Future Batteries最新文献

A thermorechargeable battery (TB) can be charged by heating or cooling, and hence, converts thermal energy into electric energy. The manufacture of TB, however, requires adjustment of the cathode and anode potentials by pre-oxidation. Here, we demonstrated that application of mixed electrodes composed of reduced and oxidized compounds makes the adjustment process unnecessary. The TB made of mixed electrodes exhibited excellent thermal cycle stability of the thermal voltage $V_{\mathrm{TB}}$ and discharge capacity $Q_{\mathrm{TB}}$ between 20 °C and 50 °C.

Mixed polycation transition metal ferrites are known to exhibit unique and superior characteristics for structural, electrical, magnetic, and optical applications. Although a few binary transition metal ferrites are found to be suitable for electrochemical energy storage application, ternary transition metal ferrites are not investigated for asymmetric supercapacitors (ASCs). Mixed polycation oxides are expected to have increased active sites that can facilitate proton and electron transfer impacting the redox reactions. Specific crystal structure and associated lattice parameters as well as surface and morphological characteristics can also influence the energy storage properties. This study for the first time reports a novel complex polycation redox material, (Cu_pMn_qZn_r)_xFe_yO_z and renewable pinewood (PW) derived porous carbon (POC) as electrodes for ASC. Both (Cu_pMn_qZn_r)_xFe_yO_z and PW-POC are subjected to electrochemical characterization and used in ASC configuration with aqueous KOH electrolyte. It is anticipated that the Faradaic characteristics of (Cu_pMn_qZn_r)_xFe_yO_z will make it to serve as a cathode while PW-POC with capacitive behavior will act as anode in ASCs. Relatively higher specific capacitance of > 200 F/g is observed for the (Cu_pMn_qZn_r)_xFe_yO_z reference electrode and fabricated ASCs. Capacitance retention rate is tested in 10,000 cycles for the working electrodes whereas for ASC, the stability tests are performed over 100 charging-discharging cycles exhibiting relatively higher capacitance retention. (Cu_pMn_qZn_r)_xFe_yO_z appears to be a promising material for a supercapacitor.

Remaining Useful Life (RUL) prediction in lithium-ion batteries is crucial for assessing battery performance. Despite the popularity of deep learning methods for RUL prediction, their complex architectures often pose challenges in interpretation and resource consumption. We propose a novel approach that combines the interpretability of a convolutional neural network (CNN) with the efficiency of a bat-based optimizer. CNN extracts battery data features and characterizes degradation kinetics, while the optimizer refines CNN parameters. Tested on NASA PCoE data, our method achieves exceptional results with minimal computational burden and fewer parameters. It outperforms traditional approaches, yielding an R2-score of 0.9987120, an MAE of 0.004397067 Ah, and a low RMSE of 0.00656 Ah. The proposed model outperforms traditional deep learning models, as confirmed by comparative analysis.

The precise input of thermal parameters is essential for thermal simulation. Although constant thermal parameters are commonly used for parametrizing thermal modeling frameworks, extensive measurements indicate a significant dependence of thermal parameters on temperature, SOC and SOH. Therefore, this work summarizes experimental data and integrates determined operating point dependencies into a validated thermal-electrical-electrochemical modeling framework. Exploring the effect of variable thermal parameters, detailed effects on fast-charging and corresponding charging times are assessed.

It is found that the strong reduction in through-plane thermal conductivity due to aging can notably increase thermal inhomogeneity. Thus, heat dissipation is reduced and the thermal management has to be revised to prevent an increase in charging time of up to 3%. However, the operating point-dependent through-plane thermal conductivity has no significant effect on fast-charging for the analyzed pristine cylindrical lithium-ion cell. Furthermore, a temperature-dependent specific heat capacity definition considerably affects the thermal behavior of lithium-ion cells at extreme temperatures. While enabling a faster heating at low temperatures, a temperature-related current derating at high temperatures is delayed. Thus, a variable thermal parameter definition can lead to an increase in fast-charging capability of up to 3% due to the more precise modeling of the physical behavior of the cell.