Indirect Carbon Emissions and Energy Consumption Model for Electric Vehicles: Indian Scenario

The environment-friendly nature of E-vehicles (electric vehicles) coupled with higher energy efficiency has increased their popularity in the automotive industry. A detailed study has been conducted in this article to evaluate the role of the energy mix for electricity generation at the charging locations in secondary C emissions from E-vehicles. The E-vehicle market is booming in India. Evaluation of indirect C emissions was conducted for 3 energy mix scenarios in India, and the results showed that in the present energy mix scenario, E-vehicle emissions will be more than that of conventional-fuel-based vehicles. An energy consumption model for the E-vehicle was also developed in this article using MATLAB Simulink, by considering road slope and driving conditions as input parameters. The developed model was tested for 3 driving conditions, namely (i) Flat road at a constant speed, (ii) Extra Urban Driving Cycle (EUDC), and (iii) Real-time driving condition, to estimate the relation between the energy consumption pattern and the driving range with road slope. Simulation results showed variation in the driving range of the E-vehicles regarding input parameters like road slope and vehicle speed. Therefore, this model could serve as an effective tool for establishing charging stations at strategic locations. Integr Environ Assess Manag 2020;16:998-1007. © 2020 SETAC.

Construction of electric vehicle driving cycle for studying electric vehicle energy consumption and equivalent emissions

This paper proposes an effective and scientific method for the construction of a representative driving cycle for electric vehicles (EV) and takes it as a foundation for studying the energy consumption and equivalent emissions for EV. First, a test route is developed through the analysis of the topology of the Xi'an road structure and traffic flow. Second, the vehicle driving pattern data is gathered through an integrated method of chase car method and on-board method. The velocity-acceleration (V-A) grid method is used to divide speed and acceleration data into micro-states. Third, the proposed driving cycle construction method incorporates the Markov chain and Monte Carlo (MCMC) simulation method. Then, a filter process is designed to screen out the most representative driving cycle. Finally, the comparison of the simulation result and test results shows the constructed EV driving cycle is in line with reality, and estimating the EV's energy consumption per kilometer, driving range, and equivalent emissions under official driving cycles results in large relative errors. Therefore, the construction of a real-world driving cycle for specific cities or areas is necessary to evaluate energy consumption, driving range, and equivalent emissions of EV.

Health and climate benefits of Electric Vehicle Deployment in the Greater Toronto and Hamilton Area

This study presents the results of an integrated model developed to evaluate the environmental and health impacts of Electric Vehicle (EV) deployment in a large metropolitan area. The model combines a high-resolution chemical transport model with an emission inventory established with detailed transportation and power plant information, as well as a framework to characterize and monetize the health impacts. Our study is set in the Greater Toronto and Hamilton Area (GTHA) in Canada with bounding scenarios for 25% and 100% EV penetration rates. Our results indicate that even with the worst-case assumptions for EV electricity supply (100% natural gas), vehicle electrification can deliver substantial health benefits in the GTHA, equivalent to reductions of about 50 and 260 premature deaths per year for 25% and 100% EV penetration, compared to the base case scenario. If EVs are charged with renewable energy sources only, then electrifying all passenger vehicles can prevent 330 premature deaths per year, which is equivalent to $3.8 Billion (2016$CAD) in social benefits. When the benefit of EV deployment is normalized per vehicle, it is higher than most incentives provided by the government, indicating that EV incentives can generate high social benefits.

Effects of one-pedal automobile operation on the driver's emotional state and cognitive workload

A one-pedal system for operating an electric vehicle allows drivers to flexibly accelerate and decelerate (and even stop) by using just an accelerator pedal. Based on previous findings, one-pedal operation is considered to have the potential to increase positive emotions and decrease cognitive workload. To test this possibility, the present study compared the emotional state and cognitive workload between one-pedal and conventional two-pedal operation. Participants drove a vehicle on public roads, and driving enjoyment (i.e., pleasure and immersion) and the cognitive workload (i.e., ease and effortlessness) were assessed by means of questionnaires. In addition, physiological variations associated with driving pleasure and difficulty were assessed by electroencephalography (EEG). Both the questionnaire and EEG results revealed an increase in driving enjoyment in one-pedal operation. On the other hand, only the EEG results suggested a decrease in the cognitive workload in one-pedal operation; the questionnaire results did not show a significant difference between the pedal conditions. These findings support the notion that one-pedal operation has a positive influence on the driver's mental state, though its influence on the cognitive workload will require further investigation. We discuss future directions toward a better understanding of the effects of one-pedal operation on the driver's mental state.

Data for an Advanced Microstructural and Electrochemical Datasheet on 18650 Li-ion Batteries with Nickel-Rich NMC811 Cathodes and Graphite-Silicon Anodes

The data presented here were collected from a commercial LG Chem cylindrical INR18650 MJ1 lithium-ion (Li-ion) battery (approximate nominal specifications: 3.5 Ah, 3.6 V, 12.2 Wh). Electrochemical and microstructural information is presented, the latter collected across several length scales using X-ray computed tomography (CT): from cell to particle. One cell-level tomogram, four assembly-level and two electrode/particle-level 3D datasets are available; all data was collected in the pristine state. The electrochemical data consists of the full current and voltage charge-discharge curves for 400 operational cycles. All data has been made freely available via a repository [10.5522/04/c.4994651] in order to aid in the development of improved computational models for commercially-relevant Li-ion battery materials.

Forecasted datasets of electric vehicle consumption on the electricity grid of Spain

The information included in this study were calculated on the basis of data provided by the Spanish electricity grid, for thirteen years between 2007 and 2019. This data includes: the average consumption demand on the Spanish electricity grid at national level, and its availability. Subsequently, the report looks at the number of electric vehicles that could be supported in the years 2020–2023, depending on the consumption demand and availably of the electricity grid for those future years. The data presented in the article refers to the research study: ‘Electric vehicles in Spain: An overview of charging systems’[1] and ‘Analysis of charging stations for electric vehicles in Spain’ [2].

Impact of Electric Vehicles on Indirect Carbon Emissions and the Role of Engine Posttreatment Emission Control Strategies

Electricity generation in developing countries is dependent on fossil fuel-based thermal power plants, and the introduction of electric vehicles will only shift the threat of emissions from the operation stage to the energy generation stage. India is one of the developing countries in South Asia where fossil fuel-based power plants make major contributions to electricity generation. In this paper, a detailed review of the challenges faced by electric vehicles is discussed, and an analysis was conducted on the equivalent C emissions from electric vehicles by considering 3 scenarios in India: 1) current electricity generation, 2) power generation considering the installed capacity, and 3) Vision 2022. Based on these 3 scenarios, the main objectives of this work are to understand the potential of electric vehicles to reduce the overall C emissions after considering the indirect C emissions from electricity generation and to highlight the importance of emission control techniques. Experimental investigations of the conversion efficiency of diesel oxidation catalysis (DOC) systems have been conducted for comparative studies. The results of the analysis showed that the indirect C emissions from electric vehicles are higher than the C emissions from internal combustion engines for scenarios 1 and 2. In scenario 3, the C emissions from electric and fossil fuel-powered vehicles are found to be in the same range. The DOC system had an average conversion efficiency of 56% for hydrocarbons and 59% for particle number emissions. The posttreatment emission control systems in internal combustion engines will be the best possible solution, compared to electric vehicles, for reducing overall vehicular emissions until renewable energy sources have a major share in electricity generation. Integr Environ Assess Manag 2020;16:234-244. © 2019 SETAC.

Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver's range anxiety

The goal of this work is to reduce driver's range anxiety by estimating the real-time energy consumption of electric vehicles using deep convolutional neural network. The real-time estimate can be used to accurately predict the remaining range for the vehicle and hence, can reduce driver's range anxiety. In contrast to existing techniques, the non-linearity and complexity induced by the combination of influencing factors make the problem more suitable for a deep learning approach. The proposed approach requires three parameters namely, vehicle speed, tractive effort and road elevation. Multiple experiments with different variants are performed to explore the impact of number of layers and input feature descriptors. The comparison of proposed approach and five of the existing techniques show that the proposed model performed consistently better than existing techniques with lowest error.

Modelling and comparative dynamic analysis due to demagnetization of a torque controlled permanent magnet synchronous motor drive for energy-efficient electric vehicle

In this paper modelling and comparative dynamic analysis of a field oriented controlled permanent magnet synchronous motor (PMSM) torque drive employing a hysteresis current controller and a PWM (Pulse Width Modulation) operated current controller is presented. To illustrate the proposed concept in this torque controlled drive, torque and mutual flux linkages are applied as external inputs and speed of the machine is kept fixed. Moreover the magnitude of torque angle as well as stator current reference is controlled through the proposed machine dynamics. In fact a computation based on demagnetization of direct axis current to achieve the flux weakening in this proposed drive for current compensation is also introduced. To achieve the faster computation and accuracy Euler's integration technique is used to solve the proposed complex dynamics of the permanent magnet synchronous machine. In a hysteresis current, controllers with a large hysteresis band current ripple and the torque pulsations are prominent at higher carrier frequency of the inverter. Additionally, a relationship with the magnitude of torque pulsations, PWM carrier frequency and the hysteresis window size is also achieved through various case-studies. Furthermore, the presence of current ripple and the pulsations generate some noise as well as vibration in a typical electric propulsion system. Afterwards a PWM current controller with identical operating conditions is proposed for such reduction of torque pulsation as well as ripples in the current waveform. Finally various feasible results are presented through MATLAB simulation and necessary hardware implementation to justify the comparative assessment of the proposed controllers for dynamic performance analysis in energy-efficient electric vehicles.

A novel fractional variable-order equivalent circuit model and parameter identification of electric vehicle Li-ion batteries

Accurate Li-ion battery modeling is integral to the design of effective battery management systems in electric vehicles. However, the voltage-current (U-I) characteristic of Li-ion batteries presents strong nonlinearity. The application of fractional-order models to create lower-order models to represent physical systems (e.g., the battery characteristics for the state of charge estimation) is interesting and timely. In this paper, a novel fractional variable-order equivalent circuit model (FVO-ECM) is proposed to represent the nonlinear U-I characteristic of Li-ion batteries; its parameter identification is achieved and verified by charge and discharge tests. Compared with the integral-order equivalent circuit model and the fractional constant-order model, the proposed FVO-ECM can identify battery nonlinear characteristics most accurately.

Life cycle assessment of car sharing models and the effect on GWP of urban transportation: A case study of Beijing

Alongside a trend in lower private-vehicle ownership and the growing popularity of the shared use economy, car sharing is emerging as an alternative travel mode. The LCA model of car sharing is proposed, the global warming potential (GWP) of four car sharing models is determined, and the effect on GWP of urban transportation is explored. This study expanded the LCA of products to the LCA of services, by expanding the functional unit to service. In this study, the time dimension was considered during the functional unit setting. It was found that there are large GWP differences among different car sharing models. Electric vehicle car sharing models have less GWP than gasoline vehicle car sharing models. The dispatch distance and the numbers of passengers in one car are two key factors for GWP of car sharing models. When car sharing replaces ~10% and ~50% of private cars, the GWP reduction potentials of urban transportation are ~4% and ~20%, respectively. The overall distribution of car sharing should be set by considering the features of different models in different areas, to achieve the largest environment benefit by using car sharing in cities. Therefore, car sharing can be used as a measure for significant GWP reduction for city transportation.

Towards sustainable business models for electric vehicle battery second use: A critical review

A global mass market adoption of electric vehicles (EVs) is still hindered by the high costs of lithium-ion batteries (LIBs). Repurposing degraded EV batteries in second use applications holds the potential to reduce first-cost impediments of EVs. New business models are emerging rapidly within the EV and battery second use (B2U) industries but they focus on economic aspects without integrating social and environmental dimensions. Simultaneously, the emerging research topic around sustainable business models (SBMs) seem to be able to bridge the environmental management concerns in conjunction with economic and social changes. This paper addresses this paucity in the literature by offering an interdisciplinary approach by drawing upon key perspectives from the emerging sustainable technology of EVs and its underlying B2U market in relation to SBMs. Findings reveal major contributions to theorists and practitioners. B2U holds the potential to facilitate current unsustainable practices in the EV industry. This in turn, will lead towards a faster EV market uptake and improvements of overall sustainability performance through SBM perspectives. Accordingly, a B2U business model framework is conceptualised that embodies the cross-sector multi-stakeholder impact and the shared value creation mechanism for the EV industry and emerging B2U market. We finally conclude that as such B2U holds the potential to prove itself to be a viable and efficient case for sustainability. This can be implemented by taking a multi-stakeholder network centric business model design compared to traditionally firm-centric models, which ultimately refreshes the traditional business models on sustainability.

Developing an electric vehicle urban driving cycle to study differences in energy consumption

This paper develops a methodology for constructing a representative electric vehicle (EV) urban driving cycle as a basis for studying the differences in estimated energy consumption, taking Xi'an as an example. The test route is designed in accordance with the overall topological structure of the urban roads in the study region and the results of a traffic flow survey. Wavelet decomposition and reconstruction are utilized to preprocess the original data. Principal component analysis (PCA) is used to reduce the number of the kinetic parameters. The fuzzy C-means (FCM) clustering algorithm is used to cluster the driving segments. A representative EV urban driving cycle is constructed in accordance with the time proportions of three classes of driving segments and the correlation coefficients of the characteristic parameters. Finally, the differences in energy consumption estimates obtained using the constructed Xi'an EV urban driving cycle (XA-EV-UDC) and the international driving cycles are studied. The comparison shows that when international driving cycles are used to estimate the energy consumption and driving range of EVs, large relative errors will result, with energy consumption errors of 9.65 to 21.17% and driving range errors of 20.10 to 38.14%. Therefore, to accurately estimate energy consumption and driving range of EVs under real-world driving conditions, representative EV driving cycles for each typical city and region should be constructed.

Active steering wheel shimmy control for electric vehicle by sampled-data output feedback

In this paper, a new two Degree of Freedom (DOF) shimmy dynamic model of an Electric Vehicle (EV) with independent suspension subject to uncertain disturbances is built via Lagrange's theorem firstly. Secondly, Based on the built model, an active control method is proposed to solve the shimmy problem of the steering system via a sampled-data output feedback controller. The output feedback domination approach is also used to dominate uncertain disturbances by using a scaling gain. With the help of these tools, the sampling period and scaling gain are calculated to guarantee global stability and disturbance attenuation for the closed-loop control system. Finally, simulations and tests are conducted to verify the effectiveness of the sampled-data output feedback controller for the EV's shimmy problem under different conditions.

Comparison of the state of Lithium-Sulphur and lithium-ion batteries applied to electromobility

The market share in electric vehicles (EV) is increasing. This trend is likely to continue due to the increased interest in reducing CO₂ emissions. The electric vehicle market evolution depends principally on the evolution of batteries capacity. As a consequence, automobile manufacturers focus their efforts on launching in the market EVs capable to compete with internal combustion engine vehicles (ICEV) in both performance and economic aspects. Although EVs are suitable for the day-to-day needs of the typical urban driver, their range is still lower than ICEV, because batteries are not able to store and supply enough energy to the vehicle and provide the same autonomy as ICEV. EV use mostly Lithium-ion (Li-ion) batteries but this technology is reaching its theoretical limit (200-250 Wh/kg). Although the research to improve Li-ion batteries is very active, other researches began to investigate alternative electrochemical energy storage systems with higher energy density. At present, the most promising technology is the Lithium-Sulphur (Li-S) battery. This paper presents a review of the state of art of Li-Sulphur battery on EVs compared to Li-ion ones, considering technical, modelling, environmental and economic aspects with the aim of depicting the challenges this technology has to overcome to substitute Li-ion in the near future. This study shows how the main drawbacks for Li-S concern are durability, self-discharge and battery modelling. However, from an environmental and economic point of view, Li-S technology presents many advantages over Li-ion.

Longitudinal safety evaluation of electric vehicles with the partial wireless charging lane on freeways

As an environment friendly transportation mode, the electric vehicle (EV) has drawn an increasing amount of attention from governments, vehicle manufactories and researchers recently. One of the biggest issue impeding EV's popularization associates with the charging process. The wireless charging lane (WCL) has been proposed as a convenient charging facility for EVs. Due to the high costs, the application of WCL on the entire freeways is impractical in the near future, while the partial WCL (PWCL) may be a feasible solution. This study aims to evaluate longitudinal safety of EVs with PWCL on freeways based on simulations. The simulation experiments are firstly designed, including deployment of PWCL on freeways and distribution of state of charge (SOC) of EVs. Then, a vehicle behavior model for EVs is proposed based on the intelligent driver model (IDM). Two surrogate safety measures, derived from time-to-collision (TTC), are utilized as indicators for safety evaluations. Sensitivity analysis is also conducted for related factors. Results show that the distribution of EVs' SOC significantly affect longitudinal safety when the PWCL is utilized. The low SOC in traffic consisting of EVs has the negative effect on longitudinal safety. The randomness and incompliance of EV drivers worsens the safety performance. The sensitivity analysis indicates that the larger maximum deceleration rate results in the higher longitudinal crash risks of EVs, while the length of PWCL has no monotonous effect. Different TTC thresholds also show no impact on results. A case study shows the consistent results. Based on the findings, several suggestions are discussed for EVs' safety improvement. Results of this study provide useful information for freeway safety when EVs are applied in the future.

Data and material of the Safe-Range-Inventory: An assistance tool helping to improve the charging infrastructure for electric vehicles

The Safe-Range-Inventory (SRI) was constructed in order to help public authorities to improve the charging infrastructures for electric vehicles [1; 10.1016/j.trf.2017.04.011]. Specifically, the impact of fast (vs slow) charging stations on people's range anxiety was examined. Ninety-seven electric vehicle users from Germany (81 male; Mage=46.3 years, SD=12.1) were recruited to participate in the experimental design. Statistical analyses were conducted using ANOVA for repeated measures to test for interaction effects of available charging stations and remaining range with the dependent variable range anxiety. The full data set is publicly available via https://osf.io/bveyw/ (Carbon and Gebauer, 2017) [2].

Data and material of the Safe-Range-Inventory: An assistance tool helping to improve the charging infrastructure for electric vehicles

The Safe-Range-Inventory (SRI) was constructed in order to help public authorities to improve the charging infrastructures for electric vehicles [1; 10.1016/j.trf.2017.04.011]. Specifically, the impact of fast (vs slow) charging stations on people's range anxiety was examined. Ninety-seven electric vehicle users from Germany (81 male; M_age=46.3 years, SD=12.1) were recruited to participate in the experimental design. Statistical analyses were conducted using ANOVA for repeated measures to test for interaction effects of available charging stations and remaining range with the dependent variable range anxiety. The full data set is publicly available via https://osf.io/bveyw/ (Carbon and Gebauer, 2017) [2].

A market modeling review study on predicting Malaysian consumer behavior towards widespread adoption of PHEV/EV

With the rising concern about climate change, there has been an increased public awareness that has resulted in new government policies to support scientific research for mitigating these problems. Malaysia is among the major energy-intense countries and is under an excessive burden to advance its energy efficiency and to also work towards the reduction of its carbon emission. Plug-in hybrid electric vehicles (PHEVs) have the potential to lessen the carbon emission and gasoline consumption in order to alleviate environmental problems. Most of the energy problems linked to the increasing transportation pollution are now being reduced with the solution of the adoption of PHEVs. PHEVs are seen as a solution to cut carbon emission, which prevents environmental damages. Furthermore, PHEVs' driving range and performance can be comparable to the other hybrid vehicles as well as the conventional IC engines that have gasoline and diesel tanks. Thus, many efforts are being initiated to promote the use of PHEVs as an innovative and affordable transportation system. In order to achieve making the consumers aware of the adoption of PHEVs, we used a model which is based on the extended theory of planned behavior (TPB). This review is based on the factors affecting the adoption of PHEVs among Malaysian consumers. The model takes into account the ten key features that influence the adoption of PHEVs, such as environmental concern, personal norm, attitude, vehicle ownership costs, driving range, charging time, intention, subjective norm, perceived behavioral control, and personal norm. All these constructs are drivers towards the adoption of PHEVs. These factors affect the relationship between the adoption of PHEVs and how consumers intend to protect the environment. This review is based on improving how the "attitude-action" gap is understood as it is an important element for further studies on PHEVs. The aim of the research is to come up with a framework that examines how to modify the consumer's environmental concerns in acquiring PHEVs. This will pave the way for more academic research and future works that can emphasize how to obtain empirical results. The authors' recommendation is that, before a consumer's behavior is assessed and considered, an observation of the current technology is needed with methods and knowledge of the existing technology adoption aspect.

Effect of additional warning sounds on pedestrians' detection of electric vehicles: An ecological approach

Virtually silent electric vehicles (EVs) may pose a risk for pedestrians. This paper describes two studies that were conducted to assess the influence of different types of external sounds on EV detectability. In the first study, blindfolded participants had to detect an approaching EV with either no warning sounds at all or one of three types of sound we tested. In the second study, designed to replicate the results of the first one in an ecological setting, the EV was driven along a road and the experimenters counted the number of people who turned their heads in its direction. Results of the first study showed that adding external sounds improve EV detection, and modulating the frequency and increasing the pitch of these sounds makes them more effective. This improvement was confirmed in the ecological context. Consequently, pitch variation and frequency modulation should both be taken into account in future AVAS design.

Potential impacts of electric vehicles on air quality in Taiwan

The prospective impacts of electric vehicle (EV) penetration on the air quality in Taiwan were evaluated using an air quality model with the assumption of an ambitious replacement of current light-duty vehicles under different power generation scenarios. With full EV penetration (i.e., the replacement of all light-duty vehicles), CO, VOCs, NOx and PM2.5 emissions in Taiwan from a fleet of 20.6 million vehicles would be reduced by 1500, 165, 33.9 and 7.2Ggyr(-1), respectively, while electric sector NOx and SO2 emissions would be increased by up to 20.3 and 12.9Ggyr(-1), respectively, if the electricity to power EVs were provided by thermal power plants. The net impacts of these emission changes would be to reduce the annual mean surface concentrations of CO, VOCs, NOx and PM2.5 by about 260, 11.3, 3.3ppb and 2.1μgm(-3), respectively, but to increase SO2 by 0.1ppb. Larger reductions tend to occur at time and place of higher ambient concentrations and during high pollution events. Greater benefits would clearly be attained if clean energy sources were fully encouraged. EV penetration would also reduce the mean peak-time surface O3 concentrations by up to 7ppb across Taiwan with the exception of the center of metropolitan Taipei where the concentration increased by <2ppb. Furthermore, full EV penetration would reduce annual days of O3 pollution episodes by ~40% and PM2.5 pollution episodes by 6-10%. Our findings offer important insights into the air quality impacts of EV and can provide useful information for potential mitigation actions.

Consumer purchase intention towards environmentally friendly vehicles: an empirical investigation in Kuala Lumpur, Malaysia

This paper examines whether attitudes towards electric vehicles (ATEVs), subjective norms (SNs) and perceived behavioural control (PBC) have significant associations with consumer purchase intention (PI) and the purchase behaviour of environmentally friendly vehicles (EFVs). The results from the survey questionnaires are analysed using confirmatory factor analysis (CFA) and structural equation modelling (SEM). The findings of this paper indicate that ATEV, SN and PBC significantly influence PI. This finding also indicates that environmental consequence and individual preferences do not influence the PI of the respondents. We found that Malaysian car owners are largely unaware of the greenhouse effects on the environment or attach to it little importance, which is reflected in their PI towards EFVs. The outcomes of this study could help policymakers design programmes to influence attitudes, subjective norms, perceived behavioural control and purchase behaviour to prevent further air pollution and reduce CO2 emissions from the transportation sector.

Uniform stable observer for the disturbance estimation in two renewable energy systems

In this study, an observer for the states and disturbance estimation in two renewable energy systems is introduced. The restrictions of the gains in the proposed observer are found to guarantee its stability and the convergence of its error; furthermore, these results are utilized to obtain a good estimation. The introduced technique is applied for the states and disturbance estimation in a wind turbine and an electric vehicle. The wind turbine has a rotatory tower to catch the incoming air to be transformed in electricity and the electric vehicle has generators connected with its wheels to catch the vehicle movement to be transformed in electricity.

Evaluation strategy of regenerative braking energy for supercapacitor vehicle

In order to improve the efficiency of energy conversion and increase the driving range of electric vehicles, the regenerative energy captured during braking process is stored in the energy storage devices and then will be re-used. Due to the high power density of supercapacitors, they are employed to withstand high current in the short time and essentially capture more regenerative energy. The measuring methods for regenerative energy should be investigated to estimate the energy conversion efficiency and performance of electric vehicles. Based on the analysis of the regenerative braking energy system of a supercapacitor vehicle, an evaluation system for energy recovery in the braking process is established using USB portable data-acquisition devices. Experiments under various braking conditions are carried out. The results verify the higher efficiency of energy regeneration system using supercapacitors and the effectiveness of the proposed measurement method. It is also demonstrated that the maximum regenerative energy conversion efficiency can reach to 88%.

An improved fault-tolerant control scheme for PWM inverter-fed induction motor-based EVs

This paper proposes an improved fault-tolerant control scheme for PWM inverter-fed induction motor-based electric vehicles. The proposed strategy deals with power switch (IGBTs) failures mitigation within a reconfigurable induction motor control. To increase the vehicle powertrain reliability regarding IGBT open-circuit failures, 4-wire and 4-leg PWM inverter topologies are investigated and their performances discussed in a vehicle context. The proposed fault-tolerant topologies require only minimum hardware modifications to the conventional off-the-shelf six-switch three-phase drive, mitigating the IGBTs failures by specific inverter control. Indeed, the two topologies exploit the induction motor neutral accessibility for fault-tolerant purposes. The 4-wire topology uses then classical hysteresis controllers to account for the IGBT failures. The 4-leg topology, meanwhile, uses a specific 3D space vector PWM to handle vehicle requirements in terms of size (DC bus capacitors) and cost (IGBTs number). Experiments on an induction motor drive and simulations on an electric vehicle are carried-out using a European urban driving cycle to show that the proposed fault-tolerant control approach is effective and provides a simple configuration with high performance in terms of speed and torque responses.