Battery Energy最新文献

Accurate estimation of state of charge (SoC) and maintaining balanced charge levels across secondary battery cells are crucial in battery management systems (BMSs) to extend battery life while improving the performance and thermal stability of Li-ion batteries (LIBs) in electric vehicles (EVs). However, there are still underexplored challenges associated with circulating currents in electrochemical cells during continuous operation which can overheat battery packs, reducing their life span or result in dangerous thermal runaways. This paper investigates SoC estimation using various real-world charging and discharging profiles, along with charge-balancing strategies to enhance the longevity of parallel-connected Li-ion battery cells. A newly developed hedge feedforward feedback-based gated recurrent unit with H∞ controller (HFF-GRU-H∞) is introduced to improve the SoC estimation accuracy with comparisons to nine widely-applied deep-learning algorithms. Moreover, SoC balancing for three individual battery cells is achieved using a bidirectional DC/DC power converter controlled by an H∞ robust control system during charging-discharging cycles. The experimental results indicate that SoC capacity estimation error can be reduced to 0.043%. Also, the applied optimization algorithm minimized the determination time to 0.477 s when benchmarked with existing methods leading to better charge balance among the battery cells. As a result, the overall battery pack lifespan can be extended by 27.7%, offering substantial advantages for industrial applications.

The transition from liquid to solid electrolytes is driven by the need for enhanced safety and higher energy density in advanced batteries. Solid-state electrolytes (SSEs) eliminate flammability and leakage risks but suffer from low ionic conductivity at ambient conditions due to lattice constraints and high migration barriers. Breakthroughs in SSEs materials such as Li₁₀GeP₂S₁₂ (LGPS), Li₇La₃Zr₂O₁₂ (LLZO), and Argyrodite-type Li₆PS₅Cl reveal a unique phenomenon: lithium ions exhibit “floating” behavior within a stable anionic framework, enabling quasi-fluid migration through interconnected channels. This work explores the physicochemical nature of “floating Li,” emphasizing weak interactions, multi-path coupling, and framework flexibility as key factors reducing migration barriers. We further propose an electronic-density-based approach using the interaction region indicator (IRI) to extract characteristic descriptors for high-conductivity SSEs. Comparative analysis of IRI maps across different electrolytes demonstrates distinct patterns associated with low-electron-density migration channels. These insights establish a paradigm shift from single-path models to networked migration behavior and suggest that integrating chemical bonding theory, lattice dynamics, and data-driven screening can accelerate the rational design of next-generation solid electrolytes.

Nitrogen-doped graphene aerogels (NGAs) have attracted much attention as next-generation electrode materials for supercapacitors because of their high surface area, excellent conductivity, and chemical tunability. Recent studies have confirmed how nitrogen doping can improve pseudocapacitive behaviour, wettability, and electron transport, thus significantly improving the specific capacitance, energy density, and cycling performance. This review analyses the different synthesis strategies, such as hydrothermal self-assembly, sol-gel polymerisation, and template-directed synthesis, and shows the electrochemical performance obtained from both symmetric and asymmetric set-ups. The best-performing NGAs have demonstrated specific capacitances reaching 900 F/g, energy densities of over 60 Wh/kg, and long-term retention exceeding 90% over 10,000 cycles. Nonetheless, multiple synthesis strategies are still limited by batch processing, excessive thermal demand, and difficulty with dopant homogeneity. Details on the electrode configuration and performance reported between studies are inconsistent, making direct comparisons challenging and hindering industrial translation. This review highlights the critical demand for scalable, greener synthesis protocols, standardised testing protocols, and systematic evaluations of the role of nitrogen species in capacitance enhancement. This work can be extended to dual-doping, flexible electrode fabrication, and the incorporation of the doped material into practical device architectures. Such insights provide a basis for rationally designing high-performance N-GAs for supercapacitors.

Active battery balancing is essential for maximizing the performance and safety of lithium-ion battery packs in electric vehicles and energy storage systems, yet traditional control methods struggle with nonlinear dynamics. This paper investigates the critical role of state-space design in tabular Q-learning for controlling switches of a buck-boost converter in a four-cell pack, addressing a key gap in the application of reinforcement learning to battery management systems. We propose and compare three novel discrete state representations: a coarse 11-state pairwise comparison, an intermediate 27-state hierarchical relational model, and a fine-grained 81-state individual deviation model. Through simulations across 1000 training episodes and 24 test scenarios, the 27-state model achieves superior convergence, with an average balancing time of around 41 timesteps and the lowest performance variance (σ = 12.28). Statistical analysis and state-transition graphs reveal that this optimal granularity enables hierarchical control strategies, balancing informational richness with learnability to avoid perceptual aliasing and the curse of dimensionality. These findings provide a blueprint for designing efficient RL policies in BMS, which has implications for scalable and real-time implementations in high-voltage applications.

The increased primary particle size generally leads to reduced electrochemical performance of electrode materials in Li-ion batteries. Herein, we report the simultaneous achievement of enhanced rate performance and increased particle size in spinel LiMn₂O₄ (LMO) through niobium (Nb) incorporation. After Nb incorporation, the surface energies of the (100), (110), and (111) crystal planes are significantly reduced, resulting in the formation of larger particles. Moreover, Nb doping increases the lattice parameter of the spinel structure, thereby facilitating Li⁺ transport and reducing polarization. Electrochemical tests demonstrate that the LMO cathode with 0.4 wt.% Nb delivers an initial discharge capacity of 130 mAh g⁻¹ and retains 93.9% of its capacity after 100 cycles at 1 C and 45°C.

This work elucidates the lattice dynamical origins of enhanced adsorbate–substrate interactions in oxygen-deficient Co₃O₄ via first-principles calculations. We reveal that oxygen vacancy formation induces a localized reconstruction of the phonon landscape, characterized by the emergence of high-frequency vibrational modes specifically on atoms neighboring the defect. Critically, these defect-induced modes exhibit strong spectral resonance with the vibrational centers of H₂O molecules, thereby governing the thermodynamic favorability of adsorption through a vibrational coupling mechanism. By establishing a direct correlation between local phonon redistribution and chemical reactivity, this study provides a theoretical basis for leveraging phonon engineering in the design of advanced electrode materials for energy storage applications.

For efficient battery management that ensures lifetime and dependability in applications like electric vehicles, an accurate real-time assessment of the State of Charge (SOC) and State of Health (SOH) of lithium-ion (Li-ion) batteries is essential. To overcome the difficulties presented by aging, unmodeled dynamics, and temperature fluctuations, this study attempts to create a reliable estimation method that improves the precision and robustness of SoC and SoH assessments. To maximize transient responsiveness and guarantee estimator convergence to the actual battery state, the suggested system combines a H_∞/H₂ controller with pole placement, which is built using Linear Matrix Inequality (LMI) techniques. Furthermore, this controller is complemented by a sliding mode estimator to assess SoH, which is a novel combination in battery state estimating techniques. By optimizing the disturbance matrix structure and taking into account changes in internal resistances, capacitances, and actual capacity, the H_∞/H₂ controller is designed to reduce disturbances caused by things like age and temperature fluctuations. To evaluate SoH, the sliding mode estimator makes use of state variables from the H_∞/H₂ controller. The approach is validated under real-world circumstances, including driving schedules like UDDS, US06, and HWFET, using numerical simulations that consider variations in battery internal properties. The accuracy and dependability of SOC and SOH assessments are significantly improved by the combined estimation technique. By lowering estimating errors, the controller improves resilience to disruptions. The resilience of the approach is shown by simulations conducted under a range of driving circumstances, suggesting that battery management systems might use it in practice.

Designing a PV system for self-consumption requires knowledge of power and energy demand, solar availability and hours of autonomy. The intended system should reduce electricity bill costs, supply electricity to all loads, and maintain its efficiency. Traditional calculations and designs of solar PV systems rely on one objective and may cause over dimensioning of the system. A design tool is proposed, in this paper, aiming to optimize the design of a grid connected PV systems with Battery Energy Storage System. The proposed approach tries to minimize the initial cost of the system, alleviate the degradation of panels and batteries, reduce blackout hours, reduce power purchase and reduce the wasted generation. The degradation modes in PV panels and battery systems were modeled to expand the design to increase the lifespan of the system. The tool uses a Genetic Algorithm aiming to minimize the cost function described earlier. The proposed approach helped to reduce capital and operational costs by 61.65%.

Metal components are extensively used as current collectors, anodes, and interlayers in lithium-ion batteries. Integrating these functions into one component enhances the cell's energy density and simplifies its design. However, this multifunctional component must meet stringent requirements, including high and reversible Li storage capacity, rapid lithiation/delithiation kinetics, mechanical stability, and safety. Six single-atom metals (Mg, Zn, Al, Ag, Sn, and Cu) are screened for lithiation behavior through their interaction with ion beams in electrochemically tested samples subjected to both weak and strong lithiation regimes. These different lithiation regimes allowed us to differentiate between the thermodynamics and kinetic aspects of the lithiation process. Three types of ions are used to determine Li depth profile: H⁺ for nuclear reaction analysis (NRA), He⁺ for Rutherford backscattering (RBS), and Ga⁺ for focused ion beam milling. The study reveals three lithiation behaviors: (i) Zn, Al, Sn form pure alloys with Li; (ii) Mg, Ag create intercalation solid solutions; (iii) Cu acts as a lithiation barrier. NRA and RBS offer direct and quantitative data, providing a more comprehensive understanding of the lithiation process in LIB components. These findings fit well with our ab initio simulation results, establishing a direct correlation between electrochemical features and fundamental thermodynamic parameters.

This paper proposes a novel State of Charge (SoC)-based hierarchical control strategy to ensure accurate and rapid current sharing, effective power flow management, and stable bus voltage regulation in DC shipboard microgrids (DC SMGs). The proposed control architecture introduces a multi-layered scheme encompassing energy storage units (ESUs), photovoltaic (PV) generation, and load-side coordination to achieve power balance and facilitate autonomous microgrid operation. At its core, the adaptive SoC-based current sharing (ASCS) layer ensures SoC balancing, precise load current distribution, and mitigation of line impedance effects. Complementing this, the average voltage drop restoration (AVDR) layer maintains stable and reasonable bus voltage restoration. To enhance coordination while minimizing communication overhead, a multi-agent consensus (MAC) algorithm is integrated, enabling distributed evaluation of global variables. The hierarchical framework accelerates SoC convergence, addresses balancing challenges, and improves system resilience. A comprehensive stability analysis is conducted to validate the robustness of the proposed method. Additionally, the control strategy is rigorously tested through MATLAB/Simulink simulations and validated on a Star Sim-based hardware-in-the-loop (HIL) platform, demonstrating the scheme's effectiveness, scalability, and suitability for advanced shipboard power systems.