Search Results (581)

Flexible energy sources such as electric vehicles and the battery energy storage systems of intelligent distribution systems can provide system-wide auxiliary services such as frequency regulation for power systems. This paper proposes an optimal method for operating the local energy community that is based on two-stage scheduling. Firstly, the basic concepts of the local energy community and flexible service are introduced in detail. Taking LEC as the reserve unit of artificial frequency recovery, an energy information interaction model among LEC, balance service providers, and the power grid is established. Then, a two-stage scheduling framework is proposed to ensure the rationality and economy of community energy scheduling. In the first stage, day-ahead scheduling uses the energy community management center to predict the up/down flexibility capacity that LEC can provide by adjusting the BESS control parameters. In the second stage, real-time scheduling aims at maximizing community profits and scheduling LEC based on the allocation and activation of standby flexibility determined in real time. Finally, the correctness of the two-stage scheduling framework is verified through a case study. The results show that the control parameters used in the day-ahead stage can significantly affect the real-time profitability of LEC, and that LEC benefits more in the case of low BESS utilization than in the case of high BESS utilization and non-participation in frequency recovery reserve. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

Internal combustion engine vehicles damage the environment and public health by emitting toxic fumes, such as CO₂ or CO and other trace compounds. The use of electric cars helps to reduce the emission of pollutants into the environment due to the use of batteries with no direct and local emissions. However, accidents of battery electric vehicles pose new challenges, such as thermal runaway. Such accidents can be serious and, in some cases, may result in uncontrolled overheating that causes the battery pack to spontaneously ignite. In particular, the most dangerous vehicles are heavy goods vehicles (HGVs), as they release a large amount of energy that generate high temperatures, poor visibility, and respiratory damage. This study aims to determine the potential consequences of large BEV fires in road tunnels using computational fluid dynamics (CFD). Furthermore, a comparison between a BEV and an ICEV fire shows the differences related to the thermal and the toxic impact. Furthermore, the adoption of a longitudinal ventilation system in the tunnel helped to mitigate the BEV fire risk, keeping a safer environment for tunnel users and rescue services through adequate smoke control. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational data, how energy consumption and charging behavior affect battery aging and how operational strategies can be optimized to extend battery life under realistic conditions. This article presents an energy consumption analysis with respect to ambient temperatures and average vehicle speed based exclusively on real-world data of an urban bus fleet, providing a data foundation for range forecasting and infrastructure planning optimized for public transport needs. Additionally, the State of Charge (SOC) window during operation and vehicle idle time as well as the charging power were analyzed in this case study to formulate recommendations towards a more battery-friendly treatment. The central research question is whether battery-friendly operational strategies—such as reduced charging power and lower SOC windows—can realistically be implemented in daily public transport operations. The impact of the recommendations on battery lifetime is estimated using a battery aging model on drive cycles. Finally, the reduction in CO₂ emissions compared to diesel buses is estimated. Full article

In light of the persistent environmental degradation driven by fossil fuels, developing new energy sources is essential for achieving sustainability. The recent surge in electric vehicle adoption has underscored the significance of new energy batteries. However, the supply chains of new energy battery manufacturers face multiple sustainability risks, which impede sustainable practice adoption. To tackle these challenges, leanness philosophy is an effective tool, and Industry 5.0 enhances its efficacy significantly, further mitigating sustainability risks. This study integrates the supply chain, leanness philosophy, and Industry 5.0 by applying quality function deployment. A novel four-phase hybrid MCDM model integrating the fuzzy Delphi method, DEMATEL, AHP, and fuzzy VIKOR, identified five key sustainability risks five core leanness principles, and eight critical Industry 5.0 enablers. By examining a Chinese new energy battery manufacturer as a case study, the findings aim to assist managers and decision-makers in mitigating sustainability risks within their supply chains. Full article

The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. The proposed framework supports flexible configurations across multiple dimensions, including feature engineering, model selection, and training/testing strategies. It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. The framework’s applicability is demonstrated through five distinct use cases involving SoC estimation and RUL prediction using real-world and benchmark datasets. Experimental results highlight the framework’s adaptability, methodological transparency, and robust predictive performance across various battery chemistries, usage profiles, and degradation conditions. This work contributes to a standardized approach that facilitates the reproducibility, comparability, and practical deployment of ML-based battery analytics. Full article

This study analyses the greenhouse gas reduction potential of different end-of-life (EoL) strategies based on a case study of light electric vehicles (LEVs). Using a shared electric moped scooter as a reference, four EoL scenarios are evaluated in a comparative life cycle assessment (LCA). The modelling of the scenarios combines different R-strategies (e.g., recycling, reusing, and repurposing) regarding both the vehicle itself and the battery. German and EU regulations for vehicle and battery disposal are incorporated, as well as EU directives such as the Battery Product Pass. The global warming potential (GWP₁₀₀) of the production and EoL life cycle stages ranges from 644 to 1025 kg CO₂ eq among the four analysed scenarios. Landfill treatment led to the highest GWP₁₀₀, with 1.47 times higher emissions than those of the base scenario (status quo treatment following EU directives), while increasing component reuse and repurposing the battery cells achieved GWP₁₀₀ reductions of 2.8% and 7.8%, respectively. Overall, the importance of implementing sustainable EoL strategies for LEVs is apparent. To achieve this, a product design that facilitates EoL material and component separation is essential as well as the development of political and economic frameworks. This paper promotes enhancing the circularity of LEVs by combining the LCA of EoL strategies with eco-design considerations. Full article

Many electric vehicle (EV) batteries will retire in the next 5–10 years around the globe. These batteries are retired when no longer suitable for energy-intensive EV operations despite having 70–80% capacity left. The second-life use of these battery packs has the potential to address the increasing demand for battery energy storage systems (BESSs) for the electric grid, which will also create a robust circular economy for EV batteries. This article proposes a two-layered energy management algorithm (monthly layer and daily layer) for demand charge reduction for an industrial consumer using photovoltaic (PV) panels and BESSs made of retired EV batteries. In the proposed algorithm, the monthly layer (ML) calculates the optimal dispatch for the whole month and feeds the output to the daily layer (DL), which optimizes the BESS dispatch, BESSs’ degradation, and energy imported/exported from/to the grid. The effectiveness of the proposed algorithm is tested as a case study of an industrial load using a real-world demand charge and Real-Time Pricing (RTP) tariff. Compared with energy management with no consideration of degradation or demand charge reduction, this algorithm results in 71% less degradation of BESS and 57.3% demand charge reduction for the industrial consumer. Full article

To alleviate the impact of economic and environmental detriments caused by the increased demands of electric vehicle battery production and disposal, the use of spent batteries in second-life stationary applications such as energy storage for renewable sources or backup power systems, offers many benefits. This paper focuses on reducing the energy consumption cost and greenhouse gas emissions of Internet-of-Things-enabled campus microgrids by installing solar photovoltaic panels on rooftops alongside energy storage systems that leverage second-life batteries, a gas-fired campus power plant, and a wind turbine while considering the potential loads of a prosumer microgrid. A linear optimization problem is derived from the system by scheduling energy exchanges with the Ontario grid through net metering and solved by using Python 3.11. The aim of this work is to support Sustainable Development Goals, namely 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action). A comparison between a base case scenario and the results achieved with the proposed scenarios shows a significant reduction in electricity cost and greenhouse gas emissions and an increase in self-consumption rate and renewable fraction. This research work provides valuable insights and guidelines to policymakers. Full article

Production involves processes such as raw material extraction, energy consumption, and waste management, which can lead to significant environmental consequences. Therefore, supplier selection based not only on technical performance but also on environmental sustainability criteria has become a fundamental component of eco-friendly manufacturing strategies. Moreover, in the selection of electric vehicle batteries, it is essential to consider customer demands alongside environmental factors. Accordingly, selected suppliers should fulfill company expectations while also reflecting the “voice” of the customer. The objective of this study is to propose an integrated approach for green supplier selection by taking into account various environmental performance requirements and criteria. The proposed approach evaluates battery suppliers with respect to both customer requirements and green criteria. To construct the relational structure, the DEMATEL method was employed to analyze the interrelationships among customer requirements (CRs). Subsequently, the Quality Function Deployment (QFD) model was used to establish a central relational matrix that captures the degree of correlation between each pair of supplier selection criteria and CRs. Finally, to evaluate and rank alternative suppliers, the Interval Type-2 Fuzzy VIKOR (IT2 F-VIKOR) method was applied. The hybrid approach proposed by us, integrating DEMATEL, QFD, and IT2 F-VIKOR, offers significant improvements over traditional methods. Unlike previous approaches that focus independently on customer preferences or supplier criteria, our model provides a unified evaluation by considering both dimensions simultaneously. Furthermore, the use of Interval Type-2 Fuzzy Logic enables the model to better manage uncertainty and ambiguity in expert judgments, yielding more reliable results compared to conventional fuzzy approaches. Additionally, the applicability of the model has been demonstrated through a real-world case study, confirming its practical relevance and robustness in the selection of green suppliers for electric vehicle battery procurement. Full article

At present, transportation energy comes primarily from fossil fuels. In order to mitigate the effects of greenhouse gas emissions, it is necessary to transition to low-carbon transportation technologies. These technologies can include battery electric vehicles, fuel cell vehicles and biofuel vehicles. This transition includes not only the development and production of suitable vehicles, but also the development of appropriate infrastructure. For example, in the case of battery electric vehicles, this infrastructure would include additional grid capacity for battery charging. For fuel cell vehicles, infrastructure could include facilities for the production of suitable electrofuels, which, again, would require additional grid capacity. In the present paper, we look at some specific examples of infrastructure requirements for battery electric vehicles and vehicles using hydrogen and other electrofuels in either internal combustion engines or fuel cells. Analysis includes the necessary additional grid capacity, energy storage requirements and land area associated with renewable energy generation by solar photovoltaics and wind. The present analysis shows that the best-case scenario corresponds to the use of battery electric vehicles powered by electricity from solar photovoltaics. This situation corresponds to a 47% increase in grid electricity generation and the utilization of 1.7% of current crop land. Full article

Since 1 January 2024, newly produced heavy-duty trailers are subject to the assessment of their performance regarding CO₂ and fuel consumption according to Implementing Regulation (EU) 2022/1362. The method is based on the already established approach for the CO₂ and energy consumption evaluation of trucks and buses, i.e., applying a combination of component testing and vehicle simulation using the software VECTO (Vehicle Energy Consumption calculation TOol). For the evaluation of trailers, generic conventional towing vehicles in combination with the specific CO₂ and fuel consumption-relevant properties of the trailer, such as mass, aerodynamics, rolling resistance etc., are simulated in the “VECTO Trailer” software. The corresponding results are used in the European HDV CO₂ standards with which manufacturers must comply to avoid penalty payments (2030: −10% for semitrailers and −7.5% for trailers compared with the baseline year 2025). Methodology and legislation are currently being extended to also cover the effects of electrified trailers (trailers with an electrified axle and/or electrically supplied auxiliaries) on CO₂, electrical energy consumption, and electric range extension (special use case in combination with a battery-electric towing vehicle). This publication gives an overview of the developed regulatory framework and methods to be implemented in a future extension of VECTO Trailer as well as a comparison of different e-trailer configurations and usage scenarios regarding their impact on CO₂, energy consumption, and electric range by applying the developed methods in a preliminary potential analysis. Results from this analysis indicate that e-trailers that use small batteries (5–50 kWh) to power electric refrigeration units achieve a CO₂ reduction of 5–10%, depending primarily on battery capacity. In contrast, e-trailers designed for propulsion support with larger batteries (50–500 kWh) and e-axle(s) (50–500 kW) demonstrate a reduction potential of up to 40%, largely determined by battery capacity and e-axle rating. Despite their reduction potential, market acceptance of e-trailers remains uncertain as the higher number of trailers compared with towing vehicles could lead to slow adoption, especially of the more expensive configurations. Full article

Lithium batteries have become one of the best choices for current consumer electric vehicle batteries due to their good stability and high energy density. To ensure the safety and reliability of electric vehicles (EVs), the battery management system (BMS) must provide accurate and real-time information on the usage status of the onboard battery. This article highlights the precise estimation of the state of charge (SOC) applied to four models of lithium-ion batteries (Turnigy, LG, SAMSUNG, and PANASONIC) for electric vehicles in order to ensure optimal use of the battery and extend its lifespan, which is frequently influenced by certain parameters such as temperature, current, number of charge and discharge cycles, and so on. Because of the work’s novelty, the methodological approach combines the extended Kalman filter algorithm (EKF) with the noise matrix, which is updated in this case through an iterative process. This leads to the migration to a new adaptive extended Kalman filter algorithm (AEKF) in the MATLAB Simulink 2022.b environment, which is novel or original in the sense that it has a first-order association. The four models of batteries from various manufacturers were directly subjected to the Venin estimator, which allowed for direct comparison of the models under a variety of temperature scenarios while keeping an eye on performance metrics. The results obtained were mapped charging status (SOC) versus open circuit voltage (OCV), and the high-performance primitives collection (HPPC) tests were carried out at 40 °C, 25 °C, 10 °C, 0 °C and −10 °C. At these temperatures, their corresponding values for the root mean square error (RMSE) of (SOC) for the Turnigy graphene battery model were found to be: 1.944, 9.6237, 1.253, 1.6963, 16.9715, and for (OCV): 1.3154, 4.895, 4.149, 4.1808, and 17.2167, respectively. The tests cover the SOC range, from 100% to 5% with four different charge and discharge currents at rates of 1, 2, 5 and 10 A. After characterization, the battery was subjected to urban dynamometer driving program (UDDS), Energy Saving Test (HWFET) driving cycles, LA92 (Dynamometric Test), US06 (aggressive driving), as well as combinations of these cycles. Driving cycles were sampled every 0.1 s, and other tests were sampled at a slower or variable frequency, thus verifying the reliability and robustness of the estimator to 97%. Full article

The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging. Full article