Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination ($R^2$) exceeding 97%, significantly improving prediction accuracy and efficiency.

The promotion of electric vehicles brings notable environmental and economic advantages. Precisely estimating the state of health (SOH) of lithium-ion batteries is crucial for maintaining their efficiency and safety. This study introduces an SOH estimation approach for lithium-ion batteries that integrates multi-feature analysis with a convolutional neural network and kolmogorov-arnold network (CNN-KAN). Initially, we measure the charging time, current, and temperature during the constant voltage phase. These include charging duration, the integral of current over time, the chi-square value of current, and the integral of temperature over time, which are combined to create a comprehensive multi-feature set. The CNN’s robust feature extraction is employed to identify crucial features from raw data, while KAN adeptly models the complex nonlinear interactions between these features and SOH, enabling accurate SOH estimation for lithium batteries. Experiments were carried out at four different charging current rates. The findings indicate that despite significant nonlinear declines in the SOH of lithium batteries, this method consistently provides accurate SOH estimations. The root mean square error (RMSE) is below 1%, with an average coefficient of determination (R²) exceeding 98%. Compared to traditional methods, the proposed method demonstrates significant advantages in handling the nonlinear degradation trends in battery life prediction, enhancing the model’s generalization ability as well as its reliability in practical applications. It holds significant promise for future research in SOH estimation of lithium batteries.

Lithium-ion batteries are the primary power source for new energy vehicles, making accurate estimation of their state of health (SOH) essential for ensuring the safe operation of battery systems. This paper proposes a Capacity–Power Analysis (CPA) method that incorporates temperature features to enhance feature extraction across a broader range. Additionally, we introduce an SOH estimation method for lithium batteries based on a Convolutional Neural Network (CNN) and a Kolmogorov–Arnold Network (KAN). By extracting the capacity–power curve and average temperature features during constant-current and constant-voltage charging, the CNN-KAN model establishes a nonlinear mapping relationship between the extracted features and SOH, enabling high-precision SOH estimation for lithium-ion batteries. Four 18650 batteries were tested under various charging and discharging conditions in a laboratory setting. The coefficient of determination (R²) exceeded 96.4%, the root mean square error (RMSE) was below 0.86%, and the mean absolute error (MAE) was under 0.7%, confirming that the proposed method demonstrates excellent estimation performance.

Accurately estimating the remaining useful life (RUL) of lithium-ion batteries in energy storage systems is critical for ensuring both the safety and reliability of the power grid. To address the complex nonlinear degradation behavior associated with battery aging, this study proposes a novel RUL prediction framework that integrates ensemble empirical mode decomposition (EEMD) with an ensemble learning algorithm. The approach first applies EEMD to decompose aging data into a residual component and several intrinsic mode functions (IMFs). The residual component is then modeled using a long short-term memory (LSTM) network, while the Kolmogorov–Arnold network (KAN) focuses on learning from the IMF components. These individual predictions are subsequently combined to reconstruct the overall capacity degradation trajectory. Experimental validation on real lithium-ion battery aging datasets demonstrates that the proposed method provides highly accurate RUL predictions, exhibits strong robustness, and effectively captures nonlinear characteristics under varying operating conditions. Specifically, the method achieves R² above 0.96 with absolute RUL errors within 2–3 cycles on NASA datasets, and maintains R² values above 0.91 with errors within 7–15 cycles on CALCE datasets. Furthermore, the optimal KAN hyperparameters for different IMF components are identified, offering valuable insights for multi-scale modeling and future model optimization.

Nanozymes as a promising alternative to natural enzymes are attracting enormous attention in past decades, yet their catalytic activities remain inferior to natural enzymes owing to the difficulties in performance improvement and precise control over composition, which greatly limits their practical application. Herein, by using metal organic frameworks as the sacrificial templates, we arbitrarily synthesize cobaltosic oxide (Co3O4) nanostructures with tunable valence states of Co sites via a one-step pyrolysis facilitated by a post-thermal treatment. It is found that through balancing the content between Co3+ and Co2+, partially reduced Co3O4 (R-Co3O4) exhibits the optimal peroxidase-like activity similar to natural horseradish peroxidase with Co2+ catalytically decomposing H2O2 while Co3+ adsorbs 3,30,5,50-tetramethylbenzidine. In addition, R-Co3O4 shows good stability in wide pH (2.58–10.04) and temperature (25.5–64.5 °C) ranges. Based on R-Co3O4 nanozymes, disposable paper-based bioassays are subsequently designed, exhibiting a good sensitivity to dopamine with a paper-based detection limit estimated to be 1.4 μM.