Search Results (32)

The accelerating integration of electric vehicles (EVs) into contemporary transportation infrastructure has underscored significant limitations in traditional charging paradigms, particularly in accommodating heterogeneous user requirements within dynamic operational environments. This study presents a differentiated optimization framework for EV charging strategies through the systematic classification of user types. A multidimensional decision-making environment is established for three representative user categories—residential, commercial, and industrial—by synthesizing time-variant electricity pricing models with dynamic carbon emission pricing mechanisms. A bi-level optimization architecture is subsequently formulated, leveraging deep reinforcement learning (DRL) to capture user-specific demand characteristics through customized reward functions and adaptive constraint structures. Validation is conducted within a high-fidelity simulation environment featuring 90 autonomous EV charging agents operating in a metropolitan parking facility. Empirical results indicate that the proposed typology-driven approach yields a 32.6% average cost reduction across user groups relative to baseline charging protocols, with statistically significant improvements in expenditure optimization (p < 0.01). Further interpretability analysis employing gradient-weighted class activation mapping (Grad-CAM) demonstrates that the model’s attention mechanisms are well aligned with theoretically anticipated demand prioritization patterns across the distinct user types, thereby confirming the decision-theoretic soundness of the framework. Full article

Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle subsystems as data-driven entities. The simulation framework is developed in the MATLAB/Simulink environment and is based on a power dynamics approach, capturing nonlinear interactions and performance intricacies between different powertrain elements. This study investigates subsystem synergies and performance boundaries under a combined driving cycle composed of the NEDC, WLTP Class 3 and US06 profiles, representing urban, extra-urban and aggressive highway conditions. To emulate the real-world load-following strategy, a state transition power management and allocation method is synthesised. The proposed method dynamically governs the power flow between the fuel cell stack and the traction battery across three operational states, allowing the battery to stay within its allocated bounds. This simulation framework offers a near-accurate and computationally efficient digital counterpart to a commercial hybrid powertrain, serving as a valuable tool for educational and research purposes. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

Rechargeable Lithium-ion batteries (LIBs) have experienced swift advancement and widespread commercialization in electronic devices and electric vehicles, driven by their exceptional efficiency, energy capacity, and elevated power density. However, to promote sustainable energy development there is a dire need to further extend the search for developing and optimizing the existing anode active energy storage materials. This has steered research towards carbon-based anode materials, particularly graphene, to promote and develop sustainable and efficient LIB technology that can drive the next wave of industrial innovation. In this regard, density functional theory (DFT) computations are considered a powerful tool to elucidate chemical and physical properties at an atomistic scale and serve as a transformative framework, catalyzing the discovery of novel high-performance anode materials for LIBs. This review highlights the computational progress in graphene and graphene composites to design better graphene-based anode materials for LIBs. Full article

Recent advancements in lithium-metal-based battery technology have garnered significant attention, driven by the increasing demand for high-energy storage devices such as electric vehicles (EVs). Lithium (Li) metal has long been considered an ideal negative electrode due to its high theoretical specific capacity (3860 mAh g⁻¹) and low redox potential. However, the commercialization of Li-metal batteries (LMBs) faces significant challenges, primarily related to the safety and cyclability of the negative electrodes. The formation of lithium dendrites and uneven solid electrolyte interphases, along with volumetric expansion during cycling, severely hinder the commercial viability of LMBs. Among the various strategies developed to overcome these challenges, the introduction of artificial protective layers and the structural engineering of current collectors have emerged as highly promising approaches. These techniques are critical for regulating Li deposition behavior, mitigating dendrite growth, and enhancing interfacial and mechanical stability. This review summarizes the current state of Li-negative electrodes and introduces methods of enhancing their performance using a protective layer and current collector design. Full article

The increasing adoption of electric vehicles (EVs) has highlighted the need for sustainable lithium-ion battery (LIB) management. This study presents a material flow analysis (MFA) of EV LIBs in the Republic of Korea (RoK), using both a mass-based MFA and a substance flow analysis (SFA). The analysis defines 33 systems and 170 flows across the manufacturing, consumption, discharge and collection, and treatment stages, based on national statistics and data from 11 commercial facilities. In 2022, about 72,446 t of EV LIBs entered the consumption stage through new vehicle sales and battery replacements. However, domestic recovery was limited, as approximately 76.5% of used EVs were exported, reducing the volume of batteries available for recycling. The SFA, focusing on nickel (Ni), cobalt (Co), manganese (Mn), and lithium (Li), showed recovery rates of 69% for Ni, 80% for Co, 1% for Mn, and 80% for Li. Mn was not recovered because its low market price made the recovery process economically impractical. Additional losses occurred from the incineration of separators containing black mass and lithium discharged through wastewater. These findings offer data-driven insights to improve recovery efficiency, guide policy, and enhance the circularity of EV LIB management in the RoK. Full article

Research on lithium-ion batteries has been driven by the growing demand for electric vehicles to mitigate greenhouse gas emissions. Despite advances, batteries still face significant challenges in efficiency, lifetime, safety, and material optimization. In this context, the objective of this research is to develop a predictive model based on Deep deep-Learning learning techniques. Based on Deep Learning techniques that combine Transformer and Physicsphysics-Informed informed approaches for the optimization and design of electrochemical parameters that improve the performance of lithium batteries. Also, we present a training database consisting of three key components: numerical simulation using the Doyle–Fuller–Newman (DFN) mathematical model, experimentation with a lithium half-cell configured with a zinc oxide anode, and a set of commercial battery discharge curves using electronic monitoring. The results show that the developed Transformer–Physics physics-Informed informed model can effectively integrate deep deep-learning DNF to make predictions of the electrochemical behavior of lithium-ion batteries. The model can estimate the battery battery-charge capacity with an average error of 2.5% concerning the experimental data. In addition, it was observed that the Transformer could explore new electrochemical parameters that allow the evaluation of the behavior of batteries without requiring invasive analysis of their internal structure. This suggests that the Transformer model can assess and optimize lithium-ion battery performance in various applications, which could significantly impact the battery industry and its use in Electric Vehicles vehicles (EVs). Full article

The increasing demand for electricity and the environmental challenges associated with traditional fossil fuel-based power generation have accelerated the global transition to renewable energy sources. While renewable energy offers significant advantages, including low carbon emissions and sustainability, its inherent variability and intermittency create challenges for grid stability and energy management. This study contributes to addressing these challenges by developing an AI-driven power consumption forecasting system. The core of the proposed system is a multi-cluster long short-term memory model (MC-LSTM), which combines k-means clustering with LSTM neural networks to enhance forecasting accuracy. The MC-LSTM model achieved an overall prediction accuracy of 97.93%, enabling dynamic, real-time demand-side energy management. Furthermore, to validate its effectiveness, the system integrates vehicle-to-grid technology and reused energy storage systems as external energy sources. A real-world demonstration was conducted in a commercial building on Jeju Island, where the AI-driven system successfully reduced total energy consumption by 21.3% through optimized peak shaving and load balancing. The proposed system provides a practical framework for enhancing grid stability, optimizing energy distribution, and reducing dependence on centralized power systems. Full article

Metal-ion capacitors (MICs) have emerged as advanced hybrid energy storage devices that combine the high energy density of batteries with the superior power density and long cycle life of supercapacitors. By leveraging a unique configuration of faradaic and non-faradaic energy storage mechanisms, MICs offer a balanced performance that meets the diverse requirements of modern applications, including renewable energy systems, electric vehicles, and portable electronics. MICs employ diverse ions such as lithium, sodium, and potassium, which provide flexibility in material selection, scalability, and cost-effectiveness. For instance, lithium-ion capacitors (LICs) excel in compact and high-performance applications, while sodium-ion (NICs) and potassium-ion capacitors (KICs) provide sustainable and affordable solutions for large-scale energy storage. This review highlights the advancements in electrode materials, including carbon-based materials, transition metal oxides, and emerging candidates like MXenes and metal–organic frameworks (MOFs), which enhance MIC performance. The role of electrolytes, ranging from organic and aqueous to hybrid and solid-state systems, is also examined, emphasizing their influence on energy density, safety, and operating voltage. Additionally, the article discusses the environmental and economic benefits of MICs, including the use of earth-abundant materials and bio-derived carbons, which align with global sustainability goals. The review concludes with an analysis of practical applications, commercialization challenges, and future research directions, including AI-driven material discovery and integration into decentralized energy systems. As versatile and transformative energy storage devices, MICs are poised to play a critical role in advancing sustainable and efficient energy solutions for the future. Full article

Amid concerns about environmental degradation and the consumption of non-renewable energy, the development of electric vehicles (EVs) has accelerated, with increasing focus on safety. On the road, battery packs are exposed to potential risks from unforeseen objects that may collide with or scratch the system, which may lead to damage or even explosions, thus endangering the safety of transportation participants. In this study, several predictive models aimed at assessing the safety performances of battery packs are proposed to provide a basis for data-driven structural optimization by numerically simulating the deformation of the battery base plate. Initially, a finite element model of the battery pack was developed, and the accuracy of the model was verified by performing modal analysis with various commercial software tools. Then, representative samples were collected using optimal Latin hypercube sampling, followed by collision simulations to gather data under different collision conditions. Next, the prediction accuracy of three models—PSO-BP neural network, RIME-BP neural network, and RBF neural network—was compared for predicting battery pack bottom shell deformation. Finally, the prediction accuracy of the models was compared based on error functions. The results indicate that these neural network models can accurately predict deformation under frontal collision conditions within the specified limits, with the RIME-BP model yielding the best performance beyond those limits. The developed neural network prediction model is able to accurately assess the mechanical response of battery packs under frontal collision, providing support for data-driven structural optimization. It also provides an important reference for improving the safety and durability of battery pack design. Full article

With the growing market of secondary batteries for electric vehicles (EVs) and grid-scale energy storage systems (ESS), driven by environmental challenges, the commercialization of sodium-ion batteries (SIBs) has emerged to address the high price of lithium resources used in lithium-ion batteries (LIBs). However, achieving competitive energy densities of SIBs to LIBs remains challenging due to the absence of high-capacity anodes in SIBs such as the group-14 elements, Si or Ge, which are highly abundant in LIBs. This review presents potential candidates in metal pnictogenides as promising anode materials for SIBs to overcome the energy density bottleneck. The sodium-ion storage mechanisms and electrochemical performance across various compositions and intrinsic physical and chemical properties of pnictogenide have been summarized. By correlating these properties, strategic frameworks for designing advanced anode materials for next-generation SIBs were suggested. The trade-off relation in pnictogenides between the high specific capacities and the failure mechanism due to large volume expansion has been considered in this paper to address the current issues. This review covers several emerging strategies focused on improving both high reversible capacity and cycle stability. Full article

To analyze the safety behavior of electric vehicles, mechanical simulation models of their battery cells are essential. To ensure computational efficiency, the heterogeneous cell structure is represented by homogenized material models. The required parameters are calibrated against several characteristic cell experiments. As a result, it is hardly possible to describe the behavior of the individual battery components, which reduces the level of detail. In this work, a new data-driven material model is presented, which not only provides the homogenized behavior but also information about the components. For this purpose, a representative volume element (RVE) of the cell structure is created. To determine the constitutive material models of the individual components, different characterization tests are performed. A novel method for carrying out single-layer compression tests is presented for the characterization in the thickness direction. The parameterized RVE is subjected to a large number of load cases using first-order homogenization theory. This data basis is used to train an artificial neural network (ANN), which is then implemented in commercial FEA software LS-DYNA R9.3.1 and is thus available as a material model. This novel data-driven material model not only provides the stress–strain relationship, but also outputs information about the condition of the components, such as the thinning of the separator. The material model is validated against two characteristic cell experiments. A three-point-bending test and an indentation test of the cell is used for this purpose. Finally, the influence of the architecture of the neural network on the computational effort is discussed. Full article

For electrically driven commercial vehicles equipped with three-speed automatic mechanical transmission (AMT), the transmission control unit (TCU) without vehicle mass and road gradient estimation function will lead to frequent shifting and insufficient power during vehicle full-load or grade climbing. Therefore, it is necessary to estimate the mass and road gradient for the electrically driven commercial vehicles equipped with the three-speed AMT, and to adjust the shift rule according to the estimation results. Given the above problems, this paper focuses on the control and development of the electrically driven three-speed AMT and takes the shift controller with the vehicle mass and road gradient estimation as the research goal. The mathematical model and simulation model of vehicle dynamics are established to verify the shift function of TCU. The least squares method and calibration techniques are applied to estimate the vehicle mass and road gradient. According to the estimation results, the existing shift strategy is optimized, and the software-in-the-loop simulation of the transmission controller is carried out to verify the function of the control algorithm software. The hardware-in-the-loop test model is established to verify the shift strategy’s optimization effect, which shortens the controller’s forward development cycle. According to the estimation results of mass and gradient, the error result of the proposed method is controlled within 4.5% for mass and 8.6% for gradient. The experiment verifies that the optimized shift strategy can effectively improve the dynamic performance of the vehicle. The HIL experimental results show that the vehicle can maintain low gear while climbing the hill, and the vehicle speed does not decrease significantly. Full article

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful. Full article

Fighting climate change has become a major task worldwide. One of the key energy sectors to emit greenhouse gases is transportation. Therefore, long term strategies all over the world have been set up to reduce on-road combustion emissions. In this context, the road freight sector faces significant challenges in decarbonization, driven by its limited availability of low-emission fuels and commercialized zero-emission vehicles compared with its high energy demand. In this work, we develop the Mobility and Energy Transportation Analysis (META) Model, a python-based optimization model to quantify the impact of transportation projected policies on freight transport by projecting conventional and alternative fuel technologies market acceptance as well as greenhouse gas (GHG) emissions. Along with introducing e-fuels as an alternative refueling option for conventional vehicles, META investigates the market opportunities of Mobile Carbon Capture (MCC) until 2050. To accurately assess this technology, a techno-economic analysis is essential to compare MCC abatement cost to alternative decarbonization technologies such as electric trucks. The novelty of this work comes from the detailed cost categories taken into consideration in the analysis, including intangible costs associated with heavy-duty technologies, such as recharging/refueling time, cargo capacity limitations, and consumer acceptance towards emerging technologies across different regions. Based on the study results, the competitive total cost of ownership (TCO) and marginal abatement cost (MAC) values of MCC make it an economically promising alternative option to decarbonize the freight transport sector. Both in the KSA and EU, MCC options could reach greater than 50% market shares of all ICE vehicle sales, equivalent to a combined 35% of all new sales shares by 2035. Full article