Search Results (581)

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article

In this work, graphite anodes and lithium iron phosphate (LFP) cathodes are used to examine the effects of sodium hexafluorophosphate (NaPF₆) and potassium hexafluorophosphate (KPF₆) electrolyte additives on the formation of the solid electrolyte interphase and the performance of lithium-ion batteries in both half-cell and full-cell designs. The objective is to assess whether these additives may increase cycle performance, decrease irreversible capacity loss, and improve interfacial stability. Compared to the control electrolyte (1.22 M Lithium hexafluorophosphate (LiPF₆)), cells with NaPF₆ and KPF₆ additives produced less SEI products, which decreased irreversible capacity loss and enhanced initial coulombic efficiency. Following the formation of the solid electrolyte interphase, the specific capacity of the control cell was 607 mA·h/g, with 177 mA·h/g irreversible capacity loss. In contrast, irreversible capacity loss was reduced by 38.98% and 37.85% in cells containing KPF₆ and NaPF₆ additives, respectively. In full cell cycling, a considerable improvement in capacity retention was achieved by adding NaPF₆ and KPF₆. The electrolyte, including NaPF₆, maintained 67.39% greater capacity than the LiPF₆ baseline after 20 cycles, whereas the electrolyte with KPF₆ demonstrated a 30.43% improvement, indicating the positive impacts of these additions. X-ray photoelectron spectroscopy verified that sodium (Na⁺) and potassium (K⁺) ions were present in the SEI of samples containing NaPF₆ and KPF₆. While K⁺ did not intercalate in LFP, cyclic voltammetry confirmed that Na⁺ intercalated into LFP with negligible impact on the energy storage of full cells. These findings demonstrate that NaPF₆ and KPF₆ are suitable additions for enhancing lithium-ion battery performance in the popular artificial graphite/LFP system. Full article

Fast charging accelerates lithium-ion battery operation but increases the risk of lithium (Li) plating—a process that undermines efficiency, longevity, and safety. Here, we introduce a predictive modeling framework that captures the onset and severity of Li plating under practical fast-charging conditions. By integrating an empirically parameterized SOC threshold model with time-dependent kinetic simulations and Arrhenius based thermal analysis, we delineate operating regimes prone to irreversible Li accumulation. The framework distinguishes reversible and irreversible plating fractions, quantifies energy losses, and identifies a critical activation energy (0.25 eV) associated with surface-limited deposition. Visualizations in the form of severity maps and voltage-zone risk classifications enable direct application to battery management systems. This approach bridges electrochemical degradation modeling with real-time charge protocol design, offering a practical tool for safe, high-performance battery operation. Full article

In this paper, a detailed review of electric vehicle (EV) charging station architectures is first presented, and then an optimal architecture suitable for a large MW-scale EV fast-charging station (EVFS) with multiple fast chargers is proposed and evaluated. The study examines various EVFS architectures, including those currently deployed in commercial sites. Most EVFS implementations use either a common AC-bus or a common DC-bus configuration, with DC-bus architectures being slightly more predominant. The paper analyzes the EV charging and battery energy storage system (BESS) requirements for future large-scale EVFSs and identifies key implementation challenges associated with the full adoption of the common DC-bus approach. To overcome these limitations, a novel multi-zone EVFS architecture is proposed that employs an optimal combination of isolated and non-isolated DC-DC converter topologies while maintaining galvanic isolation for EVs. The system efficiency and total power converter capacity requirements of the proposed architecture are evaluated and compared with those of other EVFS models. A major feature of the proposed design is its multi-zone division and zonal isolation capabilities, which are not present in conventional EVFS architectures. These advantages are demonstrated through a scaled-up model consisting of 156 EV fast chargers. The analysis highlights the superior performance of the proposed multi-zone EVFS architecture in terms of efficiency, total power converter requirements, fault tolerance, and reduced grid impacts, making it the best solution for reliable and scalable MW-scale commercial EVFS systems of the future. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

This paper presents a hybrid fast charging strategy for static wireless power transfer (WPT) systems that synergistically combines pulsed current and multi-stage current (MCM) modes to enable rapid yet battery-health-conscious electric vehicle (EV) charging, thereby promoting sustainable transportation. The proposed approach employs a memetic algorithm (MA) to dynamically optimize the charging parameters, achieving an optimal balance between speed and battery longevity while maintaining 90.78% system efficiency at the SAE J2954-standard 85 kHz operating frequency. A neural-network-based state of charge (SOC) estimator provides accurate real-time monitoring, complemented by MA-tuned PI control for enhanced resonance stability and adaptive pulsed current–MCM profiles for the optimal energy transfer. Simulations and experimental validation demonstrate faster charging compared to that using the conventional constant current–constant voltage (CC-CV) methods while effectively preserving the battery’s state of health (SOH)—a critical advantage that reduces the environmental impact of frequent battery replacements and minimizes the carbon footprint associated with raw material extraction and battery manufacturing. By addressing both the technical challenges of high-power WPT systems and the ecological imperative of battery preservation, this research bridges the gap between fast charging requirements and sustainable EV adoption, offering a practical solution that aligns with global decarbonization goals through optimized resource utilization and an extended battery service life. Full article

Lithium-polymer (LiPo) batteries are valued for their high energy density, stable voltage output, low self-discharge, and strong reliability, making them a popular choice for high-performance and portable applications. Despite these advantages, the charging behavior of LiPo batteries—especially during rapid charging—remains an area with limited understanding. This research examines the electro-thermal characteristics of VARTA LiPo batteries when subjected to high charging currents (2C, 3C, and 4C rates). A temperature-sensitive charging model is developed to address safety and efficiency concerns during fast charging. Experimental data indicate that charging at 45 °C yields the best performance, achieving 80% state of charge (SoC) within 25 min. However, charging at temperatures above or below this level (such as 25 °C) reduces efficiency due to increased internal resistance and accelerated battery aging. The model, validated across a range of temperatures (25 °C, 35 °C, 45 °C, and 60 °C), shows that longer constant-current (CC) charging phases at higher temperatures are associated with lower internal resistance. These results highlight the importance of effective thermal management for optimizing both safety and performance in LiPo battery applications. Full article

This study explores the development of aluminum-doped MgV₂O₄ spinel cathodes for aqueous zinc-ion batteries (AZIBs), addressing the challenges of poor Zn²⁺ ion diffusion and structural instability. Al³⁺ ions were pre-inserted into the spinel structure using a sol-gel method, which enhanced the material’s structural stability and electrical conductivity. The doping of Al³⁺ mitigates the electrostatic interactions between Zn²⁺ ions and the cathode, thereby improving ion diffusion and facilitating efficient charge/discharge processes. While pseudocapacitive behavior plays a dominant role in fast charge storage, the diffusion of Zn²⁺ within the bulk material remains crucial for long-term performance and stability. Our findings demonstrate that Al-MgV₂O₄ exhibits enhanced Zn²⁺ diffusion kinetics and robust structural integrity under high-rate cycling conditions, contributing to its high electrochemical performance. The Al-MgVO cathode retains a capacity of 254.3 mAh g⁻¹ at a high current density of 10 A g⁻¹ after 1000 cycles (93.6% retention), and 186.8 mAh g⁻¹ at 20 A g⁻¹ after 2000 cycles (90.2% retention). These improvements, driven by enhanced bulk diffusion and the stabilization of the crystal framework through Al³⁺ doping, make it a promising candidate for high-rate energy storage applications. Full article

In this study, we systematically analyzed selective lithium plating on graphite (Gr)–silicon (Si) composite anodes for lithium-ion batteries during fast charging, using electrochemical techniques. To achieve this, half-cells were first constructed with single Gr and Si electrodes, and lithium plating on each electrode was examined at different charging rates. It was observed that lithium plating on both electrodes began at a lower state of charge (SoC) as the charge rate increased. Furthermore, at a given charge rate, lithium plating occurred on the Si electrode at a lower SoC than on the Gr electrode. Based on the experimental findings, the lithium plating behavior of Gr and Si as a function of the charge rate was formulated to investigate the plating behavior of hypothetical composite electrodes with varying Gr–Si ratios. The lithium plating behavior observed on the actual composite electrode was consistent with that predicted from the hypothetical composite electrode, which was simulated using the same Gr–Si ratio based on the behaviors of the individual electrodes. By comparing the results from the single and composite electrodes, it is proposed that lithium plating occurs first on Si and then on Gr at low charge rates, whereas, at high charge rates, it proceeds first on Gr and then on Si. We discuss how to extrapolate the preferential plating signals—namely, plating onto Si at low charge rates and onto Gr at high charge rates—that are not directly evident in the signal from the actual composite electrode. Full article

The utilization of electric vehicles (EVs) has grown significantly and continuously in recent years, encouraging the creation of new implementation opportunities. The battery electric vehicle (BEV) charging system can be effectively used during peak load periods, for voltage regulation, and for the improvement of power system stability within the smart grid. It provides an efficient bidirectional interface for charging the battery from the grid and discharging the battery into the grid. These two operation modes are referred to as grid-to-vehicle (G2V) and vehicle-to-grid (V2G), respectively. The management of power flow in both directions is highly complex and sensitive, which requires employing a robust control scheme. In this paper, an Integral Fast Terminal Synergetic Control Scheme (IFTSC) is designed to control the BEV charger system through accurately tracking the required current and voltage in both G2V and V2G system modes. Moreover, the Black-Winged Kite Algorithm is introduced to select the optimal gains of the proposed IFTS control scheme. The system stability is checked using the Lyapunov stability method. Comprehensive simulations using MATLAB/Simulink are conducted to assess the safety and efficacy of the suggested optimal IFTSC in comparison with IFTSC, optimal integral synergetic, and conventional PID controllers. Furthermore, processor-in-the-loop (PIL) co-simulation is carried out for the studied system using the C2000 launchxl-f28379d digital signal processing (DSP) board to confirm the practicability and effectiveness of the proposed OIFTS. The analysis of the obtained quantitative comparison proves that the proposed optimal IFTSC provides higher control performance under several critical testing scenarios. Full article

Batteries have become the main power source in today’s automotive systems. This paper proposes the design of a fast-balancing passive battery management system (BMS) with remote monitoring for the automotive domain. This system is designed for four series-connected lithium iron phosphate (LiFePO4) cells, which are the preferred choice in the automotive industry. The results show that the proposed BMS can monitor the cell voltages with an error lower than 0.12%, and it can perform the balancing operation successfully with maximum currents of 750 mA during both charging and discharging cycles, not only for LiFePO4 cells, but also for lithium-ion (Li-ion) cells. Furthermore, the cell voltages are sent over the controller area network (CAN) interface for remote monitoring. Full article

Lithium-titanate-oxide batteries can reduce the long charging time of electric vehicles by offering fast charging capabilities. However, high charging currents require an accurate estimation of battery internal state to prevent early aging of the battery and dangerous situations. An accurate algorithm based on neural networks for the co-estimation of state of charge, internal resistance, and capacity state of health is proposed in this work. The algorithm is trained with synthetic data generated by an electric vehicle simulation platform running seven different standard driving cycles at various settings. The algorithm is then validated using an additional standard driving cycle, achieving, for state of charge, internal resistance, and capacity state of health, a root mean square error lower than 2%, 80 μΩ, and 2.9%, and a mean absolute percentage error lower than 3.4%, 4.4%, and 3.3%, respectively. The results obtained and the comparison with literature works indicate that the co-estimation algorithm proposed is able to estimate the considered quantities with very good accuracy. Full article

In this study, we couple precise interface engineering via alternating current electrophoretic deposition (AC–EPD) with performance-enhancing structural transformation via annealing, enabling the development of high-performance, stable, and tunable Mn-based cathodes for aqueous zinc-ion batteries (ZIBs). Using AC–EPD to fabricate Mn(BTC) (BTC = 1,3,5-benzenetricarboxylic acid) cathodes followed by thermal annealing to synthesize MOF-derived Mn₃O₄ offers a synergistic approach that addresses several key challenges in aqueous ZIB systems. The Mn₃O₄ cathode prepared via AC–EPD from Mn(BTC) exhibited a remarkable specific capacity of up to 430 mAh/g at a current density of 200 mA/g. Interestingly, the capacity continued to increase progressively with cycling, suggesting dynamic structural or interfacial changes that improved Zn²⁺ transport and utilization over time. Such capacity enhancement behavior during prolonged cycling at elevated rates has not been observed in previously reported Mn₃O₄-based ZIB systems. Kinetic analysis further revealed that the charge storage process is predominantly governed by diffusion-controlled mechanisms. This behavior can be attributed to the intrinsic characteristics of the Mn₃O₄ phase formed from the MOF precursor, where the bulk redox reactions involving Zn²⁺ insertion require ion migration into the electrode interior. Even though the electrode was processed as an ultrathin film with enhanced electrolyte contact, the charge storage remains limited by solid-state ion diffusion rather than fast surface-driven reactions, reinforcing the diffusion-dominant nature of the system. Full article

The Dual Active Bridge converter (DABC), known for its bidirectional power transfer capability and high efficiency, plays a crucial role in various applications, particularly in electric vehicles (EVs), where it facilitates energy storage, battery charging, and grid integration. The Dual Active Bridge Converter (DABC), when paired with a high-performance CLLC filter, is well-regarded for its ability to transfer power bidirectionally with high efficiency, making it valuable across a range of energy applications. While these features make the DABC highly efficient, they also complicate controller design due to nonlinear behavior, fast switching, and sensitivity to component variations. We have used a Fractional-order PID (FOPID) controller to benefit from the simple structure of classical PID controllers with lower complexity and improved flexibility because of additional filtering gains adopted in this method. However, for a FOPID controller to operate effectively under real-time conditions, its parameters must adapt continuously to changes in the system. To achieve this adaptability, a Multi-Agent Reinforcement Learning (MARL) approach is adopted, where each gain of the controller is tuned individually using the Deep Deterministic Policy Gradient (DDPG) algorithm. This structure enhances the controller’s ability to respond to external disturbances with greater robustness and adaptability. Meanwhile, finding the best initial gains in the RL structure can decrease the overall efficiency and tracking performance of the controller. To overcome this issue, Grey Wolf Optimization (GWO) algorithm is proposed to identify the most suitable initial gains for each agent, providing faster adaptation and consistent performance during the training process. The complete approach is tested using a Hardware-in-the-Loop (HIL) platform, where results confirm accurate voltage control and resilient dynamic behavior under practical conditions. In addition, the controller’s performance was validated under a battery management scenario where the DAB converter interacts with a nonlinear lithium-ion battery. The controller successfully regulated the State of Charge (SOC) through automated charging and discharging transitions, demonstrating its real-time adaptability for BMS-integrated EV systems. Consequently, the proposed MARL-FOPID controller reported better disturbance-rejection performance in different working cases compared to other conventional methods. Full article