Search Results (49)

The transition to passenger car electrification is a crucial step in China’s strategic efforts to achieve carbon peak and carbon neutrality. However, previous research has not considered the variances in vehicle models. Hence, this study aims to fill this gap by comparing the carbon emission reduction and economic feasibility of battery electric vehicles (BEVs) in the Chinese market, taking into account different powertrains, vehicle segments, classes, and driving ranges. Next, the study identifies the most cost-effective BEV within each market segment, employing life-cycle assessment and life cycle cost analysis methods. Moreover, at different levels of technological development, we construct three low-carbon measures, including electricity decarbonization (ED), energy efficiency improvement (EEI), and vehicle lightweight (LW), to quantify the emission mitigation potentials from different carbon reduction pathways. The findings indicate that BEVs achieve an average carbon reduction of about 31.85% compared to internal combustion engine vehicles (ICEVs), demonstrating a significant advantage in carbon reduction. However, BEVs are not economically competitive. The total life cycle cost of BEVs is 1.04–1.68 times higher than that of ICEVs, with infrastructure costs accounting for 18.8–57.8% of the vehicle’ s life cycle costs. In terms of cost-effectiveness, different models yield different results, with sedans generally outperforming sport utility vehicles (SUVs). Among sedans, both A-class and B-class sedans have already reached a point of cost-effectiveness, with the BEV400 emerging as the optimal choice. In low-carbon emission reduction scenarios, BEVs could achieve carbon reduction potentials of up to 45.3%, 14.9%, and 9.0% in the ED, EEI, and LW scenarios, respectively. Thus, electricity decarbonization exhibits the highest potential for mitigating carbon emissions, followed by energy efficiency improvement and vehicle lightweight. There are obvious differences in the stages of impact among different measures. The ED measure primarily impacts the waste treatment process (WTP) stage, followed by the vehicle cycle, while the EEI measure only affects the WTP stage. The LW measure has a complex impact on emission reductions, as the carbon reductions achieved in the WTP stage are partially offset by the increased carbon emissions in the vehicle cycle. Full article

This study explores the importance of considering regional aspects and different calculation approaches when assessing the environmental impact of passenger cars in Germany. The transportation sector, in general, needs to improve its transition to comply with national and international goals, and more efficient measures are necessary. To achieve this, the spatial heterogeneity of underlying data, such as vehicle stocks, cubic capacity classes as a proxy for consumption values, and annual mileage, is investigated with respect to regional differences. Using data samples for the year 2017, the average emission values per car and year are calculated as well as Germany’s total emission values from the utilization of passenger cars. Conducting a spatially informed allocation algorithm, battery electric vehicles (BEVs) are added to certain regional fleets, replacing cars with internal combustion engines (ICEs). The results show significant regional differences in the underlying data, with a divergence between rural and urban areas as well as northern and southern regions, while the spread in mileage values is higher than that in consumption values. Comparing the tank-to-wheel (TtW) and well-to-wheel (WtW) approaches reveals different values with an increased spread as more BEVs are introduced to the fleet. Using the presented concept to allocate BEVs, emissions can be reduced by 1.66% to 1.35%, depending on the calculation perspective, compared to the extrapolation of historical values. Furthermore, rural areas benefit more from optimized allocation compared to urban ones. The findings suggest that regional distribution strategies could lead to more efficient reductions in GHG emissions within the transportation sector while incorporating both TtW and WtW approaches, leading to more comparable and precise analyses. Full article

In the United States, transportation is the largest contributor to greenhouse gas emissions, with passenger vehicles accounting for the majority. Battery electric vehicles (BEVs) offer a significant opportunity to reduce emissions, as they have fewer emissions related to electricity generation compared to gasoline-powered vehicles. However, the benefits of BEVs are limited by their low adoption rates, particularly among older adults. In 2023, only 9.3% of vehicles on US roads were electric, and older adults (age 65 and above) have the lowest ownership and least interest in purchasing electric vehicles. This review aimed to understand the empirical data on the adoption and use of BEVs among older drivers, identify research gaps, and provide a research agenda to promote BEV use among this demographic for a more sustainable future. The review found that older drivers possess unique perceptions, often seeing more environmental benefits and fewer cost-related barriers than younger drivers, but concerns about charging infrastructure remain a significant obstacle. Notably, there is limited detailed research specific to older adults’ use patterns, charging behaviors, and the potential influence of socioeconomic factors. Future research should consider more nuanced age definitions, mixed-method approaches, and real-world behavioral studies over extended periods. A concerted effort toward understanding and addressing these barriers can inform strategies to increase BEV adoption among older adults, contributing to broader environmental goals. The review proposes a research agenda focused on understanding older adults’ adoption decisions, driving and charging behaviors, and effective training methods to facilitate BEV use. Full article

BEVs (Battery Electric Vehicles) have received widespread attention from various countries for their potential in combating global warming, the energy crisis, and environmental pollution. The driving range and energy consumption of BEVs vary significantly under different driving cycles, which often results in discrepancies between the values reported by manufacturers and real-world data. To address this issue, this paper establishes a modular simulation model of a BEV on the Matlab/Simulink platform and conducts simulation experiments and analyses of driving range and energy consumption under three different standard driving cycles, namely, the NEDC (New European Driving Cycle), WLTC (World Light Vehicle Test Cycle), and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger Car), and compares the results with data from vehicle manufacturers and consumers. The results of the study show that the NEDC conditions are more ideal, the CLTC-P conditions are the most intense vehicle driving, and the WLTC conditions require the highest overall vehicle performance. Compared with other standard cycles, the WLTC conditions show better alignment with real-world driving range data. The two main factors affecting the energy consumption in each condition are driving range and acceleration. The energy recovery strategy, braking frequency, and average deceleration speed of the driving cycle conditions are important factors affecting the braking energy recovery. This study provides a theoretical basis for driving range and energy consumption testing and driving cycle condition improvement of BEVs. Full article

The automotive industry is experiencing a surge in system complexity driven by the ever-growing number of interacting components, subsystems, and control systems. This complexity is further amplified by the expanding range of component options available to original equipment manufacturers (OEMs). OEMs work in parallel on more than one vehicle model, with multiple vehicle variants for each vehicle model. With the increasing number of vehicle variants needed to cater to diverse regional needs, development complexity escalates. To address this challenge, modern techniques like Model-Based Systems Engineering (MBSE), digitalization, and Artificial Intelligence (AI) are becoming essential tools. These advancements can streamline concept development, optimize thermal and HVAC system design across variants, and accelerate the time-to-market for next-generation EVs. The development of battery electric vehicles (BEVs) needs a strong focus on thermal management systems (TMSs) and heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in maintaining optimal battery temperature, maximizing range and efficiency, and ensuring passenger comfort. This article proposes a digital prototype (DP) and AI-based methodology to specify BEV thermal system and HVAC system components in the concept phase. This methodology uses system and variant thinking in combination with digital prototype (DP) and AI to verify BEV thermal system architecture component specifications for future variants without extensive simulation. A BEV cabin cooling requirement of 22 °C to be achieved within 1800s at a high ambient temperature (45 °C) is required, and its verification is used to prove this methodology. Full article

During the last decade, developing more sustainable transportation modes has become a primary objective for car manufacturers and governments around the world to mitigate environmental issues, such as climate change, the continuous increase in greenhouse gas (GHG) emissions, and energy depletion. The use of hydrogen fuel cell technology as a source of energy in electric vehicles is considered an emerging and promising technology that could contribute significantly to addressing these environmental issues. In this study, the effects of Hydrogen Fuel Cell Battery Electric Vehicles (HFCBEVs) on global GHG emissions compared to other technologies, such as BEVs, were determined based on different relevant factors, such as predicted sales for 2050 (the result of the developed prediction model), estimated daily traveling distance, estimated future average global electricity emission factors, future average Battery Electric Vehicle (BEV) emission factors, future global hydrogen production emission factors, and future average HFCBEV emission factors. As a result, the annual GHG emissions produced by passenger cars that are expected to be sold in 2050 were determined by considering BEV sales in the first scenario and HFCBEV replacement in the second scenario. The results indicate that the environmental benefits of HFCBEVs are expected to increase over time compared to those of BEVs, due to the eco-friendly methods that are expected to be used in hydrogen production in the future. For instance, in 2021, HFCBEVs could produce more GHG emissions than BEVs by 54.9% per km of travel, whereas in 2050, BEVs could produce more GHG emissions than HFCBEVs by 225% per km of travel. Full article

The use of electric automobiles, or EVs, is essential to environmentally conscious transportation. Battery EVs (BEVs) are predicted to become increasingly accepted for passenger vehicle transportation within the next 10 years. Although enthusiasm for EVs for environmentally friendly transportation is on the rise, there remain significant concerns and unanswered research concerns regarding the possible future of EV power transmission. Numerous motor drive control algorithms struggle to deliver efficient management when ripples in torque minimization and improved dependability control approaches in motors are taken into account. Control techniques involving direct torque control (DTC), field orientation control (FOC), sliding mode control (SMC), intelligent control (IC), and model predictive control (MPC) are implemented in electric motor drive control algorithms to successfully deal with this problem. The present study analyses only sophisticated control strategies for frequently utilized EV motors, such as the brushless direct current (BLDC) motor, and possible solutions to reduce torque fluctuations. This study additionally explores the history of EV motors, the operational method between EM and PEC, and EV motor design techniques and development. The future prospects for EV design include a vital selection of motors and control approaches for lowering torque ripple, as well as additional research possibilities to improve EV functionality. Full article

Rapid advancements in lithium-ion battery (LIB) technology have paved the way for the electrification of diverse applications, with continuous improvements in performance, substantial cost reductions, and the emergence of new manufacturers, formats, and cell chemistries. However, this diversity poses challenges in identifying the most suitable battery cells for specific applications. Here, we present a high-level techno-economic framework for cell selection, leveraging an extensive database of over 500 real-world cells, techno-economic analyses of emerging applications, and a Python-based modeling approach. We apply this method to three electrifiable mobile applications with distinct characteristics: battery electric cars, industrial forklifts, and regional passenger trains. Our results emphasize substantial variations in technical requirements, from power capability to energy density or longevity. We observe no particular differentiation according to cell formats, but tendencies for most suitable chemistries per application. No cell is suitable for all applications, particularly regarding the required maximum cell costs to ensure profitability, ranging from a few to several hundred Euros per kWh to achieve cost parity with a state-of-the-art reference technology. These findings highlight the importance of tailored cell selection strategies for decision makers to optimize performance and cost-effectiveness across different applications. Full article

Battery electric vehicle (BEV) air conditioning systems often use positive temperature coefficient (PTC) heaters to heat the passenger compartment. The heating process consumes a lot of energy in low-temperature environments, which seriously affects the driving range and user experience. This study aims to reduce the low-temperature energy consumption of the air conditioning system and improve energy efficiency through an innovative optimization method. In this study, the energy consumption composition of the air conditioning system was analyzed, and the goal of minimizing the sum of the total power consumption of the PTC heater and the blower was determined, while the efficiency characteristic of the blower was considered at the same time. The relationship between the average temperature of the passenger compartment measurement points and the PTC power and airflow rate was studied by combining experiments and numerical simulations, and the alternative operating conditions that met the temperature requirement were determined. On this basis, the total power consumption of the air conditioning system was analyzed and optimized. The results show that PTC power, airflow rate, and blower efficiency all have an important influence on the total power consumption of the air conditioning system. The optimized scheme could reduce the theoretical total power from 1315.32 W of the original scheme to 1246.83 W, and the actual total power from 1350.05 W of the original scheme to 1326.56 W, with reductions of 5.21% and 1.74%, respectively. The low-temperature energy consumption optimization method for the BEV air conditioning systems proposed in this study is instructive for the selection of blowers and the design of control strategies for air conditioning systems. Full article

Although technical improvements to engines and aftertreatment systems have the greatest impact on pollutant emissions, there is also potential for reducing emissions through driver behavior. This potential can be realized in the very short term, while better emission-control technologies only take effect once they have penetrated the market. In addition to a change in driving style, the vehicle owner’s choice of vehicle technology and size class will also have an impact on the future emissions of the vehicle fleet. The effects of different driving styles, the tire choice, the vehicle size class, and propulsion technologies on energy consumption and tailpipe and non-exhaust emissions are analyzed in this paper for different traffic situations and start temperatures for cars with petrol and diesel combustion engines and for battery electric vehicles. The analysis is completed with the corresponding upstream emissions from fuel and electricity production. The analysis is based on a vehicle simulation using the Passenger car and Heavy-duty Emission Model (PHEM), which is based on a large database of vehicles created using measurements of real driving conditions. For the assessment of the driving style, a novel method was developed in an H2020 project, which reproduces a measured trip with a virtual eco-driver. Carbon dioxide equivalent emissions (CO_2eq) increase with increasing vehicle size, but can be reduced by around 20% for conventional vehicles and 17% for battery electric vehicles (BEVs) through an environmentally conscious driving style. On average, BEVs have around 50% lower CO_2eq emissions than conventional vehicles, if the emissions from vehicle production are also taken into account. On an average journey of 35 km, the cold start of modern diesel vehicles accounts for around half of the total NO_x emissions, while the proportion of cold starts for petrol vehicles is around 25%. Tire and brake wear together generate a similar amount of PN23 emissions as the exhaust gases from new cars. Full article

One of the global ecological problems is the excessive carbon dioxide emissions generated by vehicles in the transport sector, including passenger transport. Therefore, the objective of this investigation was to develop a model that supports the prediction of vehicle variants that will be satisfactory to the customer in terms of: (i) quality level and (ii) environmental impact throughout the life cycle. This model was developed with the following techniques: TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), LCA (Life Cycle Assessment), SMARTER (Specific, Measurable, Achievable, Relevant, and Time-bound), Pareto–Lorenz, and the Multi-Criteria Decision Method rule (7 ± 2). A model test was carried out for production variants of the electric vehicle BEV (battery electric vehicle) for which the quality level and life cycle assessment were estimated. Vehicle quality levels ranged from 0.15 to 0.69, with a weight of 0.75. However, vehicle life cycle scores were estimated in the range of 0.25 to 0.57, with a weight of 0.25. Ultimately, the level of the vehicles’ LCA ranged from 0.18 to 0.62. As a result, it was shown that on the basis of various modifications of the quality level of vehicle variants and the corresponding environmental impacts throughout their life cycle, it is possible to predict the vehicle variant that is most satisfactory for the customer and, at the same time, environmentally friendly. The originality of the model relies on supporting the making of sustainable design decisions and the planning of vehicle improvement actions according to customer expectations. Therefore, the model can be used to analyse different types of vehicles by producers and dealers of these products. Full article

Exergy is a thermodynamic concept that ponders the quality of energy. It evaluates the irreversibilities of a machine, demonstrating its capacity to perform work associated with energy conversion. This article focuses on directing public policies and vehicle development toward their most proper usage worldwide. In the urban mobility scenario, there is an obvious demand to decrease greenhouse gas (GHG) emissions. In addition, the internal combustion engine (ICE) experiences considerable energy losses through heat exchange through the radiator and exhaust flow gases, which are not considerable in battery electric vehicles (BEVs) since there are no exhaust gases subsequent to combustion, nor combustion itself. This work presents longitudinal dynamics simulations of passenger vehicles to understand the magnitude of exergy destruction in ICEVs and BEVs, considering the Brazilian and European Union electric energy mix. Overall, the method can be applied to any other country. The simulation and model parameters were configured to match production road vehicles commercialized in the Brazilian market based on different versions of the same model. Two vehicle dynamic duty cycles were used, one relating to urban usage and another to highway usage, resulting in an overall exergy efficiency of around 50–51% for BEVs considering the exergy destruction in power plants. In contrast, ICE has an average efficiency of 20% in the urban cycle and around 30% in the highway cycle. By comparing the overall equivalent CO₂ emissions, it is possible to conclude that EVs in the European energy matrix produce more GHG than ICE vehicles running on ethanol in Brazil. Nevertheless, there are increasing uses of coal, natural gas, and oil thermal electric power plants, raising the question of how the transition may occur with a general increase in electrification since there is an increasing electric expenditure in all sectors of society, and the renewable energy plants may not meet all of the demand. Full article

All electric vehicles are the only way to decarbonize transport quickly and substantially. Although multilevel inverters have already been used in some transportation modes, they are rarely used in road transportation, especially in light-duty passenger BEVs. With the transition to a high 800-V DC link to extend the driving range and enable extreme fast charging, the possibility of using multilevel inverters in commercial light-duty passenger BEVs becomes feasible. Higher efficiency, higher power density, better waveform quality, lower switching frequency, the possibility of using low-rated switches, and inherent fault tolerance are known advantages of multilevel inverters that make them an efficient option for replacing 2-level inverters in high DC link passenger BEVs. This paper discusses high DC link voltage benefits in light-duty passenger BEVs, presents the state-of-the-art of different conventional multilevel inverter topologies used in BEVs, and compares them with conventional 2-level inverters from different aspects and limitations. Based on commercial upper-class passengers’ BEV data and a review of multilevel inverters on the market, future trends and possible research areas are identified. Full article

To limit climate change, decarbonization of the transportation sector is necessary. The change from conventional combustion vehicles to vehicles with electric drives is already taking place. In the long term, it can be assumed that a large proportion of passenger cars will be battery–electric. On the one hand, this conversion will result in higher energy and power requirements for the electricity network; on the other hand, it also offers the potential for vehicles to provide energy for various systems in the future. Battery–electric vehicles can be used to shift grid purchases, optimize the operation of other components and increase the self-consumption rate of photovoltaic systems. An LP model for the optimal energy management of the neighborhood consisting of buildings with electricity and heat demand, a PV system, a BEV fleet, a heat pump and thermal storage was formulated. The potential of the BEV fleet to provide energy via V2B in the neighborhood was investigated, considering electricity tariff models and individual charging/discharging efficiencies of vehicles and stochastic mobility profiles. The vehicle fleet provides between $4.8$$\mathrm{kWh}$⁻¹$\mathrm{sqm}$⁻¹$\mathrm{a}$ (flat-fee) and $25.3$$\mathrm{kWh}$⁻¹$\mathrm{sqm}$⁻¹$\mathrm{a}$ (dynamic tariff) per year, corresponding to 6.7, 9.5% and 35.7% of the annual energy demand of the neighborhood. All tariff models lead to optimization of self-consumption in summer. Dynamic pricing also leads to arbitrage during winter, and a power price tariff avoids peaks in grid draw. Due to individual charging efficiencies, the power supplied by the fleet is distributed unevenly among the vehicles, and setting limits for additional equivalent full cycles distributes the energy more evenly across the fleet. The limits affect the V2B potential, especially below the limits of 20 yearly cycles for flat and power tariffs and below 80 cycles for a dynamic tariff. Full article

Hybrid electric vehicles (HEVs) are used as a bridge during the transition to battery electric vehicles (BEVs) and to make energy consumption more efficient. The main problem in improving the efficiency of HEV energy consumption is torque management. In this study, a novel approach based on a nonlinear model predictive controller to solve the reference tracking and torque distribution problem is proposed. That is to say, in order to increase the efficiency of torque distribution, the weights of nonlinear model predictive control (NMPC) are trained with a Deep Q Network (DQN), and an event-triggered mechanism is designed with DQN to reduce the computational cost of MPC. The considered torque distribution problem varies according to the type and structure of the HEV. In this study, a parallel type 2 hybrid electric vehicle (P2 HEV) is considered and modeled via publicly shared passenger vehicle data of the engine, motor, high-voltage battery, transmission, clutch, differential, and wheel characteristics. NMPC is formulated so that the torque values remain within the physical limits of the engine, and the battery also operates at its physical limits. Namely, it is guaranteed that the battery works according to a certain state of charge (SOC) window and current limits. The state of health (SOH) of the battery is also considered in the optimization. The motor and engine efficiencies increase by 3.61% and 2.86%, respectively, with the proposed control structure, while the computational cost is reduced by 52.01% when utilizing the proposed event-triggering mechanism in the NMPC controller. Full article