International Science Index
A Review on the Potential of Electric Vehicles in Reducing World CO2 Footprints
The conventional Internal Combustion Engine (ICE) based vehicles are a threat to the environment as they account for a large proportion of the overall greenhouse gas (GHG) emissions in the world. Hence, it is required to replace these vehicles with more environment-friendly vehicles. Electric Vehicles (EVs) are promising technologies which offer both human comfort “noise, pollution” as well as reduced (or no) emissions of GHGs. In this paper, different types of EVs are reviewed and their advantages and disadvantages are identified. It is found that in terms of fuel economy, Plug-in Hybrid EVs (PHEVs) have the best fuel economy, followed by Hybrid EVs (HEVs) and ICE vehicles. Since Battery EVs (BEVs) do not use any fuel, their fuel economy is estimated as price per kilometer. Similarly, in terms of GHG emissions, BEVs are the most environmentally friendly since they do not result in any emissions while HEVs and PHEVs produce less emissions compared to the conventional ICE based vehicles. Fuel Cell EVs (FCEVs) are also zero-emission vehicles, but they have large costs associated with them. Finally, if the electricity is provided by using the renewable energy technologies through grid connection, then BEVs could be considered as zero emission vehicles.
Optimization by Means of Genetic Algorithm of the Equivalent Electrical Circuit Model of Different Order for Li-ion Battery Pack
The purpose of this article is to optimize the Equivalent Electric Circuit Model (EECM) of different orders to obtain greater precision in the modeling of Li-ion battery packs. Optimization includes considering circuits based on 1RC, 2RC and 3RC networks, with a dependent voltage source and a series resistor. The parameters are obtained experimentally using tests in the time domain and in the frequency domain. Due to the high non-linearity of the behavior of the battery pack, Genetic Algorithm (GA) was used to solve and optimize the parameters of each EECM considered (1RC, 2RC and 3RC). The objective of the estimation is to minimize the mean square error between the measured impedance in the real battery pack and those generated by the simulation of different proposed circuit models. The results have been verified by comparing the Nyquist graphs of the estimation of the complex impedance of the pack. As a result of the optimization, the 2RC and 3RC circuit alternatives are considered as viable to represent the battery behavior. These battery pack models are experimentally validated using a hardware-in-the-loop (HIL) simulation platform that reproduces the well-known New York City cycle (NYCC) and Federal Test Procedure (FTP) driving cycles for electric vehicles. The results show that using GA optimization allows obtaining EECs with 2RC or 3RC networks, with high precision to represent the dynamic behavior of a battery pack in vehicular applications.
Review of Strategies for Hybrid Energy Storage Management System in Electric Vehicle Application
Electric Vehicles (EV) appear to be gaining increasing patronage as a feasible alternative to Internal Combustion Engine Vehicles (ICEVs) for having low emission and high operation efficiency. The EV energy storage systems are required to handle high energy and power density capacity constrained by limited space, operating temperature, weight and cost. The choice of strategies for energy storage evaluation, monitoring and control remains a challenging task. This paper presents review of various energy storage technologies and recent researches in battery evaluation techniques used in EV applications. It also underscores strategies for the hybrid energy storage management and control schemes for the improvement of EV stability and reliability. The study reveals that despite the advances recorded in battery technologies there is still no cell which possess both the optimum power and energy densities among other requirements, for EV application. However combination of two or more energy storages as hybrid and allowing the advantageous attributes from each device to be utilized is a promising solution. The review also reveals that State-of-Charge (SoC) is the most crucial method for battery estimation. The conventional method of SoC measurement is however questioned in the literature and adaptive algorithms that include all model of disturbances are being proposed. The review further suggests that heuristic-based approach is commonly adopted in the development of strategies for hybrid energy storage system management. The alternative approach which is optimization-based is found to be more accurate but is memory and computational intensive and as such not recommended in most real-time applications.
A Data Driven Approach for the Degradation of a Lithium-Ion Battery Based on Accelerated Life Test
Lithium ion batteries are currently used for many applications including satellites, electric vehicles and mobile electronics. Their ability to store relatively large amount of energy in a limited space make them most appropriate for critical applications. Evaluation of the life of these batteries and their reliability becomes crucial to the systems they support. Reliability of Li-Ion batteries has been mainly considered based on its lifetime. However, another important factor that can be considered critical in many applications such as in electric vehicles is the cycle duration. The present work presents the results of an experimental investigation on the degradation behavior of a Laptop Li-ion battery (type TKV2V) and the effect of applied load on the battery cycle time. The reliability was evaluated using an accelerated life test. Least squares linear regression with median rank estimation was used to estimate the Weibull distribution parameters needed for the reliability functions estimation. The probability density function, failure rate and reliability function under each of the applied loads were evaluated and compared. An inverse power model is introduced that can predict cycle time at any stress level given.
Energy Benefits of Urban Platooning with Self-Driving Vehicles
The primary focus of this paper is the generation of
energy-optimal speed trajectories for heterogeneous electric vehicle
platoons in urban driving conditions. Optimal speed trajectories are
generated for individual vehicles and for an entire platoon under
the assumption that they can be executed without errors, as would
be the case for self-driving vehicles. It is then shown that the
optimization for the “average vehicle in the platoon” generates similar
transportation energy savings to optimizing speed trajectories for
each vehicle individually. The introduced approach only requires the
lead vehicle to run the optimization software while the remaining
vehicles are only required to have adaptive cruise control capability.
The achieved energy savings are typically between 30% and 50%
for stop-to-stop segments in cities. The prime motivation of urban
platooning comes from the fact that urban platoons efficiently utilize
the available space and the minimization of transportation energy in
cities is important for many reasons, i.e., for environmental, power,
and range considerations.
Comparison of the Thermal Characteristics of Induction Motor, Switched Reluctance Motor and Inset Permanent Magnet Motor for Electric Vehicle Application
Modern day electric vehicles require compact high torque/power density motors for electric propulsion. This necessitates proper thermal management of the electric motors. The main focus of this paper is to compare the steady state thermal analysis of a conventional 20 kW 8/6 Switched Reluctance Motor (SRM) with that of an Induction Motor and Inset Permanent Magnet (IPM) motor of the same rating. The goal is to develop a proper thermal model of the three types of models for Finite Element Thermal Analysis. JMAG software is used for the development and simulation of the thermal models. The results show that the induction motor is subjected to more heating when used for electric vehicle application constantly, compared to the SRM and IPM.
Improvement of Ride Comfort of Turning Electric Vehicle Using Optimal Speed Control
With the spread of EVs (electric Vehicles), the ride
comfort has been gaining a lot of attention. The influence of the lateral
acceleration is important for the improvement of ride comfort of EVs
as well as the longitudinal acceleration, especially upon turning of
the vehicle. Therefore, this paper proposes a practical optimal speed
control method to greatly improve the ride comfort in the vehicle
turning situation. For consturcting this method, effective criteria that
can appropriately evaluate deterioration of ride comfort is derived.
The method can reduce the influence of both the longitudinal and
the lateral speed changes for providing a confortable ride. From
several simulation results, we can see the fact that the method can
prevent aggravation of the ride comfort by suppressing the influence
of longitudinal speed change in the turning situation. Hence, the
effectiveness of the method is recognized.
Presentation of a Mix Algorithm for Estimating the Battery State of Charge Using Kalman Filter and Neural Networks
Determination of state of charge (SOC) in today’s world becomes an increasingly important issue in all the applications that include a battery. In fact, estimation of the SOC is a fundamental need for the battery, which is the most important energy storage in Hybrid Electric Vehicles (HEVs), smart grid systems, drones, UPS and so on. Regarding those applications, the SOC estimation algorithm is expected to be precise and easy to implement. This paper presents an online method for the estimation of the SOC of Valve-Regulated Lead Acid (VRLA) batteries. The proposed method uses the well-known Kalman Filter (KF), and Neural Networks (NNs) and all of the simulations have been done with MATLAB software. The NN is trained offline using the data collected from the battery discharging process. A generic cell model is used, and the underlying dynamic behavior of the model has used two capacitors (bulk and surface) and three resistors (terminal, surface, and end), where the SOC determined from the voltage represents the bulk capacitor. The aim of this work is to compare the performance of conventional integration-based SOC estimation methods with a mixed algorithm. Moreover, by containing the effect of temperature, the final result becomes more accurate.
Slip Suppression Sliding Mode Control with Various Chattering Functions
This study presents performance analysis results of
SMC (Sliding mode control) with changing the chattering functions
applied to slip suppression problem of electric vehicles (EVs). In
SMC, chattering phenomenon always occurs through high frequency
switching of the control inputs. It is undesirable phenomenon and
degrade the control performance, since it causes the oscillations of the
control inputs. Several studies have been conducted on this problem
by introducing some general saturation function. However, study
about whether saturation function was really best and the performance
analysis when using the other functions, weren’t being done so much.
Therefore, in this paper, several candidate functions for SMC are
selected and control performance of candidate functions is analyzed.
In the analysis, evaluation function based on the trade-off between
slip suppression performance and chattering reduction performance
is proposed. The analyses are conducted in several numerical
simulations of slip suppression problem of EVs. Then, we can
see that there is no difference of employed candidate functions
in chattering reduction performance. On the other hand, in slip
suppression performance, the saturation function is excellent overall.
So, we conclude the saturation function is most suitable for slip
suppression sliding mode control.
Electric Vehicle Market Penetration Impact on Greenhouse Gas Emissions for Policy-Making: A Case Study of United Arab Emirates
The United Arab Emirates is clearly facing a multitude of challenges in curbing its greenhouse gas emissions to meet its pre-allotted framework of Kyoto protocol and COP21 targets due to its hunger for modernization, industrialization, infrastructure growth, soaring population and oil and gas activity. In this work, we focus on the bonafide zero emission electric vehicles market penetration in the country’s transport industry for emission reduction. We study the global electric vehicle market trends, the complementary battery technologies and the trends by manufacturers, emission standards across borders and prioritized advancements which will ultimately dictate the terms of future conditions for the United Arab Emirate transport industry. Based on our findings and analysis at every stage of current viability and state-of-transport-affairs, we postulate policy recommendations to local governmental entities from a supply and demand perspective covering aspects of technology, infrastructure requirements, change in power dynamics, end user incentives program, market regulators behavior and communications amongst key stakeholders.
Design and Development of Real-Time Optimal Energy Management System for Hybrid Electric Vehicles
This paper describes a strategy to develop an energy
management system (EMS) for a charge-sustaining power-split hybrid
electric vehicle. This kind of hybrid electric vehicles (HEVs) benefit
from the advantages of both parallel and series architecture. However,
it gets relatively more complicated to manage power flow between the
battery and the engine optimally. The applied strategy in this paper is
based on nonlinear model predictive control approach. First of all, an
appropriate control-oriented model which was accurate enough and
simple was derived. Towards utilization of this controller in real-time,
the problem was solved off-line for a vast area of reference signals
and initial conditions and stored the computed manipulated variables
inside look-up tables. Look-up tables take a little amount of memory.
Also, the computational load dramatically decreased, because to find
required manipulated variables the controller just needed a simple
interpolation between tables.
Online Battery Equivalent Circuit Model Estimation on Continuous-Time Domain Using Linear Integral Filter Method
Equivalent circuit models (ECMs) are widely used in
battery management systems in electric vehicles and other battery
energy storage systems. The battery dynamics and the model
parameters vary under different working conditions, such as different
temperature and state of charge (SOC) levels, and therefore online
parameter identification can improve the modelling accuracy. This
paper presents a way of online ECM parameter identification using a
continuous time (CT) estimation method. The CT estimation method
has several advantages over discrete time (DT) estimation methods
for ECM parameter identification due to the widely separated battery
dynamic modes and fast sampling. The presented method can be used
for online SOC estimation. Test data are collected using a lithium ion
cell, and the experimental results show that the presented CT method
achieves better modelling accuracy compared with the conventional
DT recursive least square method. The effectiveness of the presented
method for online SOC estimation is also verified on test data.
Optimal Energy Management System for Electrical Vehicles to Further Extend the Range
This research targets at alleviating the problem of range anxiety associated with the battery electric vehicles (BEVs) by considering mechanical and control aspects of the powertrain. In this way, all the energy consuming components and their effect on reducing the range of the BEV and battery life index are identified. On the other hand, an appropriate control strategy is designed to guarantee the performance of the BEV and the extended electric range which is evaluated by an extensive simulation procedure and a real-world driving schedule.
Mobile Assembly of Electric Vehicles: Decentralized, Low-Invest and Flexible
The growing speed of innovation in related industries requires the automotive industry to adapt and increase release frequencies of new vehicle derivatives which implies a significant reduction of investments per vehicle and ramp-up times. Emerging markets in various parts of the world augment the currently dominating established main automotive markets. Local content requirements such as import tariffs on final products impede the accessibility of these micro markets, which is why in the future market exploitation will not be driven by pure sales activities anymore but rather by setting up local assembly units. The aim of this paper is to provide an overview of the concept of decentralized assembly and to discuss and critically assess some currently researched and crucial approaches in production technology. In order to determine the scope in which complementary mobile assembly can be profitable for manufacturers, a general cost model is set up and each cost driver is assessed with respect to varying levels of decentralization. One main result of the paper is that the presented approaches offer huge cost-saving potentials and are thus critical for future production strategies. Nevertheless, they still need to be further exploited in order for decentralized assembly to be profitable for companies. The optimal level of decentralization must, however, be specifically determined in each case and cannot be defined in general.
Offline Parameter Identification and State-of-Charge Estimation for Healthy and Aged Electric Vehicle Batteries Based on the Combined Model
Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.
A Strategic Sustainability Analysis of Electric Vehicles in EU Today and Towards 2050
Ambitions within the EU for moving towards sustainable transport include major emission reductions for fossil fuel road vehicles, especially for buses, trucks, and cars. The electric driveline seems to be an attractive solution for such development. This study first applied the Framework for Strategic Sustainable Development to compare sustainability effects of today’s fossil fuel vehicles with electric vehicles that have batteries or hydrogen fuel cells. The study then addressed a scenario were electric vehicles might be in majority in Europe by 2050. The methodology called Strategic Lifecycle Assessment was first used, were each life cycle phase was assessed for violations against sustainability principles. This indicates where further analysis could be done in order to quantify the magnitude of each violation, and later to create alternative strategies and actions that lead towards sustainability. A Life Cycle Assessment of combustion engine cars, plug-in hybrid cars, battery electric cars and hydrogen fuel cell cars was then conducted to compare and quantify environmental impacts. The authors found major violations of sustainability principles like use of fossil fuels, which contribute to the increase of emission related impacts such as climate change, acidification, eutrophication, ozone depletion, and particulate matters. Other violations were found, such as use of scarce materials for batteries and fuel cells, and also for most life cycle phases for all vehicles when using fossil fuel vehicles for mining, production and transport. Still, the studied current battery and hydrogen fuel cell cars have less severe violations than fossil fuel cars. The life cycle assessment revealed that fossil fuel cars have overall considerably higher environmental impacts compared to electric cars as long as the latter are powered by renewable electricity. By 2050, there will likely be even more sustainable alternatives than the studied electric vehicles when the EU electricity mix mainly should stem from renewable sources, batteries should be recycled, fuel cells should be a mature technology for use in vehicles (containing no scarce materials), and electric drivelines should have replaced combustion engines in other sectors. An uncertainty for fuel cells in 2050 is whether the production of hydrogen will have had time to switch to renewable resources. If so, that would contribute even more to a sustainable development. Except for being adopted in the GreenCharge roadmap, the authors suggest that the results can contribute to planning in the upcoming decades for a sustainable increase of EVs in Europe, and potentially serve as an inspiration for other smaller or larger regions. Further studies could map the environmental effects in LCA further, and include other road vehicles to get a more precise perception of how much they could affect sustainable development.
Influence of Driving Strategy on Power and Fuel Consumption of Lightweight PEM Fuel Cell Vehicle Powertrain
In this paper, a prototype PEM fuel cell vehicle
integrated with a 1 kW air-blowing proton exchange membrane fuel
cell (PEMFC) stack as a main power sources has been developed for
a lightweight cruising vehicle. The test vehicle is equipped with a
PEM fuel cell system that provides electric power to a brushed DC
motor. This vehicle was designed to compete with industrial
lightweight vehicle with the target of consuming least amount of
energy and high performance. Individual variations in driving style
have a significant impact on vehicle energy efficiency and it is well
established from the literature. The primary aim of this study was to
assesses the power and fuel consumption of a hydrogen fuel cell
vehicle operating at three difference driving technique (i.e. 25 km/h
constant speed, 22-28 km/h speed range, 20-30 km/h speed range).
The goal is to develop the best driving strategy to maximize
performance and minimize fuel consumption for the vehicle system.
The relationship between power demand and hydrogen consumption
has also been discussed. All the techniques can be evaluated and
compared on broadly similar terms. Automatic intelligent controller
for driving prototype fuel cell vehicle on different obstacle while
maintaining all systems at maximum efficiency was used. The result
showed that 25 km/h constant speed was identified for optimal
driving with less fuel consumption.
Design and Control Algorithms for Power Electronic Converters for EV Applications
The power electronic components within Electric Vehicles (EV) need to operate in several important modes. Some modes directly influence safety, while others influence vehicle performance. Given the variety of functions and operational modes required of the power electronics, it needs to meet efficiency requirements to minimize power losses. Another challenge in the control and construction of such systems is the ability to support bidirectional power flow. This paper considers the construction, operation, and feasibility of available converters for electric vehicles with feasible configurations of electrical buses and loads. This paper describes logic and control signals for the converters for different operations conditions based on the efficiency and energy usage bases.
MP-SMC-I Method for Slip Suppression of Electric Vehicles under Braking
In this paper, a new SMC (Sliding Mode Control)
method with MP (Model Predictive Control) integral action for the
slip suppression of EV (Electric Vehicle) under braking is proposed.
The proposed method introduce the integral term with standard SMC
gain , where the integral gain is optimized for each control period by
the MPC algorithms. The aim of this method is to improve the safety
and the stability of EVs under braking by controlling the wheel slip
ratio. There also include numerical simulation results to demonstrate
the effectiveness of the method.
Optimal Allocation of PHEV Parking Lots to Minimize Distribution System Losses
To tackle the air pollution issues, Plug-in Hybrid
Electric Vehicles (PHEVs) are proposed as an appropriate solution.
Charging a large amount of PHEV batteries, if not controlled, would
have negative impacts on the distribution system. The control process
of charging of these vehicles can be centralized in parking lots that
may provide a chance for better coordination than the individual
charging in houses. In this paper, an optimization-based approach is
proposed to determine the optimum PHEV parking capacities in
candidate nodes of the distribution system. In so doing, a profile for
charging and discharging of PHEVs is developed in order to flatten
the network load profile. Then, this profile is used in solving an
optimization problem to minimize the distribution system losses. The
outputs of the proposed method are the proper place for PHEV
parking lots and optimum capacity for each parking. The application
of the proposed method on the IEEE-34 node test feeder verifies the
effectiveness of the method.
Providing Energy Management of a Fuel Cell-Battery Hybrid Electric Vehicle
On account of the concern of the fossil fuel is
depleting and its negative effects on the environment, interest in
alternative energy sources is increasing day by day. However,
considering the importance of transportation in human life, instead of
oil and its derivatives fueled vehicles with internal combustion
engines, electric vehicles which are sensitive to the environment and
working with electrical energy has begun to develop. In this study,
simulation was carried out for providing energy management and
recovering regenerative braking in fuel cell-battery hybrid electric
vehicle. The main power supply of the vehicle is fuel cell on the other
hand not only instantaneous power is supplied by the battery but also
the energy generated due to regenerative breaking is stored in the
battery. Obtained results of the simulation is analyzed and discussed.
Control Strategy for Two-Mode Hybrid Electric Vehicle by Using Fuzzy Controller
Hybrid electric vehicles can reduce pollution and
improve fuel economy. Power-split hybrid electric vehicles (HEVs)
provide two power paths between the internal combustion engine
(ICE) and energy storage system (ESS) through the gears of an
electrically variable transmission (EVT). EVT allows ICE to operate
independently from vehicle speed all the time. Therefore, the ICE can
operate in the efficient region of its characteristic brake specific fuel
consumption (BSFC) map. The two-mode powertrain can operate in
input-split or compound-split EVT modes and in four different fixed
gear configurations. Power-split architecture is advantageous because
it combines conventional series and parallel power paths. This
research focuses on input-split and compound-split modes in the
two-mode power-split powertrain. Fuzzy Logic Control (FLC) for an
internal combustion engine (ICE) and PI control for electric machines
(EMs) are derived for the urban driving cycle simulation. These
control algorithms reduce vehicle fuel consumption and improve ICE
efficiency while maintaining the state of charge (SOC) of the energy
storage system in an efficient range.
Vehicle Routing Problem with Mixed Fleet of Conventional and Heterogenous Electric Vehicles and Time Dependent Charging Costs
In this paper, we consider the vehicle routing problem
with mixed fleet of conventional and heterogenous electric vehicles
and time dependent charging costs, denoted VRP-HFCC, in which
a set of geographically scattered customers have to be served by a
mixed fleet of vehicles composed of a heterogenous fleet of Electric
Vehicles (EVs), having different battery capacities and operating
costs, and Conventional Vehicles (CVs). We include the possibility
of charging EVs in the available charging stations during the routes
in order to serve all customers. Each charging station offers charging
service with a known technology of chargers and time dependent
charging costs. Charging stations are also subject to operating time
windows constraints. EVs are not necessarily compatible with all
available charging technologies and a partial charging is allowed.
Intermittent charging at the depot is also allowed provided that
constraints related to the electricity grid are satisfied.
The objective is to minimize the number of employed vehicles and
then minimize the total travel and charging costs.
In this study, we present a Mixed Integer Programming Model and
develop a Charging Routing Heuristic and a Local Search Heuristic
based on the Inject-Eject routine with different insertion methods. All
heuristics are tested on real data instances.
Feasibility and Penetration of Electric Vehicles in Indian Power Grid
As the current status and growth of Indian automobile
industry is remarkable, transportation sectors are the main concern in
terms of energy security and climate change. Due to rising demand of
fuel and its dependency on foreign countries that affects the GDP of
nation, suggests that penetration of electrical vehicle will increase in
near future. So in this context analysis is done if the 10 percent of
conventional vehicles including cars, three wheelers and two
wheelers becomes electrical vehicles in near future which is also a
part of Nations Electric Mobility Mission Plan then the saving which
improves the nation’s economy is analyzed in detail. Whether the
Indian electricity grid is capable of taking this load with current
generation and demand all over the country is also analyzed in detail.
Current situation of Indian grid is analyzed and how the gap between
generation and demand can be reduced is discussed in terms of
increasing generation capacity and energy conservation measures.
Electrical energy conservation measures in Industry and especially in
rural areas have been analyzed to improve performance of Indian
electricity grid in context of electrical vehicle penetration in near
future. Author was a part of Vishvakarma yojna in which energy
losses were measured in 255 villages of Gujarat and solutions were
suggested to mitigate them and corresponding reports was submitted
to the authorities of Gujarat government.
Advanced Simulation of Power Consumption of Electric Vehicles
Electric vehicles are one of the most complicated
electric devices to simulate due to the significant number of different
processes involved in electrical structure of it. There are concurrent
processes of energy consumption and generation with different
onboard systems, which make simulation tasks more complicated to
perform. More accurate simulation on energy consumption can
provide a better understanding of all energy management for electric
transport. As a result of all those processes, electric transport can
allow for a more sustainable future and become more convenient in
relation to the distance range and recharging time. This paper
discusses the problems of energy consumption simulations for
electric vehicles using different software packages to provide ideas
on how to make this process more precise, which can help engineers
create better energy management strategies for electric vehicles.
Modeling and Simulation of Standalone Photovoltaic Charging Stations for Electric Vehicles
Batteries of electric vehicles (BEV) are becoming
more attractive with the advancement of new battery technologies
and promotion of electric vehicles. BEV batteries are recharged on
board vehicles using either the grid (G2V for Grid to Vehicle) or
renewable energies in a stand-alone application (H2V for Home to
Vehicle). This paper deals with the modeling, sizing and control of a
photovoltaic stand-alone application that can charge the BEV at
home. The modeling approach and developed mathematical models
describing the system components are detailed. Simulation and
experimental results are presented and commented.
A Novel Design Methodology for a 1.5 KW DC/DC Converter in EV and Hybrid EV Applications
This paper presents a method for the efficient
implementation of a unidirectional or bidirectional DC/DC converter.
The DC/DC converter is used essentially for energy exchange
between the low voltage service battery and a high voltage battery
commonly found in Electric Vehicle applications. In these
applications, apart from cost, efficiency of design is an important
characteristic. A useful way to reduce the size of electronic
equipment in the electric vehicles is proposed in this paper. The
technique simplifies the mechanical complexity and maximizes the
energy usage using the latest converter control techniques. Moreover
a bidirectional battery charger for hybrid electric vehicles is also
implemented in this paper. Several simulations on the test system
have been carried out in Matlab/Simulink environment. The results
exemplify the robustness of the proposed design methodology in case
of a 1.5 KW DC-DC converter.
Energy Management System in HEV Using PI Controller
Nowadays the use of Hybrid Electric Vehicles (HEV) is increasing dramatically. The HEV is mainly dependent on electricity and there is always a need for storage of charge. Fuel Cell (FC), Batteries and Ultra Capacitor are being used for the proposed HEV as an electric power source or as an energy storage unit. The aim of developing an energy management technique is to utilize the sources according to the requirement of the vehicle with help of controller. This increases the efficiency of hybrid electric vehicle to reduce the fuel consumption and unwanted emission. The Maximum Power Point Tracking (MPPT) in FC is done using (Perturb & Observe) algorithm. In this paper, the control of automobiles at variable speed is achieved effectively.
FEA-Based Calculation of Performances of IPM Machines with Five Topologies for Hybrid- Electric Vehicle Traction
The paper presents a detailed calculation of characteristic of five different topology permanent magnet machines for high performance traction including hybrid -electric vehicles using finite element analysis (FEA) method. These machines include V-shape single layer interior PM, W-shape single-layer interior PM, Segment interior PM and surface PM on the rotor and with distributed winding on the stator. The performance characteristics which include the back-emf voltage and its harmonic, magnet mass, iron loss and ripple torque are compared and analyzed. One of a 7.5kW IPM prototype was tested and verified finite-element analysis results. The aim of the paper is given some guidance and reference for machine designer which are interested in IPM machine selection for high performance traction application.
Design of an Experimental Setup to Study the Drives of Battery Electric Vehicles
This paper describes the design considerations of an
experimental setup for research and exploring the drives of batteryfed
electric vehicles. Effective setup composition and its components
are discussed. With experimental setup described in this paper,
durability and functional tests can be procured to the customers.
Multiple experiments are performed in the form of steady-state
system exploring, acceleration programs, multi-step tests (speed
control, torque control), load collectives or close-to-reality driving
tests (driving simulation). Main focus of the functional testing is on
the measurements of power and energy efficiency and investigations
in driving simulation mode, which are used for application purposes.
In order to enable the examination of the drive trains beyond
standard modes of operation, different other parameters can be