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Volume 9
Issue 4
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World Electr. Veh. J., Volume 9, Issue 4 (December 2018) – 7 articles

Cover Story (view full-size image): With the exponential growth of EV charging infrastructure, one tends to forget the lessons that can be learned from sectors such as telecom and internet. They have already gone through a learning curve when it comes to developing the boundary conditions for an efficient and international market, as well as providing a flawless user experience in e.g. cross-border pricing and billing. We translate lessons learned to e-mobility and show how independent protocols such as OCPI will play a crucial role in moving towards an integrated EU-wide charging infrastructure. View this paper.
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15 pages, 3220 KiB  
Article
Reducing Mobile Air Conditioner (MAC) Power Consumption Using Active Cabin-Air-Recirculation in A Plug-In Hybrid Electric Vehicle (PHEV)
by Chengguo Li, Eli Brewer, Liem Pham and Heejung Jung
World Electr. Veh. J. 2018, 9(4), 51; https://doi.org/10.3390/wevj9040051 - 19 Dec 2018
Cited by 33 | Viewed by 6681
Abstract
Air conditioner power consumption accounts for a large fraction of the total power used by hybrid and electric vehicles. This study examined the effects of three different cabin air ventilation settings on mobile air conditioner (MAC) power consumption, such as fresh mode with [...] Read more.
Air conditioner power consumption accounts for a large fraction of the total power used by hybrid and electric vehicles. This study examined the effects of three different cabin air ventilation settings on mobile air conditioner (MAC) power consumption, such as fresh mode with air conditioner on (ACF), fresh mode with air conditioner off (ACO), and air recirculation mode with air conditioner on (ACR). Tests were carried out for both indoor chassis dynamometer and on-road tests using a 2012 Toyota Prius plug-in hybrid electric vehicle. Real-time power consumption and fuel economy were calculated from On-Board Diagnostic-II (OBD-II) data and compared with results from the carbon balance method. MAC consumed 28.4% of the total vehicle power in ACR mode when tested with the Supplemental Federal Test Procedure (SFTP) SC03 driving cycle on the dynamometer, which was 6.1% less than in ACF mode. On the other hand, ACR and ACF mode did not show significant differences for the less aggressive on-road tests. This is likely due to the significantly lower driving loads experienced in the local driving route compared to the SC03 driving cycle. On-road and SC03 test results suggested that more aggressive driving tends to magnify the effects of the vehicle HVAC (heating, ventilation, and air conditioning) system settings. ACR conditions improved relative fuel economy (or vehicle energy efficiency) to that of ACO conditions by ~20% and ~8% compared to ACF conditions for SC03 and on-road tests, respectively. Furthermore, vehicle cabin air quality was measured and analyzed for the on-road tests. ACR conditions significantly reduced in-cabin particle concentrations, in terms of aerosol diffusion charger signal, by 92% compared to outside ambient conditions. These results indicate that cabin air recirculation is a promising method to improve vehicle fuel economy and improve cabin air quality. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>SC03 cycle speed plot.</p> Full article ">Figure 2
<p>On-road route (3.4 miles) for tests in charge depletion mode.</p> Full article ">Figure 3
<p>Average speed plot of the on-road route.</p> Full article ">Figure 4
<p>Comparison of fuel consumption between carbon balance and OBD-II methods over the SC03 cycle.</p> Full article ">Figure 5
<p>AC power consumption (%) in relation to total power consumption of on-road and SC03 drive cycles.</p> Full article ">Figure 6
<p>Fuel Economy calculated from the OBD-II data for all tests.</p> Full article ">Figure 7
<p>Difference in fuel economy relative to average ACO conditions result.</p> Full article ">Figure 8
<p>Normalized frequency plot of particle surface area concentrations, corresponding to the diffusion charger signal, during the highway test. The ambient condition was measured with two front windows fully opened.</p> Full article ">
15 pages, 2178 KiB  
Communication
Advancing E-Roaming in Europe: Towards a Single “Language” for the European Charging Infrastructure
by Roland Ferwerda, Michel Bayings, Mart Van der Kam and Rudi Bekkers
World Electr. Veh. J. 2018, 9(4), 50; https://doi.org/10.3390/wevj9040050 - 7 Dec 2018
Cited by 22 | Viewed by 12767
Abstract
The E.U. market for electric vehicles (EVs) is growing significantly, but the absence of widely adopted protocols and interoperability standards for charging hinders the development of cross-border EV travel (“e-roaming”). In this paper, we present our vision on what should be the basic [...] Read more.
The E.U. market for electric vehicles (EVs) is growing significantly, but the absence of widely adopted protocols and interoperability standards for charging hinders the development of cross-border EV travel (“e-roaming”). In this paper, we present our vision on what should be the basic functionalities of e-roaming. Furthermore, we describe the best practices of 6 years of e-roaming in the Netherlands, and analyze what can be learned from other sectors that were successful in introducing roaming mechanisms in the past. We translate these into proposed next steps, such as the need for piloting e-roaming on a European level using open standards, such as Open Charge Point Interface (OCPI). We conclude with a proposal for a comparative study of protocols to pave the way for future convergence, and, thus, facilitate a European market for EV products and services. Full article
(This article belongs to the Special Issue Selected Papers from The 30th International Electric Vehicles Symposium and Exhibition (Stuttgart, Germany))
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Figure 1
<p>Current market situation, Scenario 1. Peer-to-peer only. Different colours indicate different protocols, and the red arrows show when a user cannot charge at stations of that specific charge point operator. EV, electric vehicle.</p> Full article ">Figure 2
<p>Current market situation, Scenario 2. A combination of peer-to-peer and roaming; two roaming hubs. Different colours indicate different protocols, and the red arrows show when a user cannot charge at stations of that specific charge point operator.</p> Full article ">Figure 3
<p>Future market situation, Scenario 3. A combination of peer-to-peer and roaming; one roaming hub. Only a single protocol is used.</p> Full article ">Figure 4
<p>Future market situation, Scenario 4. A combination of peer-to-peer and roaming; two roaming hubs. Only a single protocol is used.</p> Full article ">
10 pages, 1539 KiB  
Article
Shell Analysis and Optimisation of a Pure Electric Vehicle Power Train Based on Multiple Software
by Shaocui Guo, Xiangrong Tong and Xu Yang
World Electr. Veh. J. 2018, 9(4), 49; https://doi.org/10.3390/wevj9040049 - 5 Dec 2018
Viewed by 3271
Abstract
Motor end cover mounting fracture is a problem recently encountered by novel pure electric vehicles. Regarding the study of the traditional vehicle engine mount bracket and on the basis of the methods of design and optimisation available, we have analysed and optimised the [...] Read more.
Motor end cover mounting fracture is a problem recently encountered by novel pure electric vehicles. Regarding the study of the traditional vehicle engine mount bracket and on the basis of the methods of design and optimisation available, we have analysed and optimised the pure electric vehicle end cover mount system. Multi-body dynamic software and finite element software have been combined. First, we highlight the motor end cover mount bracket fracture engineering problems, analyse the factors that may produce fracture, and propose solutions. By using CATIA software to establish a 3D model of the power train mount system, we imported it into ADAMS multi-body dynamic software, conducted 26 condition analysis, obtained five ultimate load conditions, and laid the foundations for subsequent analysis. Next, a mount and shell system was established by the ANSYS finite element method, and modal, strength, and fatigue analyses were performed on the end cover mount. We found that the reason for fracture lies in the intensity of the end cover mount joint, which leads to the safety factor too small and the fatigue life not being up to standard. The main goal was to increase the strength of the cover mount junction, stiffness, safety coefficient, and fatigue life. With this aim, a topology optimisation was conducted to improve the motor end cover. A 3D prototype was designed accordingly. Finally, stiffness, strength, modal, and fatigue were simulated. Our simulation results were as follows. The motor end cover suspension stiffness increases by 20%, the modal frequency increases by 2.3%, the quality increases by 3%, the biggest deformation decreases by 52%, the maximum stress decreases by 28%, the minimum safety factor increases by 40%, and life expectancy increases 50-fold. The results from sample and vehicle tests highlight that the component fracture problem has been successfully solved and the fatigue life dramatically improved. Full article
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<p>Power train shell crack. (<b>a</b>) Before cracking; (<b>b</b>) after cracking.</p> Full article ">Figure 2
<p>Power train mounting system by ADAMS.</p> Full article ">Figure 3
<p>Stress and strain under extreme conditions. (<b>a</b>) Working Condition 1 strain; (<b>b</b>) Working condition 2 strain; (<b>c</b>) Working Condition 3 strain; (<b>d</b>) Working condition 4 strain; (<b>e</b>) Working condition 5 strain.</p> Full article ">Figure 4
<p>CAD and FEM model of middle product: (<b>a</b>) CAD model of side A (<b>b</b>) CAD model of side B (<b>c</b>) FEM model of side A (<b>d</b>) FEM model of side B.</p> Full article ">Figure 5
<p>CAD and FEM model of the optimised product. (<b>a</b>) FEM model of side A (<b>b</b>) FEM model of side B (<b>c</b>) CAD model of side A (<b>d</b>) CAD model of side B.</p> Full article ">
15 pages, 3604 KiB  
Article
Scube—Concept and Implementation of a Self-balancing, Autonomous Mobility Device for Personal Transport
by Manfred Klöppel, Felix Römer, Michael Wittmann, Bijan Hatam, Thomas Herrmann, Lee Leng Sim, Jun Siang Douglas Lim, Yunfan Lu, Vladimir Medovy, Lukas Merkle, Wy Xin Richmond Ten, Aybike Ongel, Yan Jack Jeffrey Hong, Heong Wah Ng and Markus Lienkamp
World Electr. Veh. J. 2018, 9(4), 48; https://doi.org/10.3390/wevj9040048 - 5 Dec 2018
Cited by 2 | Viewed by 5902
Abstract
Public transportation (PT) systems suffer from disutility compared to private transportation due to the inability to provide passengers with a door-to-door service, referred to as the first/last mile problem. Personal mobility devices (PMDs) are thought to improve PT service quality by closing this [...] Read more.
Public transportation (PT) systems suffer from disutility compared to private transportation due to the inability to provide passengers with a door-to-door service, referred to as the first/last mile problem. Personal mobility devices (PMDs) are thought to improve PT service quality by closing this first/last mile gap. However, current PMDs are generally driven manually by the rider and require a learning phase for safe vehicle operation. Additionally, most PMDs require a standing riding position and are not easily accessible to elderly people or persons with disabilities. In this paper, the concept of an autonomously operating mobility device is introduced. The visionary concept is designed as an on-demand transportation service which transports people for short to medium distances and increases the accessibility to public transport. The device is envisioned to be operated as a larger fleet and does not belong to an individual person. The vehicle features an electric powertrain and a one-axle self-balancing design with a small footprint. It provides one seat for a passenger and a tilt mechanism that is designed to improve the ride comfort and safety at horizontal curves. An affordable 3D-camera system is used for autonomous localization and navigation. For the evaluation and demonstration of the concept, a functional prototype is implemented. Full article
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<p>Examples of various PMDs (Personal mobility devices): (<b>a</b>) Segway i2 Personal Transporter; (<b>b</b>) Scewo Bro [<a href="#B12-wevj-09-00048" class="html-bibr">12</a>]; (<b>c</b>) Airwheel X8; (<b>d</b>) Zoom Stryder EX [<a href="#B11-wevj-09-00048" class="html-bibr">11</a>].</p> Full article ">Figure 2
<p>Body measurements according to reference [<a href="#B14-wevj-09-00048" class="html-bibr">14</a>,<a href="#B15-wevj-09-00048" class="html-bibr">15</a>].</p> Full article ">Figure 3
<p>Display of the suspension and actuator system.</p> Full article ">Figure 4
<p>Abstract model of the vehicle for the calculations of occurring forces on the actuators.</p> Full article ">Figure 5
<p>Overview of the system components.</p> Full article ">Figure 6
<p>(<b>a</b>) Localization by recognition of landmarks; (<b>b</b>) Calculation of heading by the pinhole-camera model.</p> Full article ">Figure 7
<p>(<b>a</b>) Mainframe of Scube; (<b>b</b>) Scube with attached shell.</p> Full article ">Figure 8
<p>Exterior dimensions of the vehicle.</p> Full article ">Figure 9
<p>Simulation of occurred acceleration during a defined turn (<b>a</b>) without tilting (<b>b</b>) and with tilting (<b>c</b>).</p> Full article ">Figure 10
<p>Distribution of localisation measurements using multiple landmarks.</p> Full article ">Figure 11
<p>(<b>a</b>) Comparison of the predefined driving route (black) with data recorded by onboard odometry (blue); (<b>b</b>) Recorded heading during travel over the predefined route.</p> Full article ">Figure 12
<p>(<b>a</b>) Overview of the testing area and the test track; (<b>b</b>) Enlarged detail of the planned track (black dotted line), planned waypoints (black crosses), start position (green cross), destination (orange cross), internally computed track (blue line), computed destination position (blue cross), manually measured final position (red cross).</p> Full article ">
20 pages, 2940 KiB  
Article
Combination of LiCs and EDLCs with Batteries: A New Paradigm of Hybrid Energy Storage for Application in EVs
by Immanuel N. Jiya, Nicoloy Gurusinghe and Rupert Gouws
World Electr. Veh. J. 2018, 9(4), 47; https://doi.org/10.3390/wevj9040047 - 19 Nov 2018
Cited by 8 | Viewed by 5827
Abstract
The research presented in this paper proposes a hybrid energy storage system that combines both electrolytic double-layer capacitors (EDLCs) also known as supercapacitors (SCs) and lithium-ion capacitors (LiCs) also known as hybrid capacitors (HCs) with a battery through a multiple input converter. The [...] Read more.
The research presented in this paper proposes a hybrid energy storage system that combines both electrolytic double-layer capacitors (EDLCs) also known as supercapacitors (SCs) and lithium-ion capacitors (LiCs) also known as hybrid capacitors (HCs) with a battery through a multiple input converter. The proposal was verified in simulation and validated by implementing a laboratory prototype. A new hybridisation topology, which reduces the amount of resource requirement when compared to the conventional hybridisation topology, is introduced. An electric vehicle (EV) current profile from previous research was used to test the performance of the proposed topology. From the results obtained, the hybridisation topology proposed in this research had the lowest cost per unit power at 14.81 $/kW, the lowest cost per unit power to energy, and available power to energy ratio, both at 1:1.3, thus making it a more attractive hybridisation topology than the two conventional alternatives. The multiple input converter built had efficiency values in excess of 80%. The key take away from this paper is that using the proposed hybridisation topology, the battery is less often required to supply energy to the electric vehicle, and so, its cycle life is preserved. Furthermore, since the battery is not used for the repeated acceleration and deceleration in the entire driving cycle, the battery’s cycle life is further preserved. Furthermore, since the battery is not the only storage device in the energy storage system, it can be further downsized to best fit the required base load; therefore, leading to a more optimized energy storage system by reducing the weight and volume of space occupied by the energy storage system, while also achieving better efficiencies. Full article
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<p>Overview of the concept design, highlighting the major focus areas of this research.</p> Full article ">Figure 2
<p>Comparison of the physical sizes of the supercapacitor (SC) and hybrid capacitor (HC) banks.</p> Full article ">Figure 3
<p>Normalized comparison of the normalized characteristics of the energy storage regimes.</p> Full article ">Figure 4
<p>The implementation circuit of (<b>a</b>) the conventional hybridisation topology using a three-input converter; (<b>b</b>) the proposed hybridisation topology having SC and HC directly connected in parallel; and (<b>c</b>) the experimental implementation of the converter high side and low side switches with the MOSFET driver.</p> Full article ">Figure 5
<p>Steady-state waveforms of the operation of the multiple input converter for the proposed energy storage hybridisation topology.</p> Full article ">Figure 6
<p>The experimental validation of the battery showing the charging and discharging curves under a 2-A current source and load, respectively.</p> Full article ">Figure 7
<p>The experimental validation of the charging of (<b>a</b>) HC and (<b>b</b>) SC under a 3-A source.</p> Full article ">Figure 8
<p>The experimental validation of the SC and HC banks showing the discharging curves under a 3-A and a 12-A load.</p> Full article ">Figure 9
<p>The experimental transient response of the SC bank, HC bank, and parallel arrangement of the SC and HC bank to a constant load current of 4.5 A rising to a pulse of 10 A with a rise time of 1 µs.</p> Full article ">Figure 10
<p>Performance of the experimental SC and HC when directly connected in parallel to a realistic electronic EV load profile.</p> Full article ">Figure 11
<p>Scope results showing the inductor <span class="html-italic">L</span><sub>1</sub> current and voltage alongside the associated switching signals of the MOSFETs for operating the multiple input converter in Mode A at (<b>a</b>) 30% duty and (<b>b</b>) 75% duty.</p> Full article ">Figure 12
<p>Scope results showing the inductor <span class="html-italic">L</span><sub>2</sub> current and voltage alongside the associated switching signals of the MOSFETs for operating the multiple input converter in Mode B at (<b>a</b>) 30% duty and (<b>b</b>) 75% duty.</p> Full article ">Figure 13
<p>The efficiency results of the multiple input converter operating in Modes A and B (<b>a</b>) under varying duty cycles at a constant load and (<b>b</b>) under varying load currents at a constant output voltage.</p> Full article ">Figure 14
<p>Experimental result of the hybridisation topology with SC and HC connected directly in parallel and hybridised through one port; a test using a realistic EV profile.</p> Full article ">Figure 15
<p>Validation of the converter output voltage for its operation in Mode A (having an input voltage of 5 V) and Mode B (having an input voltage of 10 V) against the simulation results and analytical calculations.</p> Full article ">
14 pages, 3288 KiB  
Article
Scaling Trends of Electric Vehicle Performance: Driving Range, Fuel Economy, Peak Power Output, and Temperature Effect
by Heejung Jung, Rebecca Silva and Michael Han
World Electr. Veh. J. 2018, 9(4), 46; https://doi.org/10.3390/wevj9040046 - 9 Nov 2018
Cited by 33 | Viewed by 13513
Abstract
This study investigated scaling trends of commercially available light-duty battery electric vehicles (BEVs) ranging from model year 2011 to 2018. The motivation of this study is to characterize the status of BEV technology with respect to BEV performance parameters to better understand the [...] Read more.
This study investigated scaling trends of commercially available light-duty battery electric vehicles (BEVs) ranging from model year 2011 to 2018. The motivation of this study is to characterize the status of BEV technology with respect to BEV performance parameters to better understand the limitations and potentials of BEV. The raw data was extracted from three main sources: INL (Idaho National Laboratory) website, EPA (Environmental Protection Agency) Fuel Economy website, and the websites BEV manufacturers and internet in general. Excellent scaling trends were found between the EPA driving range per full charge of a battery and the battery capacity normalized by vehicle weight. In addition, a relatively strong correlation was found between EPA city fuel economy and vehicle curb weight, while a weak correlation was found between EPA highway fuel economy and vehicle curb weight. An inverse power correlation was found between 0–60 mph acceleration time and peak power output from battery divided by vehicle curb weight for 10 BEVs investigated at INL. Tests done on the environmentally controlled chamber chassis dynamometer at INL show that fuel economy drops by 19 ± 5% for the summer driving condition with air conditioner on and 47 ± 7% for the winter driving condition. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Scaling trend of EPA driving range (miles) per charge vs. battery capacity/vehicle curb weight (kWh/kg). Blue represents Tesla vehicles, red represents non-Tesla vehicles, circle represents short-range BEVs, and triangle represents long-range BEVs.</p> Full article ">Figure 2
<p>Scaling trend of EPA city (MPGe) fuel economy with vehicle curb weight (kg). Blue represents Tesla vehicles, red represents non-Tesla vehicles, circle represents short-range BEVs, and triangle represents long-range BEVs.</p> Full article ">Figure 3
<p>Scaling trend of EPA highway fuel economy (MPGe) with vehicle curb weight (kg). Blue represents Tesla vehicles, red represents non-Tesla vehicles, circle represents short-range BEV, and triangle represents long-range BEV.</p> Full article ">Figure 4
<p>Scaling trend of EPA combined fuel economy (MPGe) with vehicle curb weight (kg). Blue represents Tesla vehicles, red represents non-Tesla vehicles, circle represents short-range BEV, and triangle represents long-range BEV.</p> Full article ">Figure 5
<p>Correlation between EPA city mileage and EPA highway mileage for light duty BEVs.</p> Full article ">Figure 6
<p>Acceleration for 0–60 mph (s) as a function of peak power from battery normalized by vehicle curb weight.</p> Full article ">Figure 7
<p>Battery capacity over battery weight relationship.</p> Full article ">Figure 8
<p>Effect of ambient conditions on BEV fuel economy. Yellow and green lines represent average values for MPGe at 95 F with solar load and 20 F respectively. The fuel economy was over the UDDS cycle.</p> Full article ">
18 pages, 5654 KiB  
Article
Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle
by Nicolas Sockeel, Jian Shi, Masood Shahverdi and Michael Mazzola
World Electr. Veh. J. 2018, 9(4), 45; https://doi.org/10.3390/wevj9040045 - 6 Nov 2018
Cited by 9 | Viewed by 4364
Abstract
Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, [...] Read more.
Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, a model predictive control (MPC) has been considered as PMS. This control design has been defined as an optimization problem that uses the projected system behaviors over a finite prediction horizon to determine the optimal control solution for the current time instant. In this manuscript, the MPC controller intents to diminish simultaneously the battery aging and the equivalent fuel consumption. The main contribution of this manuscript is to evaluate numerically the impacts of the vehicle battery model on the MPC optimal control solution when the plug hybrid electric vehicle (PHEV) is in the battery charge sustaining mode. Results show that the higher fidelity model improves the capability of accurately predicting the battery aging. Full article
(This article belongs to the Special Issue Integrated Thermal Management Architecture, Modeling and Simulation for Control of EVs)
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<p>Picture of the car of the future plug-in series hybrid electric vehicle.</p> Full article ">Figure 2
<p>Series PHEV block diagram of the Subaru BRZ 2015 Urban Dynamometer Driving Schedule (UDDS).</p> Full article ">Figure 3
<p>Model predictive control based power management system.</p> Full article ">Figure 4
<p>Control loop for the sensitivity analysis.</p> Full article ">Figure 5
<p>Picture of the real battery pack assembly.</p> Full article ">Figure 6
<p>Impedance model of the battery.</p> Full article ">Figure 7
<p>Error between the SoC computed by the model predictive control (MPC) model and the reference vehicle during two Urba n Dynamometer Driving Schedule (UDDS, <b>up</b>) and Highway Fuel Economy Test (HWFET, <b>down</b>) drive cycles.</p> Full article ">Figure 8
<p>Error between the fuel consumption computed by the MPC model and the reference vehicle during two UDDS (<b>up</b>) and HWFET (<b>down</b>) drive cycles.</p> Full article ">Figure 9
<p>Error between the battery capacity fade computed by the MPC model and the reference vehicle during 2 UDDS (<b>up</b>) and HWFET (<b>down</b>) drive cycles.</p> Full article ">Figure 10
<p>Error between the objective function computed by the MPC model and the reference vehicle during two UDDS (<b>up</b>) and HWFET (<b>down</b>) drive cycles.</p> Full article ">Figure 11
<p>Fuel consumption comparison at the end of the two UDDS (<b>up</b>) and HWFET (<b>down</b>) drive cycles.</p> Full article ">Figure 12
<p>Battery aging comparison at the end of the two UDDS (<b>up</b>) and HWFET (<b>down</b>) drive cycles.</p> Full article ">Figure 13
<p>Objective function comparison at the end of the two UDDS (<b>up</b>) and HWFET (<b>down</b>) drive cycles.</p> Full article ">
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