Davi Marcelo Soares

A Python-based tool for analyzing galvanostatic charge/discharge data (GCD-γ Analyzer) in energy storage devices is presented. The software processes voltage–time data together with applied current, active electrode mass, and device type. It performs numerical integration of the voltage–time curve to determine the precise value of the stored energy, while the ideal deviceresponse is estimated under the same operational conditions. A deviation coefficient (γ) is calculated to quantify non-ideal behavior. Based on these values, realistic specific energy and power are obtained. Additionally, the tool provides a graphical visualization of the discharge profile and energy storage regions. As illustrative examples, the tool is demonstrated for both a pseudocapacitor and a battery-type device, which supports normalization by active mass or by electrolyte volume. Finally, the tool has been deployed in a user-friendly streamlit-based interface to perform the analysis and reduce excessive manual data processing.

Monitoring lithium-ion battery (LIB) temperature in real time is crucial for ensuring system safety, especially across different chemistries, testing, and environmental conditions. In particular, low temperatures, accurate temperature prediction is crucial because it supports early detection of potentially unsafe operating conditions, such as those associated with lithium plating. From such prediction, the battery management system must intervene before any damage to the LIBs occurs, thus improving both safety and performance in cold environments. This work introduces an enhanced physics-informed neural network (PINN) model that leverages thermal laws and entropy effects to accurately predict the surface temperature of commercial LIBs cycled at 1C at different temperature conditions (5 °C, 25 °C, and 45 °C). As an understudied temperature range, focus is given to low temperature cycling. Unlike prior PINN approaches, the proposed model incorporates Joule heating, reversible entropic heat, and radiative losses directly into its loss function. The model also uses adaptive weighting to balance physical constraints with data-driven terms. A charge-consistency term further enhances prediction stability by linking state of charge behavior to thermal dynamics, while enforcing unit consistency in the final loss function. The experimental results from three commercial cell chemistries (LFP, LCO, and NMC) show that the proposed model achieves mean absolute errors as low as 0.07 °C (LFP at 5 °C). Such performance was obtained when the model is trained and evaluated within the same chemistry using only 30% of the dataset. These results fall within the typical uncertainty range of thermocouple measurements (±1 to ±2 °C), demonstrating that the proposed approach is robust and shows close agreement with the measured temperature signals, thereby supporting improved safety and performance in lithium-ion battery systems.

Compared with three-dimensional (3D) materials, low-dimensional materials attract more research interest due to their spatial quantization [1–11]. Low-dimensional materials, including zero-dimensio...

The growing dependency on lithium-ion batteries for electronic and electric vehicles raises concerns as the availability of lithium is limited in earth’s crust. An alternative solution to the problem is adopting alternative battery technology depending on the locally available metal. Sodium-ion batteries (NIB) can be a suitable battery technology because of the wide availability of material across nations. However, the under-exploration of material for NIB and limited research on established material for harsh conditions adaptability inside a battery compartment limits the progress of NIB technology. Commercial graphite is an established active material for lithium-ion batteries (LIB), but it shows poor cycle life and ~35 mAh/g capacity towards Na⁺. On the other hand, transition metal dichalcogenides (TMD) are another established material for both LIB and NIB that shows good cycle life stability and higher capacity towards Li⁺ and Na⁺ due to their capability of possessing both intercalation-conversion reaction mechanisms. Here, we explore the performance of metallic layered niobium selenide (NbSe₂) at different temperature conditions. The materials' X-ray diffraction (XRD) pattern indicates the 2H phase of NbSe₂ with 6.34 Å interlayer spacing. The wide interlayer gap of NbSe₂ potentially facilitates storage of larger ions. The long cycle life at 25^oC demonstrates ~99% coulombic efficiency at the 200^th cycle and stores 0.53 Na⁺ per formula unit. Periodic EIS unveils SEI formation over the first few cycles until the cell reaches stability. Galvanostatic intermittent titration (GITT) is also used to investigate the Na⁺ ion diffusion behavior in NbSe₂ at different temperatures. This study found that at both 25 ^oC and 45 ^oC NbSe₂ presents good rate capability, demonstrated by 78.2% and 72.6% discharge capacity retention at 25 ºC and 45 ºC, respectively, from 50 mA/g to 100 mA/g. In summary, this work relates the material properties, such as the larger interlayer spaced metallic 2H-NbSe₂, to the thermodynamic and kinetic electrochemical performance as a negative electrode for NIB at 25 ^oC and 45 ^oC. The stable cycle life of NbSe₂ at 1 A/g at 25 ^oC indicates its stability at room temperature.

Lithium‐ion batteries (LIBs) are widely used for energy storage in various industries due to their high energy density and long lifespan. However, degradation mechanisms may lead to hazardous conditions, such as thermal runaway. Solely relying on capacity changes for state of health (SOH) assessment is insufficient, given the complexity of LIBs. This work introduces comprehensive real‐time insights for state of health prediction (CRISP), a novel framework for comprehensive SOH assessment and degradation mechanisms identification. Using data from commercial LIBs, CRISP runs on a low‐cost Raspberry Pi with remote monitoring capabilities, generating aged anode half‐cell voltages for each reference performance test (RPT) as references for lithium plating assessment. CRISP processed the data of each RPT in ≈2.9 s and outputs multiple physical quantities for SOH evaluation. Results show that LIBs cycled at 100% depth of discharge (DOD) exhibited greater cathode material loss compared to those cycled at narrower DODs. However, no dendritic lithium deposition is detected by evaluating and correlating the physical parameters provided by CRISP. In summary, this study highlights the importance of multi‐parameter SOH assessment, demonstrating that single‐parameter methods (e.g., capacity‐based) fail to capture the full scope of LIBs health.

Alkali metals such as sodium and potassium have become promising candidates for the next generation of monovalent-ion batteries. However, a challenge for these battery technologies lies in the development of electrode materials that deliver high capacity and stable performance even at high cycling currents. Here we study orthorhombic tungsten ditelluride or Td-WTe ₂ as an electrode material for sodium- (SIB) and potassium-ion batteries (KIB) in propylene carbonate (PC) based electrolyte. Results show that despite larger Shannon’s radius of potassium-ions and their sluggish diffusion in Td-WTe ₂ due to higher overpotential, at 100 mA.g ⁻¹ KIB-half cells showed higher cycling stability and low capacity decay of 4% versus 16% compared to SIB-half cells. Likewise, in a rate capability test at 61 ^st cycle (at 50 mA.g ⁻¹ ), the KIB-half cells yielded charge capacity of 172 mAh.g ⁻¹ versus 137 mAh.g ⁻¹ of SIB-half cells. The superior electrochemical performance of Td-WTe ₂ electrode material in KIB-half cells is explained based on the concept of Stokes’ radius—smaller desolvation activation energy resulted in higher mobility of potassium-ions in PC-based electrolyte. In addition, the likely mechanisms of electrochemical insertion and extraction of Na- and K-ions in Td-WTe ₂ are also discussed.