Search Results (1,215)

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

Accurate parametric modeling of lithium-ion batteries is essential for battery management system (BMS) design in electric vehicles and broader energy storage applications, enabling reliable state estimation and effective thermal control under diverse operating conditions. This study presents a detailed characterization of lithium-ion cells to support advanced BMS in electric vehicles and stationary storage. A second-order equivalent circuit model is developed to capture instantaneous and dynamic voltage behavior, with parameters extracted through Hybrid Pulse Power Characterization over a broad range of temperatures (−10 °C to 45 °C) and state-of-charge levels. The method includes multi-duration pulse testing and separates ohmic and transient responses using two resistor–capacitor branches, with parameters tied to physical processes like charge transfer and diffusion. A weakly coupled electro-thermal model is presented to support real-time BMS applications, enabling accurate voltage, temperature, and heat generation prediction. This study also evaluates open-circuit voltage and direct current internal resistance across pulse durations, leading to power capability maps (“fish charts”) that capture discharge and regenerative performance across SOC and temperature. The analysis highlights performance asymmetries between charging and discharging and confirms model accuracy through curve fitting across test conditions. These contributions enhance model realism, thermal control, and power estimation for real-world lithium-ion battery applications. Full article

The global rise of electric vehicles (EVs) is reshaping the automotive industry, driven by a 25% increase in EV sales in 2024 and mounting regulatory pressure from European countries aiming to phase out thermal and hybrid vehicle production. In this context, the development of advanced battery technologies has become a critical priority. However, progress in electrochemical storage systems remains limited due to persistent technological barriers such as gaps in data, inadequate modeling tools, and difficulties in system integration, such as thermal management and interface instability. Safety concerns like thermal runaway and the lack of long-term performance data also hinder large-scale adoption. This study presents an in-depth analysis of lithium–ion (Li–ion) batteries, with a particular focus on evaluating their charging and discharging behaviors. To facilitate this, a series of automated experiments was conducted using a custom-built test bench equipped with MATLAB (2024b) programming and dSPACE data acquisition cards, enabling precise current and voltage measurements. The acquired data were analyzed to derive mathematical models that capture the operational characteristics of Li–ion batteries. Furthermore, various state-of-charge (SoC) estimation techniques were investigated to enhance battery efficiency and improve range management in EVs. This paper contributes to the advancement of energy storage technologies and supports global ecological goals by proposing safer and more efficient solutions for the electric mobility sector. Full article

The transition to high-energy-density lithium metal batteries (LMBs) is essential for advancing electric vehicle (EV) technologies beyond the limitations of conventional lithium-ion batteries. A key challenge in scaling LMB production is the precise, contamination-free separation of lithium metal (LiM) anodes, hindered by lithium’s strong adhesion to mechanical cutting tools. This study investigates high-speed, contactless laser cutting as a scalable alternative for shaping double-coated LiM anodes. The effects of pulse duration, pulse energy, repetition frequency, and scanning speed were systematically evaluated using a nanosecond pulsed laser system on 30 µm LiM foils laminated on both sides of an 8 µm copper current collector. A maximum single-pass cutting speed of 3.0 m/s was achieved at a line energy of 0.06667 J/mm, with successful kerf formation requiring both a minimum pulse energy (>0.4 mJ) and peak power (>2.4 kW). Cut edge analysis showed that shorter pulse durations (72 ns) significantly reduced kerf width, the heat-affected zone (HAZ), and bulge height, indicating a shift to vapor-dominated ablation, though with increased spatter due to recoil pressure. Optimal edge quality was achieved with moderate pulse durations (261–508 ns), balancing energy delivery and thermal control. These findings define critical laser parameter thresholds and process windows for the high-speed, high-fidelity cutting of double-coated LiM battery anodes, supporting the industrial adoption of nanosecond laser systems in scalable LMB electrode manufacturing. Full article

Implementing an effective energy management system (EMS) is essential for optimizing electric vehicle (EV) charging stations (EVCSs), especially when combined with battery energy storage systems (BESSs). This study analyzes a real-world EVCS scenario and compares several EMS approaches, aiming to reduce operating costs while accounting for BESS degradation. Initially, significant savings were achieved by optimizing the EV charging schedule using genetic algorithms (GAs), even without storage. Next, different BESS-based EMSs, including rule-based and fuzzy logic systems, were optimized via GAs. Finally, in a dynamic scenario with variable electricity prices and demand, the adaptive GA-optimized fuzzy logic EMS was found to achieve the best performance, reducing annual operating costs by 15.6% compared to the baseline strategy derived from real fleet data. Full article

With the rapid advancement of new energy electric vehicles, high-capacity nickel-rich layered oxides have emerged as predominant cathode materials in lithium-ion battery systems. However, their widespread implementation necessitates rigorous investigation into cycling stability. We synthesized nickel-manganese-aluminum hydroxide precursors as raw materials by co-precipitation method, and synthesized ultrathin Al₂O₃-coated LiNi_0.9Mn_0.05Al_0.05O₂ cathode materials by hydrolysis reaction. The cathode material was uniformly covered by an Al₂O₃ layer with an average thickness of 5–10 nm by high resolution transmission electron microscopy (HRTEM). Electrochemical performance tests showed that the modified cathode material exhibited significantly enhanced reversible capacity, cycling stability, and rate performance, and a more favorable differential capacity curve. In particular, the LNMA-2 samples were able to maintain 90.6% and 88.3% of their initial capacity after 100 cycle tests (with cutoff voltages of 4.3 and 4.5 V, respectively) at 0.5 C charge/discharge rate. These improved electrochemical properties are mainly attributed to the advantages offered by the unique Al₂O₃ coating structure. This study provides significant theoretical value for designing and optimizing the production of high-nickel cobalt-free cathode materials with high cycling performance. Full article

Electric vehicles (EVs) are the fastest-growing type of transport. Battery packs are a key component in EVs. Modern lithium-ion battery cells are characterized by low self-discharge current, high power density, and durability. At the same time, the battery management system (BMS) plays a pivotal role in ensuring high efficiency and durability of battery cells and packs. The BMS monitors and controls the battery charge and discharge to ensure EV safety and optimum operation. This paper is devoted to analyzing BMS circuitry configurations and algorithms. The analysis includes circuit solutions and algorithms for implementing the main BMS functions, such as parameter monitoring, protection, cell balancing, state estimation, charging and discharging management, communication, and data logging. The paper provides insights into the recent research literature on BMS, and the advantages and disadvantages of methods for implementing BMS functions are compared. The paper also discusses the application of artificial intelligence technologies and aspects of further work on next-generation BMS technologies. Full article

With the widespread use of lithium-ion batteries in electric vehicles, energy storage systems, and portable electronic devices, concerns regarding their thermal runaway have escalated, raising significant safety issues. Despite advances in existing thermal management technologies, challenges remain in addressing the complexity and variability of battery thermal runaway. These challenges include the limited heat dissipation capability of passive thermal management, the high energy consumption of active thermal management, and the ongoing optimization of material improvement methods. This paper systematically examines the mechanisms through which three main triggers—mechanical abuse, thermal abuse, and electrical abuse—affect the thermal runaway of lithium-ion batteries. It also reviews the advantages and limitations of passive and active thermal management techniques, battery management systems, and material improvement strategies for enhancing the thermal stability of batteries. Additionally, a comparison of the principles, characteristics, and innovative examples of various thermal management technologies is provided in tabular form. The study aims to offer a theoretical foundation and practical guidance for optimizing lithium-ion battery thermal management technologies, thereby promoting their development for high-safety and high-reliability applications. Full article

The increasing depletion of fossil fuels and their environmental impact have led to the development of fuel cell hybrid electric vehicles. By combining fuel cells with batteries, these vehicles offer greater efficiency and zero emissions. However, their energy management remains a challenge requiring advanced strategies. This paper presents a comparative study of two developed energy management strategies: a deterministic rule-based approach and a fuzzy logic approach. The proposed system consists of a proton exchange membrane fuel cell (PEMFC) as the primary energy source and a lithium-ion battery as the secondary source. A comprehensive model of the hybrid powertrain is developed to evaluate energy distribution and system behaviour. The control system includes a model predictive control (MPC) method for fuel cell current regulation and a PI controller to maintain DC bus voltage stability. The proposed strategies are evaluated under standard driving cycles (UDDS and NEDC) using a simulation in MATLAB/Simulink. Key performance indicators such as fuel efficiency, hydrogen consumption, battery state-of-charge, and voltage stability are examined to assess the effectiveness of each approach. Simulation results demonstrate that the deterministic strategy offers a structured and computationally efficient solution, while the fuzzy logic approach provides greater adaptability to dynamic driving conditions, leading to improved overall energy efficiency. These findings highlight the critical role of advanced control strategies in improving FCHEV performance and offer valuable insights for future developments in hybrid-vehicle energy management. Full article

Determining charge and discharge behavior is essential for optimizing charging strategies and evaluating balancing algorithms in battery energy storage systems and electric vehicles. Conventionally, a sequence of circuit simulations or tedious hardware tests is required to evaluate the performance of the balancing algorithm. To mitigate these problems, this paper proposes a variable capacitor model that can be easily built from the incremental capacity curve. This model provides a direct and insightful R-C time constant method for the charge/discharge time calculation. After validating the model accuracy by experimental results based on the cylindrical lithium-ion cell test, a switched-capacitor active balancing and a passive cell balancing circuit are implemented to further verify the effectiveness of the proposed model in calculating the cell balancing time within 2% error. Full article

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

This study investigates a novel heat pipe integrated cooling system designed for thermal management of Tesla’s 4680 cylindrical lithium–ion batteries in electric vehicles (EVs). Through a comprehensive approach combining experimental analysis, 1-D AMESim simulations, and 3-D Computational Fluid Dynamics (CFD) modeling, the thermal performance of various wick structures and working fluid filling ratios was evaluated. The experimental setup utilized a triangular prism chamber housing three surrogate heater blocks to replicate the heat generation of 4680 cells under 1C, 2C, and 3C discharge rates. Results demonstrated that a blended fabric wick with a crown-shaped design (Wick 5) at a 30–40% filling ratio achieved the lowest maximum temperature (T_max of 47.0 °C), minimal surface temperature deviation (ΔT_surface of 2.8 °C), and optimal thermal resistance (R_th of 0.27 °C/W) under 85 W heat input. CFD simulations validated experimental findings, confirming stable evaporation–condensation circulation at a 40% filling ratio, while identifying thermal limits at high heat loads (155 W). The proposed hybrid battery thermal management system (BTMS) offers significant potential for enhancing the performance and safety of high-energy density EV batteries. This research provides a foundation for optimizing thermal management in next-generation electric vehicles. 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

The transition to electric mobility has significantly increased the demand for lithium-ion batteries, raising concerns about their end-of-life management. Therefore, this study presents the design, development and first implementation steps of a stationary energy storage system utilizing second-life electric vehicle (EV) batteries. These batteries, no longer suitable for traction applications due to a reduced state of health (SoH) below 80%, retain sufficient capacity for less demanding stationary applications. The proposed system is designed to be flexible and scalable, serving both research and commercial purposes. Key challenges include heterogeneous battery characteristics, safety considerations due to increased internal resistance and battery aging, and the need for flexible power electronics. An optimized dual active bridge (DAB) converter topology is introduced to connect several batteries in parallel and to ensure efficient bidirectional power flow over a wide voltage range. A first prototype, rated at 50 $\mathrm{k}$$\mathrm{W}$, has been built and tested in the laboratory. This study contributes to sustainable energy storage solutions by extending battery life cycles, reducing waste, and promoting economic viability for industrial partners. Full article

This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter $\alpha$. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications. Full article