Search Results (1,822)

This study presents an integrated machine learning framework to evaluate the aging states of lithium-ion batteries and to classify them according to their second-life application potential. The methodology combines two key components: a set of regression models to estimate critical health indicators, such as capacity and internal resistance, and a classification stage to group batteries based on these parameters. The proposed models were trained and validated using the NASA Battery Aging Datasets. Through an in-depth analysis of environmental conditions, the study identifies their influence on aging metrics, reinforcing the relevance of the input features selected. Furthermore, a clustering-based approach was employed to validate the classification performance and to reveal the link between a battery’s operation and its aging in the Euclidean space. The results show accurate predictions without signs of overfitting or underfitting, and the classification framework proved robust across the evaluated cases. This suggests that the proposed method can serve as a scalable and adaptable tool to guide battery repurposing strategies. Overall, the findings contribute to bridging the gap between battery diagnostics and real-world energy storage applications, offering practical insights to optimize second-life deployment. Full article

Electric vehicles (EVs) provide a feasible solution for the electrification of the transportation sector. However, the large-scale deployment of EVs over wide working conditions is limited by the temperature sensitivity of lithium-ion batteries (LIBs). Therefore, an efficient and reliable battery thermal management system (BTMS) becomes essential to achieve precise temperature control of batteries and prevent potential thermal runaway. Owing to their high heat-transfer efficiency and controllability, liquid-based cooling technologies have become a key research focus in the field of BTMS. In both design and operation, BTMSs are required to comprehensively consider thermal characteristics, energy consumption, economics, and environmental impact, which demands more scientific and rational evaluation criteria. This paper reviews the latest research progress on liquid-based cooling technologies, with a focus on indirect-contact and direct-contact cooling. In addition, existing evaluation methods are summarized. This work proposes insights for future research on liquid-cooled BTMS development in EVs. Full article

Ensuring the safety, reliability, and longevity of Lithium-ion (Li-ion) batteries is crucial for sustainable integration of Electric Vehicles (EVs) within Intelligent Transportation Systems (ITSs). However, thermal stress and degradation-induced anomalies can cause sudden performance failures, posing critical operational and safety risks. Capturing complex, non-linear, and high-dimensional patterns remains challenging for traditional Machine Learning (ML) models. We propose a hybrid anomaly detection method that incorporates a Variational Quantum Neural Network (VQNN), which uses the principles of quantum mechanics, such as superposition, entanglement, and parallelism, to learn complex non-linear patterns. The VQNN is integrated with Isolation Forest (IF) and a Median Absolute Deviation (MAD)-based spike characterisation method to form a Quantum Anomaly Detector (QAD). This method distinguishes between normal and anomalous spikes in battery behaviour. Using an Arrhenius-based model, we simulate how the State of Health (SoH) and voltage of a Li-ion battery reduce as temperatures increase. We perform experiments on NASA battery datasets and detect abnormal spikes in 14 out of 168 cycles, corresponding to $8.3$% of the cycles. The QAD achieves the highest Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of $0.9820$, outperforming the baseline IF model by $7.78\%$. We use ML to predict the SoH and voltage changes when the temperature varies. Gradient Boosting (GB) achieves a voltage Mean Squared Error (MSE) of $0.001425$, while Support Vector Regression (SVR) achieves the highest $R^2$ score of $0.9343$. These results demonstrate that Quantum Machine Learning (QML) can be applied for anomaly detection in Battery Management Systems (BMSs) within intelligent transportation ecosystems and could enable EVs to autonomously adapt their routing and schedule preventative maintenance. With these capabilities, safety will be improved, downtime minimised, and public confidence in sustainable transport technologies increased. Full article

This paper presents a novel electro-thermal modeling approach for a lithium-ion battery pack in an electric vehicle (EV), along with parameter identification using controller area network (CAN) data collected from chassis dynamometer and real-world driving tests. The proposed electro-thermal model consists of a first-order equivalent circuit model (ECM) and a lumped-parameter thermal network in considering a simplified cooling circuit layout and temperature distributions across four distinct zones within the battery pack. This model captures the nonuniform heat transfer between the pack modules and the coolant, as well as variations in coolant temperature and flow rates. Model parameters are identified directly from vehicle-level test data without relying on laboratory-level measurements. Validation results demonstrate that the model can predict terminal voltage with an RMSE of less than 6 V (normalized root mean square error of less than 2%), and battery module surface temperatures with root mean square errors of less than 2 °C for over 90% of the test cases. The proposed approach provides a cost-effective and accurate solution for predicting electro-thermal behavior of EV battery systems, making it a valuable tool for battery design and management to optimize performance and ensure the safety of EVs. Full article

This paper systematically examines the key developmental stages of China’s new energy vehicle (NEV) charging and battery swapping industry, analyzing technological breakthroughs, market expansion, and policy support in each phase. The study identifies three distinct stages: the initial exploration phase (before 2014), the comprehensive deployment phase (2014–2020), and the high-quality development phase (since 2021). The industry has established a diverse energy replenishment system centered on charging infrastructure, with battery swapping serving as a complementary approach. Policy implementation has yielded significant achievements, including rapid infrastructure expansion, continuous technological upgrades, innovative business models, and improved user experiences. However, persistent challenges remain, such as insufficient standardization, unprofitable business models, and coordination barriers between stakeholders. The paper forecasts future development trajectories, including the widespread adoption of high-power charging technology, intelligent charging system upgrades, integration of Solar Power, Energy Storage, and EV Charging, diversified operational ecosystems for charging/swapping facilities, deep integration of virtual power plants, and the construction of comprehensive energy stations. Policy recommendations emphasize strengthening standardization, optimizing regional coordination and subsidy mechanisms, enhancing participation in virtual power plant frameworks, promoting the interoperability of charging/swapping infrastructure, and advancing environmental sustainability through resource recycling. Full article

Model predictive control (MPC) is a commonly used online strategy for maximizing economic benefits in smart homes that integrate photovoltaic (PV) panels, electric vehicles (EVs), and battery energy storage systems (BESSs). However, prediction errors associated with PV power and load demand can lead to economic losses. Scenario-based MPC can mitigate the impact of prediction errors by computing the expected objective value of multiple stochastic scenarios. However, reducing the number of scenarios is often necessary to lower the computation burden, which in turn causes some economic loss. To achieve online operation and maximize economic benefits, this paper proposes utilizing the consensus alternating direction method of multipliers (C-ADMM) algorithm to quickly calculate the scenario-based MPC problem without reducing stochastic scenarios. First, the system layout and relevant component models of smart homes are established. Then, the stochastic scenarios of net load prediction error are generated through Monte Carlo simulation. A consensus constraint is designed about the first control action in different scenarios to decompose the scenario-based MPC problem into multiple sub-problems. This allows the original large-scale problem to be quickly solved by C-ADMM via parallel computing. The relevant results verify that increasing the number of stochastic scenarios leads to more economic benefits. Furthermore, compared with traditional MPC with or without prediction error, the results demonstrate that scenario-based MPC can effectively address the economic impact of prediction error. Full article

The use of the Industrial Internet of Things within the domain of electric vehicles signifies a paradigm shift toward advanced, integrated, and optimized transport systems. This study thoroughly investigates the pivotal role of the Industrial Internet of Things in elevating various features of electric vehicle technology, comprising predictive maintenance, vehicle connectivity, personalized user management, energy and fleet optimization, and independent functionalities. Key IIoT applications, such as Vehicle-to-Grid integration and advanced driver-assistance systems, are examined alongside case studies highlighting real-world implementations. The findings demonstrate that IIoT-enabled advanced charging stations lower charging time, while grid stabilization lowers electricity demand, boosting functional sustainability. Battery Management Systems (BMSs) prolong battery lifespan and minimize maintenance intervals. The integration of the IIoT with artificial intelligence (AI) optimizes route planning, driving behavior, and energy consumption, resulting in safer and more efficient autonomous EV operations. Various issues, such as cybersecurity, connectivity, and integration with outdated systems, are also tackled in this study, while emerging trends powered by artificial intelligence, machine learning, and emerging IIoT technologies are also deliberated. This study emphasizes the capacity for IIoT to speed up the worldwide shift to eco-friendly and smart transportation solutions by evaluating the overlap of IIoT and EVs. Full article

As electric vehicles (EVs) gain widespread adoption in industrial environments supported by Industrial Internet of Things (IIoT) smart grids technology, coordinated charging of multiple EVs has become vital for maintaining grid stability. In response to the scalability challenges faced by traditional algorithms in multi-device environments and the limitations of discrete action spaces in continuous control scenarios, this paper proposes a dynamic charging scheduling algorithm for EVs based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). The algorithm combines real-time electricity prices, battery status monitoring, and distributed sensor data to dynamically optimize charging and discharging strategies of multiple EVs in continuous action spaces. The goal is to reduce charging costs and balance grid load through coordinated multi-agent learning. Experimental results show that, compared with baseline methods, the proposed MADDPG algorithm achieves a 41.12% cost reduction over a 30-day evaluation period. Additionally, it effectively adapts to price fluctuations and user demand changes through Vehicle-to-Grid technology, optimizing charging time allocation and enhancing grid stability. Full article

The recycling and remanufacturing of end-of-life (EoL) electric vehicle (EV) batteries are urgent challenges for a circular economy. Disassembly is crucial for handling EoL EV batteries due to their inherent uncertainties and instability. The human–robot collaborative disassembly of EV batteries as a semi-automated approach has been investigated and implemented to increase flexibility and productivity. Unscrewing is one of the primary operations in EV battery disassembly. This paper presents a new method for the robotic unfastening and collecting of screws, increasing disassembly efficiency and freeing human operators from dangerous, tedious, and repetitive work. The design inspiration for this method originated from how human operators unfasten and grasp screws when disassembling objects with an electric tool, along with the fusion of multimodal perception, such as vision and touch. A robotic disassembly system for screws is introduced, which involves a collaborative robot, an electric spindle, a screw collection device, a 3D camera, a six-axis force/torque sensor, and other components. The process of robotic unfastening and collecting screws is proposed by using position and force control. Experiments were carried out to validate the proposed method. The results demonstrate that the screws in EV batteries can be automatically identified, located, unfastened, and removed, indicating potential for the proposed method in the disassembly of EoL EV batteries. Full article

The rapid growth of electric vehicles (EVs) in the Jakarta Greater Area is expected to significantly increase the volume of end-of-life (EoL) batteries, necessitating an efficient and sustainable waste management system. This study designs a reverse logistics network that includes Collection Centers (CCs), a combined Remanufacturing and Recycling Center (RMC), and a Waste Disposal Center (WDC). Dealer clusters are identified using K-means clustering to determine the optimal CC locations. A deterministic mixed-integer linear programming (MILP) model is developed to minimize total costs. It comprises acquisition, transportation, processing, facility, and carbon tax components. The model yields a minimum total cost of IDR 1,236,435,000,187, with processing costs contributing the largest share (56.68%), followed by transportation (29.30%). The selected facilities include five CCs (CCA-1, CCE-2, CCK-3, CCM-4, and CCR-5), one RMC (RMC-1), and one WDC (WDC-1). Based on battery health, the batteries are classified into three categories: L1 (>80% health, suitable for remanufacturing), L2 (60–80%, suitable for recycling), and L3 (<60%, directed to disposal). L1 and L2 batteries are directed to RMC-1, while L3 batteries and solid waste are routed to WDC-1, totaling 1.029 tons. The results emphasize the need for improving processing efficiency and strategic facility placement to enhance the sustainability and cost-effectiveness of EoL battery management in urban EV ecosystems. Full article

The mitigation of climate change impacts from the automotive sector is important for sustainable development, and for that purpose, synthetic liquid fuel vehicles (SLF-Vs) are being considered as a potential clean option alongside electric vehicles (EVs). However, the energy-intensive production of synthetic liquid fuels (SLFs) requires a thorough life-cycle analysis, as CO₂ emissions vary significantly depending on the power sources and feedstock production technologies. This study evaluates the life-cycle CO₂ emissions of SLF-Vs in Japan through long-term multiple scenarios up to 2050 and compares them with those of gasoline vehicles (GVs), hybrid electric vehicles (HEVs), and battery electric vehicles (BEVs). The results reveal that, in 2020, SLF-Vs’ life-cycle CO₂ emissions were more than 2.9 times higher than those of GVs. By 2050, SLF-Vs’ emissions could only decrease to BEV-like levels if Japan achieves significant decarbonization of its power grid. Even if hydrogen is produced via water electrolysis in Australia, where renewable energy is abundant, and then imported, emissions remain high if Japan’s power grid remains insufficiently decarbonized. This highlights the critical importance of expanding domestic decarbonized power sources, particularly renewable energy, to reduce the life-cycle CO₂ emissions of SLF-Vs in Japan. Full article

Electric vehicles (EVs) have gained significant attention in recent decades for their environmental benefits. However, their widespread adoption poses challenges due to limited charging infrastructure and long charging times, often resulting in underutilized charging stations (CSs) and unnecessary queues that complicate travel planning. Therefore, selecting the appropriate CS is essential for minimizing the total travel time of EVs, as it depends on both driving time and the required charging duration. This selection process requires estimating the energy required to reach each candidate CS and then continue to the destination, while also checking if the EV’s battery level is sufficient for a direct trip. To address this gap, we propose an integrated platform that leverages two ensemble machine learning models: Bi-LSTM + XGBoost to predict energy consumption, and FFNN + XGBoost for identifying the most suitable CS by considering required energy, waiting time at CS, charging speed, and driving time based on varying traffic conditions. This integration forms the core novelty of our system to optimize CS selection to minimize the total trip duration. This approach was validated with SUMO simulations and OpenStreetMap data, demonstrating a mean absolute error (MAE) ranging from 2.29 to 4.5 min, depending on traffic conditions, outperforming conventional approaches that rely on SUMO functions and mathematical calculations, which typically yielded MAEs between 5.1 and 10 min. These findings highlight the proposed system’s effectiveness in reducing total travel time, improving charging infrastructure utilization, and enhancing the overall experience for EV drivers. Full article

This study conducts a detailed analysis of cybersecurity threats, including artificial intelligence (AI)-driven cyber-attacks targeting vehicle-to-vehicle (V2V) and electric vehicle (EV) communications within the rapidly evolving field of connected and autonomous vehicles (CAVs). As autonomous and electric vehicles become increasingly integrated into daily life, their susceptibility to cyber threats such as replay, jamming, spoofing, and denial-of-service (DoS) attacks necessitates the development of robust cybersecurity measures. Additionally, EV-specific threats, including battery management system (BMS) exploitation and compromised charging interfaces, introduce distinct vulnerabilities requiring specialized attention. This research proposes a comprehensive and integrated cybersecurity framework that rigorously examines current V2V, vehicle-to-everything (V2X), and EV-specific systems through systematic threat assessments, vulnerability analyses, and the deployment of advanced security controls. Unlike previous state-of-the-art approaches, which primarily focus on isolated threats or specific components such as V2V protocols, the proposed framework provides a holistic cybersecurity strategy addressing the entire communication stack, EV subsystems, and incorporates AI-driven threat detection mechanisms. This comprehensive and integrated approach addresses critical gaps found in the existing literature, making it significantly more adaptable and resilient against evolving cyber-attacks. Our framework aligns with industry standards and regulatory requirements, significantly enhancing the security, safety, and reliability of modern transportation systems. By incorporating specialized cryptographic techniques, secure protocols, and continuous monitoring mechanisms, the proposed approach ensures robust protection against sophisticated cyber threats, thereby safeguarding vehicle operations and user privacy. Full article

The adoption of sustainable and environmentally friendly solutions is becoming crucial across several sectors, particularly in transportation. As part of this transition, the transport industry has turned its attention to electric vehicle (EV) development and the deployment of electric batteries. This study provides a comprehensive analysis of the performance of EV batteries, integrating both experimental measurements and simulations. The experimental section involved a series of tests conducted on real batteries under various operating conditions, focusing on different charging and discharging rates. Additionally, the IEC 62660-1 standard was applied, to evaluate their performance under realistic usage scenarios. Moreover, a theoretical model was developed in order to simulate the batteries’ behavior and replicate the observed experimental data. A comparison between the simulation outputs and experimental data was conducted, demonstrating the accuracy of the model. This work provides valuable insights into the performance of EV batteries and lays the foundation for optimization in future applications. Full article

The MA₂Z₄ family represents a class of two-dimensional materials renowned for their outstanding mechanical properties and excellent environmental stability. By means of elemental substitution, we designed two novel phases of ScSi₂N₄, namely β₁ and β₂. Their dynamical, thermal, and mechanical stabilities were thoroughly verified through phonon dispersion analysis, ab initio molecular dynamics (AIMD) simulations, and calculations of mechanical parameters such as Young’s modulus and Poisson’s ratio. Electronic structure analysis using both PBE and HSE06 methods further revealed that both the β₁ and β₂ phases exhibit metallic behavior, highlighting their potential for battery-related applications. Based on these outstanding properties, the climbing image nudged elastic band (CI-NEB) method was employed to investigate the diffusion behavior of Li, Na, and K ions on the material surfaces. Both structures demonstrate extremely low diffusion energy barriers (Li: 0.38 eV, Na: 0.22 eV, K: 0.12 eV), indicating rapid ion migration—especially for K—and excellent rate performance. The lowest barrier for K ions (0.12 eV) suggests the fastest diffusion kinetics, making it particularly suitable for high-power potassium-ion batteries. The significantly lower barrier for Na ions (0.22 eV) compared with Li (0.38 eV) implies that both β₁ and β₂ phases may be more favorable for fast-charging/discharging sodium-ion battery applications. First-principles calculations were applied to determine the open-circuit voltage (OCV) of the battery materials. The β₂ phase exhibits a higher OCV in Li/Na systems, while the β₁ phase shows more prominent voltage for K. The results demonstrate that both phases possess high theoretical capacities and suitable OCVs. Full article