Energy Storage最新文献

The progress of lithium-ion battery performance and production technology has promoted the rapid development of electric vehicles (EVs) for these years. However, people are still used to driving EVs in the way as traditional internal combustion engine automobiles. Cruising range, acceleration ability, charging time, and safety have become the first issues that people should consider when purchasing EVs, and most of these functions depend on the performance of the battery. Therefore, this work used a multi-factor evaluation method for the performance of lithium-ion batteries used in EVs, with the help of the fuzzy analytic hierarchy process. The article focuses on the performance of the batteries that deserve the most quantified evaluation, which are the overall performance, charging performance, discharging performance, safety performance, and the sub-factors of each performance. The criteria performance rating of EV batteries is obtained, and the performance of two distinct commercially available battery packs with different functional focuses is analyzed through a case study, which assigns the battery packs performance ratings of II and III. The factors of energy density and fast charging time are with greater weights (0.558 and 0.451, respectively), which should be the direction of key research and development of EV batteries.

As global energy demand continues to rise, the strain on energy systems is intensifying. Although the widespread adoption of energy storage technologies offers a critical solution, this paper examines the pivotal role of demand response utility programs in complementing energy storage systems. Through strategic implementation, demand response programs can effectively shape energy consumption patterns, reduce costs, and enhance the integration of cleaner, renewable energy sources. Additionally, this paper contributes to the academy by demonstrating how established utility cost–benefit tests for demand response programs can incorporate energy storage costs, assessing program benefits. The findings underscore the need for further research to optimize demand response initiatives and maximize their potential within greater adoption for energy storage.

Equivalent circuit models are widely adopted for battery modeling, yet their parameters require frequent updates due to aging-induced variations. While unit data segment (UDS)-based methods leverage operational data for parameter identification, existing approaches fail to address two critical issues: (1) the sensitivity of model accuracy to historical data utilization strategies and (2) parameter discontinuity at adjacent segment boundaries. To overcome these limitations, this study proposes a novel dual-layer genetic algorithm (GA) with a parameter interaction framework. The upper-layer GA autonomously optimizes historical data selection and initializes parameters for the first segment, while the lower-layer GA identifies parameters for subsequent segments. A boundary matrix iteration mechanism enforces parameter continuity across segments by propagating constraints iteratively. Experimental validation on Urban Dynamometer Driving Schedule (UDDS) under 25°C datasets demonstrates superior performance: Under UDDS conditions, the maximum error, mean absolute error, and RMSE are 38.6, 4.7, and 6.1 mV, respectively. These values represent improvements of 8.7%, 29.8%, and 31.4% compared to the UDS-based method; and 45.5%, 42.6%, and 45.0% compared to the Recursive Least Squares-based method. The multi-temperature validation results confirm the strong robustness of the proposed approach under disparate operating temperatures. This work advances data-driven battery modeling by resolving boundary discontinuity and reducing expert dependency in parameter identification, offering a scalable solution for cloud-based battery management systems.

Precise forecasting of lithium-ion battery State of Health (SOH) is crucial for effective prognostics and health management (PHM) to ensure safety, reliability, and optimal performance. The existing kernel functions in Gaussian process regression (GPR) exhibit limitations in capturing complex non-linear relationships within constrained datasets, resulting in suboptimal modeling of underlying data patterns and reduced forecasting efficiency. To address this gap, an innovative quantum neural network–Gaussian process regression (QNN–GPR) framework leverages quantum feature spaces to capture high-dimensional complexities and improve prediction accuracy in a three-step manner: (i) proposing three health indicators strongly correlated with SOH to characterize battery aging, (ii) designing QNN which leverages superposition and entanglement for processing multi-parametric battery information, yielding intermediate state estimations, and (iii) utilizing GPR as the subsequent stage to refine these intermediate predictions through probabilistic enhancement. Validation on four NASA battery datasets shows the QNN–GPR model attains an average 0.98% mean absolute error (MAE), indicating its superiority over conventional GPR for battery health management.

This study explores the hydrothermal synthesis of NiCo₂O₄ and Ag-doped NiCo₂O₄ (Ag_xNi_1−xCo₂O₄, x = 0.0–0.5) electrodes as cost-effective materials for supercapacitor applications. Electrochemical analysis of all fabricated materials was conducted using a half-cell configuration, with measurements performed at various scan rates. As Ag concentration increased (for x ≤ 0.4), the specific capacitance also increased, reaching a maximum performance of 1501 F g⁻¹ for x = 0.4. This represents a 2.5-fold increase compared to its pristine counterpart. However, the specific capacitance value decreased by 21% when the Ag concentration was raised from x = 0.4 to x = 0.5. Performance decline is linked to the increment of pore size (20%) and the decrement in surface area (12%) in sample x = 0.5 compared to x = 0.4, as confirmed by BET analysis. Cyclic studies over 5000 cycles demonstrated retention capacities of 94% for x = 0 and 106% for x = 0.4. The x = 0.4 sample outperformed others, exhibiting the lowest solution-resistance (R_s = 0.5 Ω) and charge transfer resistance (R_ct = 0.9 Ω). The sample with the best performance, x = 0.4, has been chosen for fabrication of a 2-electrode system in both symmetric and asymmetric designs to evaluate device-level performance. The symmetric supercapacitor (SSC) showed specific capacitance of approximately 252 F g⁻¹ at current density of 1 A g⁻¹, maintaining 93.5% of its initial capacitance after 5000 cycles at 10 A g⁻¹. It delivered an energy density of 42 W h kg⁻¹ at a power density of 549 W kg⁻¹. Meanwhile, the asymmetric supercapacitor showed specific capacitance (178 F g⁻¹ at 1 A g⁻¹), and retaining 105% capacitance after 5000 cycles at 10 A g⁻¹, with energy and power densities of 67 W h kg⁻¹, 853 W kg⁻¹, respectively. The findings from various characterization techniques are thoroughly analyzed to draw the structure–property–performance correlations and presented in detail.

In this study, the hydrogen storage capacities of activated carbons derived from sunflower stalk wastes were enhanced by initial chemical activation using different activating agents (ZnCl₂ or KOH) at biomass ratios of 1:1, 2:1, and 3:1 (w/w), followed by carbonization at varying temperatures (600°C, 700°C, 800°C, and 900°C) based on their surface area performance. The optimization and characterization of the prepared samples were systematically conducted using BET, FTIR, DTA/TG, and SEM/EDX techniques. SEM/EDX analysis revealed a marked increase in porosity and notable alterations in the elemental composition of the activated carbon surfaces as a function of the activating agent and carbonization temperature. Hydrogen storage capacities of the optimized samples were measured as a function of pressure at both room and cryogenic temperatures. As a result of the optimization process, the samples with the highest surface areas were identified as AC-Z2-700 and AC-K2-700, with AC-Z2-700 exhibiting the highest hydrogen storage performance. Storage capacities increased with rising pressure and decreasing temperature for both samples, while the isotherm profiles varied significantly between room and cryogenic conditions. The experimental data fitted well with the Henry and Freundlich isotherms at room temperature and with the Langmuir isotherm at cryogenic temperature. Furthermore, kinetic analyses indicated that the adsorption followed a pseudo-second-order model and that the dominant mechanism was intraparticle diffusion within the pores of the activated carbon. Overall, the findings demonstrate that sunflower stalk is a promising and sustainable precursor for producing high-surface area activated carbons with competitive hydrogen storage capabilities, contributing to both clean energy applications and environmental sustainability.

Supercapacitors are increasingly adopting two-dimensional (2D) carbon-intercalated transition metal chalcogenide (TMC) composites (C_xMX_1−x) due to their adjustable surface properties, makeup, and structure. Even though they show promise, we still do not know much about how the strain in the structure and changes in electronic properties from 2D carbon intercalation affect them. In this study, we prepared (rGO/g-C₃N₄)_x-(CoSe)_1−x nanocomposites using a new one-step hydrothermal method with very low (x = 0.01) and higher (x = 0.10) amounts of carbon to see how they affect lattice strain and electrochemical performance. The results of XRD and Rietveld refinement demonstrated the purity of the materials and revealed an increase in lattice size with the addition of more rGO/g-C₃N₄. Crystallite size decreased from 12.97 nm for CoSe to 9.5 nm for the (rGO/g-C₃N₄)_0.1-(CoSe)_0.90 sample due to strain introduced by carbon intercalation. TEM analysis showed nanosheet morphologies with visible rGO and g-C₃N₄ structures. XPS confirmed the Co²⁺ and Se²⁻ oxidation states and validated the presence of C–C and C–N bonds. The (rGO/g-C₃N₄)_0.1-(CoSe)_0.90 electrode exhibited a high specific capacitance of 1102 F g⁻¹ at 1 A g⁻¹ and retained 97.1% after 2000 cycles. An asymmetric device using activated carbon (AC) achieved an energy density of 53.31 Wh kg⁻¹, a power density of 750 W kg⁻¹, and 97% capacitance retention after 5000 cycles, underscoring the material's potential for durable, high-performance.

India's lithium-ion battery (LIB) ecosystem is rapidly expanding, driven by the surge in electric vehicle (EV) adoption, renewable energy integration, and portable electronics. This review critically analyzes India's LIB market dynamics, which are projected to exceed 260 GWh annual demand by 2030, up from 3 GWh in 2020. It evaluates safety challenges, including thermal runaway, BMS failures, and temperature-induced degradation under Indian climatic and road conditions. Technological strategies such as hybrid BTMS designs, advanced chemistries (LFP, NMC), and AI-integrated BMS are discussed. The paper highlights national policies like FAME-II, PLI, and battery swapping frameworks, while assessing industrial readiness, localization efforts, and recycling gaps. Unique to this work is a comparative benchmarking of Indian battery performance, manufacturing capacity (targeted at 50 GWh by 2030), and regulatory progress. This comprehensive review provides a strategic roadmap for overcoming infrastructural, environmental, and technological barriers to support India's transition toward energy resilience and sustainable battery innovation.