ^ The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.^

Powertrain optimisation in a hybrid electric bus

This paper describes the use of a formal optimisation procedure to optimise a plug-in hybrid electric bus using two different case studies to meet two different performance criteria; minimum journey cost and maximum battery life. The approach is to choose a commercially available vehicle and seek to improve its performance by varying key design parameters. Central to this approach is the ability to develop a representative backward facing model of the vehicle in MATLAB/Simulink along with appropriate optimisation objective and penalty functions. The penalty functions being the margin by which a particular design fails to meet the performance specification. The model is validated against data collected from an actual vehicle and is used to estimate the vehicle performance parameters in a model-in-the-loop process within an optimisation routine. For the purposes of this paper, the journey cost/battery life over a drive cycle is optimised whilst other performance indices are met (or exceeded). Among the available optimisation methods, Powell's method and Simulated Annealing are adopted. The results show this method as a valid alternative modelling approach to vehicle powertrain optimisation. © 2012 IEEE.