Electric Vehicles Research Articles

Fault detection method for electric vehicle battery pack based on improved kurtosis and isolation forest

ABSTRACT Early detection of abnormal battery characteristics is crucial for ensuring personnel safety and minimizing property damage. Conventional fault diagnosis methods often struggle to detect minor faults in their early stages. To address this challenge, this paper proposes a fault diagnosis method for lithium-ion batteries in electric vehicles that utilizes real-world operational data. Initially, a wavelet threshold denoising algorithm is used to effectively remove voltage data noise while retaining fault characteristics. Subsequently, an early fault warning method based on improved kurtosis index is proposed, which is capable of capturing the weak abnormal features of the battery and issuing warnings. Furthermore, a faulty battery cell localization method based on two-dimension feature extraction and Isolation Forest algorithm is proposed to capture minor fault features and accurately locate faulty cells. Finally, experiments conducted on four operational electric vehicles demonstrated that the early fault warning method could promptly detect faults and issue alarms, while the faulty battery cell positioning method accurately identified the faulty cell, achieving 100 % Accuracy and 0 % False Positive Rate. This confirms the effectiveness of the proposed method. Additionally, Bootstrap was used to evaluate the performance metrics, further verifying the robustness of the method.

Promoted Osprey Optimizer: a solution for ORPD problem with electric vehicle penetration

AbstractThis paper proposes a new optimization technique to make an integration between the Optimal Reactive Power Dispatch (ORPD) problem and Electric Vehicles (EV). Here, a modified metaheuristic algorithm, called the Promoted Osprey Optimizer (POO) is used for this purpose. Inspired by the hunting behavior of ospreys, a predatory bird species, the POO algorithm employs various strategies like diving, soaring, and gliding to effectively explore the search space and avoid local optima. To evaluate its performance, the POO-based model has been applied to the IEEE 118-bus and IEEE 57-bus systems, considering different scenarios of EV penetration. The experimental findings demonstrate that the POO algorithm can effectively optimize the reactive power dispatch problem with EV integration, achieving significant reductions in active power losses and voltage deviations toward several existing metaheuristic optimization techniques in different terms. The POO algorithm demonstrates a significant reduction in power loss, achieving up to 22.2% and 16.2% in the 57-bus and 118-bus systems, respectively. This improvement is accompanied by reductions in voltage deviation of up to 20.6% and 15.7%. In the 57-bus system, power loss is reduced from 2.35 MW to 1.93 MW, while voltage deviation decreases from 0.034 p.u. to 0.027 p.u. For the 118-bus system, power loss is lowered from 4.21 MW to 3.53 MW, and voltage deviation is reduced from 0.051 p.u. to 0.043 p.u. Furthermore, the POO algorithm surpasses other optimization methods in minimizing voltage deviation, achieving reductions of up to 0.056 p.u. in the 57-bus system and up to 0.163 p.u. in the 118-bus system. Consequently, the POO algorithm holds great potential as a valuable tool for power system operators and planners to optimize reactive power dispatch and enhance power system performance with EV integration.

A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

Energy Consumption Optimization for an Electric Delivery Vehicle

For nearly two centuries, electric drives have been used in transportation. Nevertheless, they were not always favored by designers. The century-long dominance of heat engines led to the disregard of numerous challenges associated with the operation of electric drive systems. One of these issues is the optimization of energy consumption by an electric vehicle. This publication proposes an electronic Energy Consumption Optimizer (ECO) that predictively uses information about the shape of the route and speed limits on its individual sections to control the motor speed and gear changes in the gearbox. This work presents the structure of the optimizer system and the developed control algorithms. Additionally, electric motor excitation control was used, which may have contributed to reducing the power and weight of the electric drive motor. Simulation studies carried out using WLTP test cycles and cycles from real road routes showed the potential to decrease energy consumption for vehicle movement by approximately 10%.

Carbon Footprint of Electric Vehicles—Review of Methodologies and Determinants

The carbon footprint of a product and organization is one of the most important environmental indicators in many sectors, including transport. Consequently, electric vehicles (EV) are being introduced as an alternative to achieve decarbonization targets. This article presents an overview of methodologies for assessing the carbon footprint of electric vehicles, including a review of concepts, methods, standards, and calculation models based on the life cycle of the carbon footprint. The article also includes a systematic review of the results of EV carbon footprint analyses. The analysis of current knowledge on the carbon footprint focuses on road transport vehicles: Battery Electric Vehicles (BEV), Fuel Cell Electric Vehicles (FCEV), Hybrid Electric Vehicles (HEV), and Plug-in Hybrid Electric Vehicles (PHEV). Additionally, a review of factors determining the carbon footprint assessment of electric vehicles, considering their entire life cycle, has been conducted.

LIFE-CYCLE AND ENERGY CONSUMPTION OF THE COMMERCIALLY AVAILABLE PASSENGER CARS

Today, passenger vehicle emissions alone represent 15% of global greenhouse gas emissions (GHG). The automobile industry is aware of this and has taken steps towards decarbonization over the past decade. Emissions related to the six phases of the life cycle were determined from the LCA database compiled on the basis of data collected on emissions during the life cycle of different types of vehicles, units, sizes and geographical areas. Typically, Internal Combustion Engine Vehicles (ICE) constantly emit CO2 while driving, whereas Battery Electric Vehicles (BEV) and Fuel Cell Electric Vehicles (FCEV) do not. The transition to an electric vehicle is only as clean as the energy source used to charge the vehicle. Each BEV car represents approximately 2.2 MWh/year of load on the country's electric power system, while due to the way energy is converted, the ICE car represents approximately 11.9 MWh/year of fuel-equivalent load.

Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales.

Analysis of multidimensional impacts of electric vehicles penetration in distribution networks

AbstractMoving towards a cleaner, greener, and more sustainable future, expanding electric vehicles (EVs) adoption is inevitable. However, uncontrolled charging of EVs, especially with their increased penetration among the utility grid, imposes several negative technical impacts, including grid instability and deteriorated power quality in addition to overloading conditions. Hence, smart and coordinated charging is crucial in EV electrification, where Vehicle-to-Grid (V2G) technology is gaining much interest. Owing to its inherited capability of bi-directional power flow, V2G is capable of enhancing grid stability and resilience, load balancing, and congestion alleviation, as well as supporting renewable energy sources (RESs) integration. However, as with most emerging technologies, there are still technical research gaps that need to be addressed. In addition to these technical impacts, other multidisciplinary factors must be investigated to promote EVs adoption and V2G implementation. This paper provides a detailed demonstration of the technical problems associated with EVs penetration in distribution networks along with quantifiable insights into these limitations and the corresponding mitigation schemes. In addition, it discusses V2G benefits for power systems and consumers, as well as explores their technical barriers and research directions to adequately regulate their services and encourage EV’s owners to its embracement. Moreover, other factors, including regulatory, social, economic and environmental ones that affect EV market penetration are being studied and related challenges are analyzed to draw recommendations that aid market growth.

An Interval Fuzzy Programming Approach to Solve a Green Intermodal Routing Problem for Timber Transportation Under Uncertain Information

This study investigates an intermodal routing problem for transporting wood from a storage yard of the timber harvest area to a timber mill, in which the transfer nodes in the intermodal transportation network have multiple service time windows. To improve the environmental sustainability of timber transportation, a carbon tax policy is employed in the routing to reduce the carbon emissions. Uncertain information on the capacities and carbon emission factors of the transportation activities in the intermodal transportation network is modeled using interval fuzzy numbers to enhance the feasibility of the routing optimization in the actual timber transportation. Based on the above consideration, an interval fuzzy nonlinear optimization model is established to handle the specific routing problem. Model defuzzification and linearization are then conducted to obtain an equivalent formulation that is crisp and linear to make the global optimum solution attainable. A numerical experiment is conducted to verify the feasibility of the proposed model, and it reveals the influence of the optimization level and service time windows on the routing optimization, and it confirms that intermodal transportation is suitable for timber transportation. This experiment also analyzes the feasibility of a carbon tax policy in reducing the carbon emissions of timber transportation, and it finds that the performance of this policy is determined by the optimization level given by the timber mill and is not always feasible in all cases. For the case where a carbon tax policy is infeasible, this study proposes a bi-objective optimization that can use Pareto solutions to balance the economic and environmental objectives as an alternative. The bi-objective optimization further shows the relationship between lowering the transportation costs, reducing the carbon emissions, and enhancing the reliability on capacity and budget by improving the optimization level. The conclusions provide managerial insights that can help the timber mill and intermodal transportation operator organize cost-efficient, low-carbon, and reliable intermodal transportation for timber distribution, and support sustainable forest logistics.

Retraction Note: Integrated artificial intelligence and predictive maintenance of electric vehicle components with optical and quantum enhancements

Retraction Note: Integrated artificial intelligence and predictive maintenance of electric vehicle components with optical and quantum enhancements

Feasibility Assessment of Hybrid Renewable Energy Based EV Charging Station in Libya

This study presents an assessment of the feasibility of implementing a hybrid renewable energy-based electric vehicle (EV) charging station at a residential building in Tripoli, Libya. Utilizing the advanced capabilities of HOMER Grid software, the research evaluates multiple scenarios involving combinations of solar and wind energy sources integrated with energy storage and the utility grid. This analysis provides a novel approach to enhancing urban energy systems with renewable technologies in a region traditionally reliant on fossil fuels. Key contributions of this study include the demonstration of an innovative integration strategy that combines solar and wind power with battery storage to ensure a reliable and efficient energy supply for EV charging. Furthermore, the study addresses the practical implications for local energy policy, suggesting that such hybrid systems can significantly enhance energy security and support sustainable urban development. The authors studied five scenarios using HOMER. The results reveals that the annual total costs and payback periods are as follows: for Scenario 1 (wind/utility grid), the expenditure totals US$1,554,416 and payback period of 4.8/5.8 years; for Scenario 2 (solar/wind/Utility grid), the amount is US$1,554,506 and payback period of 4.8/5.8 years; and for Scenario 3(solar/wind/storage/utility grid), it escalates slightly to US$1,554,731, all predicated on the utility grid tariffs and payback period of 4.8/5.8 years. Furthermore, in Scenario 4 (solar/utility grid), the annual total cost is significantly reduced to US$30,589 and a payback period of 8.1/14.3 years, while Scenario 5 (solar/storage/utility grid) incurs an even lower expenditure of US$28,572, again based on the utility grid tariffs and a payback period of 14.0 years. The findings contribute valuable insights into the scalability and adaptability of renewable energy solutions, providing a robust framework for policymakers and planners considering similar implementations in other regions. Overall, the research underscores the potential of integrated renewable energy systems to transform urban energy infrastructures, promoting a sustainable and resilient energy future. The HOMER Grid analysis shows that configurations with energy storage are more cost-effective in the long run, even though they require higher initial costs. It also offers important insights into the economic viability and optimization of hybrid renewable energy systems for an EV charging station in Tripoli, Libya. These results highlight the significance of making calculated investments in renewable energy infrastructure and supporting policies for the development of sustainable energy.

E-Fuel: An EV-Friendly Urgent Electrical Charge-Sharing Model with Preference-Based Off-Grid Services

Electric-powered vehicles (EVs) allow for an environmentally friendly and economic alternative to fuel-running ones. However, such an alternative is expected to impose further usage hikes and periods of instability on cities’ power systems. From their perspective, cities need to scale their infrastructure grids to allow for adequate power resources to feed such new power-hungry consumers. Indeed, for such a green alternative to proceed, our power grids need to be ready to cope with any unexpected hikes in the power consumption rates without compromising the stability of the services provided to our homes and workplaces. Operators’ steps in this path are still modest, and the coverage of EV charging stations is still insufficient as they are trying to avoid any further costs for upgrading their infrastructures. The lack of price consideration for the charging services offered at charging stations may result in EV drivers paying higher costs compared to traditional fuel vehicles to charge their EVs’ batteries, hindering the economic incentive of owning such sorts of vehicles. Hence, it may take a while for sufficient coverage to exist. Although for drivers the adoption of EVs represents a city-friendly alternative with affordable expenses, it usually comes with range anxiety and battery charging concerns. In this work, we are presenting e-Fuel, a charge-sharing model that allows for preference-based mobile EV charging services. In e-Fuel, we are proposing a stable weight-based vehicle-to-vehicle matching algorithm, through which drivers of EVs will be capable of requesting instant mobile charge-sharing service for their EVs. In addition to being mobile, such charging services are customized, as they are chosen based on the drivers’ preferences of price-per-unit, charging speed, and time of delivery. The developed e-Fuel matching algorithm has been tested in various environments and settings. Compared to the benchmark price-based matching algorithm, the resulting matching decisions of e-Fuel come with balanced matching attributes that mostly allow for 6- to 7-fold shorter service delivery times for a minimal increase in service charges that vary between 9% and 65%.

Review of Economic, Technical and Environmental Aspects of Electric Vehicles

Electric vehicles (EVs) have seen significant advancements and mainstream adoption, prompting in-depth analysis of their economic, technical, and environmental impacts. Economically, while EVs offer lower operational costs than internal combustion engine vehicles, challenges remain, particularly for urban users reliant on public charging stations and the potential implementation of new road taxes to offset declining fuel tax revenues. Technically, electric motors in EVs have fewer moving parts, but battery management and cybersecurity complexities pose new risks. Transitioning from Nickel-Manganese-Cobalt (NMC) to Lithium-Iron-Phosphate (LFP) batteries reflects efforts to enhance thermal stability and mitigate fire hazards. Environmentally, lithium extraction for batteries has profound ecological impacts, including for water consumption and pollution. Battery production and the carbon footprint of the entire lifecycle remain pressing concerns, with battery recycling and second-life applications as crucial mitigation strategies. Smart integration of EVs with the energy infrastructure introduces challenges like grid stability and opportunities, such as smart, intelligent, innovative charging solutions and vehicle-to-grid (V2G) technology. Future research should develop economic models to forecast long-term impacts, advance battery technology, enhance cybersecurity, and conduct comprehensive environmental assessments to optimise the benefits of electromobility, addressing the multidimensional challenges and opportunities presented by EVs.

Techno-Economic Analysis of Grid-Connected Highway Solar EV Charging Station

AbstractSolar electric vehicle (EV) charging stations offer a promising solution to an environmental issue related to EVs by supplying eco-friendly electricity. Herein, we designed and analyzed a grid-connected highway solar EV charging station for 2022, 2030, and 2050 under two scenarios: Current policy scenario with restricted grid sales and policy mitigation scenario allowing grid sale. Future systems consider changes in EV charging station, grid CO2 emissions, carbon prices, and renewable costs. In 2022, high PV and battery costs led to a grid-only system. By 2030 and 2050, significant reductions in PV costs enabled systems with substantial PV capacity, especially under the policy mitigation scenario. Economic analysis showed that enabling grid sales reduces net present cost (NPC) by 40% in 2030 and 35% in 2050 compared to the current policy scenario, with significantly lower levelized cost of energy. CO2 emissions from EV operation are projected to be 44% and 3% of 2022 levels by 2030 and 2050, respectively. The policy mitigation scenario further reduces CO2 emissions by 22% in 2030 and 25% in 2050 compared to the current policy scenario. Sensitivity analyses reveal that increased grid sale capacity leads to higher renewable penetration and lower NPC but also increased grid congestion, highlighting the need for efficient grid management. Policymakers should consider revising regulations to support higher PV penetration and manage grid congestion. This study supports the transition to renewable systems and underscores the importance of policy measures in achieving sustainable energy goals.

The effect of different mission profiles and ambient temperature on the lifetime of boost converter IGBT with a robust controller in a hybrid electric vehicle

AbstractThe reliability of power electronic converters is one of the essential issues in designing of electric vehicles. This paper estimates the lifetime of the boost converter switch by Semikron and Coffin‐Manson models for two common failure mechanisms of Bond wire and Base plate solder, respectively. Four mission profiles based on the Artemis standard are applied to hybrid electrical vehicle model to determine unidirectional output power. Kharitonov's theory is used to design a robust controller to handle the uncertainty raised by different power cycles of the electric vehicle and the parameters of the converter during simulation. Stability of converter is achieved during simulation by identifying simple proportional integral controller coefficients with Kharitonov's theorem. A prototype 230 W boost converter is designed and utilized to validate average model and switch loss calculation relationships. The lifetime results indicate that the number of cycles, and the average and the maximum junction temperature have a more impact than the duration of the drive cycle on the lifetime of the converter. A mixed mission profile is considered to investigate the effect of sudden change in driving modes and speed on total consumed life and lifetime to enhance the study's applicability. Lifetime of switch is decreased significantly in mixed mode in comparison with other mission profiles in the same driving time. Furthermore, the motorway mission profile has 53%, 39.6%, and 160% less total consumed life in comparison with the urban, rural and mixed mission profiles, respectively. In addition, the effect of ambient temperature changes on IGBT lifetime has been investigated for four mission profiles. While motorway had the least total consumed life in 25°C, the urban had better performance in comparison with other mission profiles from 25 to 55°C.

Effectiveness and Heterogeneous Effects of Purchase Grants for Electric Vehicles

AbstractWe evaluate German purchase subsidies for battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) using data on new vehicle registrations in Germany during 2015-2022. We account for confounding time trends and interacting EU-level $$\text {CO}_{2}$$ standards using neighboring countries as a control group. We find that 40% of BEV and 25% of PHEV registrations were subsidy-induced. The program had strong distributional effects, with greater uptake in wealthier and greener counties. We estimate implied abatement costs of 870 euro per ton of $$\text {CO}_{2}$$ for BEVs and 2,470 euro for PHEVs, suggesting that policy makers should re-balance support schemes away from PHEVs.

Opportunities and Challenges of Fuel Cell Electric Vehicle-to-Grid (V2G) Integration

This paper presents an overview of the status and prospects of fuel cell electric vehicles (FC-EVs) for grid integration. In recent years, renewable energy has been explored on every front to extend the use of fossil fuels. Advanced technologies involving wind and solar energy, electric vehicles, and vehicle-to-everything (V2X) are becoming more popular for grid support. With recent developments in solid oxide fuel cell electric vehicles (SOFC-EVs), a more flexible fuel option than traditional proton-exchange membrane fuel cell electric vehicles (PEMFC-EVs), the potential for vehicle-to-grid (V2G)’s implementation is promising. Specifically, SOFC-EVs can utilize renewable biofuels or natural gas and, thus, they are not limited to pure hydrogen fuel only. This opens the opportunity for V2G’s implementation by using biofuels or readily piped natural gas at home or at charging stations. This review paper will discuss current V2G technologies and, importantly, compare battery electric vehicles (BEVs) to SOFC-EVs for V2G’s implementation and their impacts.

A Data-Driven Analysis of Electric Vehicle Adoption Barriers in the Philippines: Combining SEM and ANNs

As the world progresses into the peak of the Fourth Industrial Revolution, the adoption of smart and sustainable technologies, including electric vehicles (EVs), has gained significant momentum. However, the widespread acceptance of EVs is hindered by several unresolved barriers. This study investigates the factors influencing the adoption of electric vehicles in the Philippines, focusing on key barriers through an integrated approach using machine learning and structural equation modeling (SEM). Specifically, artificial neural networks (ANNs) and SEM are employed to analyze data from online surveys and the existing literature, identifying the critical obstacles that impact consumer acceptance. The findings reveal that the availability of charging stations, range anxiety, and vehicle costs are the primary deterrents to EV adoption. By incorporating a sustainability perspective, this study underscores the crucial role of electric vehicles in reducing environmental impacts and achieving carbon reduction targets. The hybrid methodology presented offers new insights to guide policymakers in promoting electric vehicle usage, thereby contributing to the global sustainable development goals.

Abstract 4119167: Electromagnetic Interference from Electric Vehicles: Implications for Subcutaneous Implantable Cardioverter-Defibrillators

Background: Electromagnetic interference (EMI) refers to signals discernible by a device’s circuitry, potentially resulting in erroneous sensing, pacing, mode switching and defibrillation. EMI poses potential risks to the functionality of cardiac implantable electronic devices, with Subcutaneous- Implantable Cardioverter-Defibrillators (S-ICDs) potentially more susceptible due to their design. Research Question: Does EMI from Electric Vehicles (EV) interfere with the functionality of S-ICDs? Goals: Developing and utilizing a loop antenna device that mimics S-ICDs to assess the susceptibility of S-ICDs to EMI from EVs in different charging conditions. Methods: A loop antenna device mimicking the S-ICD lead design was developed, and then validated by the Food and Drug Administration, to detect electromagnetic signals. The device was positioned at different locations in a Tesla Model 3 to measure electromagnetic signals. Results: Statistical analysis revealed a significant difference (p-value < 0.0001) in detected voltage (400-504 mV) around the cup holder compared to all other measured locations. These signals fell within the R-Wave Spectrum of 30-300 Hz. Conclusion: We identified EV-emitted EMI capable of disrupting S-ICD functionality. Further in-vivo studies are essential to determine the level of interference between S-ICDs and EMI from EVs.

Advanced System for Optimizing Electricity Trading and Flow Redirection in Internet of Vehicles Networks Using Flow-DNET and Taylor Social Optimization

The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles (EVs) are essential for cutting emissions and reliance on fossil fuels. According to research on flexible charging methods, allowing EVs to trade electricity can maximize travel distances and efficiently reduce traffic. In order to improve grid efficiency and vehicle coordination, this study suggests an ideal method for energy trading in the Internet of Vehicles (IoV) in which EVs bid for electricity and Road Side Units (RSUs) act as buyers. The Taylor Social Optimization Algorithm (TSOA) is employed for this auction process, focusing on energy and pricing to select the best Charging Station (CS). The TSOA integrates the Taylor series and Social Optimization Algorithm (SOA) to facilitate flow redirection post-trading, evaluating each RSU’s redirection factor to identify overloaded or underloaded CSs. The Flow-DNET model determines redirection policies for overloaded CSs. The TSOA + Flow-DNET approach achieved a pricing improvement of 0.816% and a redirection success rate of 0.918, demonstrating its effectiveness in optimizing electricity trading and flow management within the IoV framework.