Journal of Power Sources最新文献

Developing ultra-stable electrocatalysts for highly efficient overall water splitting at high current density (HCD) is critical for renewable hydrogen/oxygen production in the industry. However, the most active electrocatalysts for large current-driven water splitting are seriously handicapped by insufficient electrical contact kinetics due to the intensive bubble overflow. Herein, we demonstrate the ultra-stable trimetallic phosphides of NiFeP/NiCoP catalysts on a hydrophilic Ni foam skeleton via a corrosion-hydrothermal-phosphating strategy. The optimized NiFeP/NiCoP catalyst stabilizes for 600 h at −1 A cm⁻² for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in alkaline solution, and it only needs low overpotentials of 237 and 314 mV to drive HER and OER at 1 A cm⁻², respectively. As expected, the optimized NiFeP/NiCoP electrode maintains 1000 h at 0.5 A cm⁻² for water splitting, ranking among the top performers among reported catalysts. Such excellent performance could be attributed to the fast electron transfer for electrochemical reactions, the electron-deficient Fe/Ni sites contribute to forming robust metal oxyhydroxide during OER, and electron-rich Co sites facilitate H adsorption during HER. The findings present a highly promising candidate for ultra-stable non-noble metal electrocatalysts, offering a viable option for hydrogen/oxygen supply for fuel cells and metal-air batteries.

Lithium-ion batteries exhibit a complex, nonlinear and dynamic voltage behaviour. Modelling their slow dynamics is a challenge due to the multiple potential causes involved. We present here a neural equivalent circuit model for lithium-ion batteries including slow voltage dynamics. The model uses an equivalent circuit with voltage source, series resistor, and diffusion element. The series resistance is parameterized using neural networks. The diffusion element is based on a discretized form of Fickian diffusion, parameterized using a neural network and learnable parameters. It is flexible to represent not only Warburg behaviour, but also resistor-capacitor-type dynamics. Mathematically, the resulting model is given by a differential–algebraic equation system combining ordinary and neural differential equations. Therefore, the model combines concepts of both physical theory (white-box model) and artificial intelligence (black-box model) to a combined framework (grey-box model). We apply this approach to a lithium iron phosphate based lithium-ion battery cell. The experimental voltage behaviour during constant-current cycles as well as the dynamics during pulse tests are well reproduced by the model. Only at very high and very low states of charge the simulation significantly deviates from experiments, which might result from insufficient training data in these regions.

As the demand for lithium titanate oxide (LTO) batteries increases in high-power applications, their health estimation, especially the degradation mode diagnostics, is critical for the safe and economical operation of battery systems. However, various operation and environmental conditions can alter the aging of LTO cells, with few related studies. In this study, the changes in the electrode open-circuit potential (OCP) after aging are investigated and their effects on the full-cell open-circuit voltage (OCV) are discussed. An aging-aware modified electrode model is developed to simulate the evolution of the electrode OCP and used to diagnose the degradation mode by reconstructing the full-cell OCV curve. Unlike traditional polynomial models that lack physical meaning, the proposed aging-aware modified model reveals the changes in the phase transition process by identifying the warping paths of the incremental capacity curves. Compared with using only the pristine or aged OCP curves, using the synthesized OCP curves from the proposed model improves the accuracy of reconstructing full-cell OCV curves over the full life cycle, reducing the average RMSE from 80 mV to less than 8 mV. Moreover, the proposed model improves the validity of degradation mode diagnosis and adapts to different cycling conditions and different battery samples.

The critical issue of thermal runaway events in lithium-ion batteries (LIBs) is recognized as a primary cause of battery-related accidents. Despite ongoing efforts to enhance LIB safety, challenges persist due to varying chemical compositions, states of charge, and conditions of use across different batteries. To advance battery safety and considering cost and practicality, this research introduces a novel cathode material with thermal shutdown characteristics. Incorporating thermoplastic microspheres into the cathode matrix does not compromise the cathode's structural integrity, but leads to ionic conductivity value reduction, and a consequent reduction of battery performance for the larger microsphere concentrations of 5.0 wt% and 7.5 wt%. On the other hand, the samples demonstrate a thermal shutdown behaviour, triggered by the volumetric expansion of the microspheres, effectively ceasing electrical and ionic conduction, thereby preventing thermal runaway. The cathode with low microsphere concentration, 2.5 wt% of microspheres, outperforms (155 mAh·g⁻¹, at C/8-rate) room temperature battery performance with respect to the conventional cathode and also exhibits thermal shutdown behaviour at 90 °C. The research highlights the potential of integrating expandable microspheres into cathodes for the development of safer batteries, offering a scalable and cost-effective solution compatible with existing battery technologies.

Accurate estimation of state of charge (SOC) forms the foundation of battery management systems. Although commonly used for SOC estimation, equivalent circuit models (ECMs) inadequately capture battery nonlinear dynamics and rely on SOC-open circuit voltage curves. To overcome these limitations, this paper introduces state-dependent mechanisms into ECMs, proposing a state-dependent quasi-linear parameter-varying model (SD-QLPVM). This model incorporates a quasi-linear model derived from ECMs. Crucially, it eschews traditional approaches of parameter determination through offline experiments or online adaptive methods, which are limited by their linear nature. Conversely, the parameters of the quasi-linear model are treated as time-varying and state-dependent functional parameters, calculated using radial basis function neural networks (RBF-NNs). Subsequently, state variables, such as terminal voltage, current, SOC, and temperature, are used to characterize the operation point of LIBs. By considering state variables as the inputs to the RBF-NNs, the proposed parameter determination approach enables the quasi-linear model to dynamically adjust its parameters in response to evolving battery operation points, representing battery dynamics accurately and responsively. Finally, an online SOC estimation method is developed based on the SD-QLPVM and a particle filter. The effectiveness of the proposed model and SOC estimation method is verified across various drive cycles and temperature conditions.

In this study, a novel ceramic-polyethylene (PE) composite separator is designed and achieved by a very facile preparation strategy for high-safety lithium-ion batteries (LIBs). The optimized ceramic slurry composed of Al₂O₃ and polyvinyl pyrrolidone (PVP) is coated on one side of PE separator in an economically efficient way to form the novel Al₂O₃/PVP-PE separator. It is found that the superior PVP binder can improve the adherence of heat-resistant Al₂O₃ ceramic powders on the PE substrate, which significantly enhances the thermal stability of the optimized separator. The Al₂O₃/PVP-PE separator shows negligible shrinkage at a high temperature of 180 °C after 30 min thermal treatment. In addition, the prepared Al₂O₃/PVP-PE separator shows excellent wettability with electrolyte, large porosity and high liquid storage capacity, which are quite beneficial to the ionic conductivity and charge storage kinetics. Furthermore, LIBs (LiFePO₄/graphite) assembled with the optimized Al₂O₃/PVP-PE separator shows superior electrochemical performance than LIBs assembled with bare PE separator. This study provides insight into the practical application and commercialization of novel Al₂O₃/PVP-PE separator for high-safety LIBs with improved electrochemical performance.

Aqueous zinc metal batteries (AZMBs) represent one of the most promising next-generation energy storage devices in terms of competitiveness. However, the commercialization of AZMBs has been significantly impeded by the occurrence of random dendrite growth and inevitable parasitic reactions on Zn metal anodes. In order to address this issue, we propose a solution wherein the surface of Zn foil is retouched through a replacement reaction with zincophilic and low-active Cu particles (designated as CuRZn). The CuRZn electrode exhibits exceptional corrosion resistance and enables stable Zn plating/stripping via alloying/dealloying processes. Consequently, the CuRZn||CuRZn symmetric battery exhibits stable operation for over 3200 h under 1 or 5 mA cm⁻² and 1 mA h cm⁻², and the CuRZn||Cu asymmetric battery achieves an excellent coulombic efficiency of 99.86 % under 2 mA cm⁻² and 1 mA h cm⁻² for over 2300 cycles. When coupled to an ammonium vanadate (NVO) cathode, the performance of the CuRZn||NVO full battery, which has a larger capacity (183.4 mA h g⁻¹) than that of the bare Zn||NVO full battery (94.8 mA h g⁻¹) at 2 A g⁻¹, greatly improves after 1000 cycles. This study provides a simple and efficient surface retouching strategy for Zn metal anodes to achieve high-performance AZMBs.

The electrochemical performance of supercapacitors has often overlooked the effect of surface charge on cellulose-based separators. Cellulose nanofibrils (CNFs) produced by 2,2,6,6-tetramethylpipridine-1-oxyl (TEMPO) oxidation possess a high anionic surface charge due to the presence of carboxylate groups, which could affect the transport of electrolyte ions. In this work, the surface of CNFs is modified with a cationic polyelectrolyte, namely polydiallydimethylammonium chloride (PDADMAC), to yield a nearly-zero surface charge CNF membrane derived from rice straw. The surface modification of CNFs using 20 wt% PDADMAC results in CNF-M2, with a surface charge of +5.3 mV, notable porosity (64 %), excellent electrolyte uptake (225 %), and improved ionic conductivity (5.0 mS cm⁻¹). A symmetric supercapacitor assembled with CNF-M2 as a separator, exhibits enhanced specific capacitance (185.3 F g⁻¹ at 0.1 A g⁻¹), energy density (37.1 Wh kg⁻¹ at a power density of 0.24 kW kg⁻¹), and is able to maintain 100 % capacitance retention over 10,000 cycles in 1.0 M Na₂SO₄ aqueous electrolyte solution. This surface modification leads to 1.2–1.4 times increase in energy and power densities compared to the unmodified CNF membrane. Thus, the nearly-zero surface charge of the modified CNF membrane holds promise as a separator that elevates the performance of supercapacitors.

The practical implementation of Zn-based flow batteries encounters the challenges associated with uneven deposition of Zn ions and undesirable side reactions. Here, a hybrid Zn-based electrolyte system composed of ZnBr₂, ethylene glycol (EG), H₂O and potassium gluconate (KGlu) is developed as anolyte to modulate the solvation structure and interface engineering for a sustainable Zn-based flow battery. The EG molecules could exclude water molecules outside the solvation structure, inhibiting the water-induced side reactions and Zn corrosion. Moreover, the incorporation of potassium gluconate constructs an artificial stable anionic interface for dendrite-free Zn deposition. Chemical stability and hydrogen evolution potential tests demonstrate that the activity of water molecules could be suppressed in the EG-containing hybrid electrolyte. Deposition morphologies and Zn//Zn symmetric flow battery tests also reveal that gluconate anions preferentially adsorbed on zinc anode could effectively facilitate uniform zinc deposition. As a result, the Zn-based flow battery with the proposed hybrid electrolyte delivers a stable cycling performance over 200 cycles and high reversibility with an average CE of over 97.4 % at 20 mA cm⁻², exhibiting a peak power density of 103.2 mW cm⁻². This work provides a universal electrolyte design strategy for realizing a sustainable Zn-based flow battery.

Sodium (Na) dendrite growth poses challenges such as short circuits and capacity loss, and the underlying mechanisms are unclear. Although the experiments of Na dendrite growth were reported, there are very few simulations that can assist in analyzing its morphology evolution mechanism. In addressing this challenge, we develop a phase-field model to explore the influence of current density, anisotropic strength, and Na-ion consumption on the evolution of Na dendrite morphology. It is found that higher current density promotes more dendrite growth and lateral branches. Anisotropy strength influences dendrite growth rates horizontally and vertically, with horizontal growth exceeding or equaling vertical growth, reducing short-circuit risks. Increased Na-ion consumption results in significant lateral branches, kinks, and pores in dendrite structures. These results offer valuable insights into mitigating the formation of erratic dendrites, holding significant implications for the advancement of Na-based batteries with high stability and safety.