Accurate and rapid estimation of power battery state of charge (SOC) and state of health (SOH) is crucial for ensuring the safe and reliable operation of electric vehicles. A joint SOC and SOH estimation method proposed in the study based on a fractional-order model incorporates an adaptive multi-innovations unscented H-infinity filter (FOAMIUHF) to address challenges such as insufficient model accuracy and algorithm robustness in SOC and SOH co-estimation for lithium-ion batteries. First, a fractional-order second-order equivalent circuit model (FOM) is established, then the sparrow search algorithm (SSA) is employed in order to identify model parameters and fractional orders. By integrating the FOAMIUHF algorithm, dynamic adjustments of fading and weighting factors is introduced to suppress noise and enhance the accuracy of the SOC estimation process. Meanwhile, the unscented Kalman filter (UKF) is employed for SOH prediction and battery capacity updates, enabling joint SOC/SOH estimation. Experimental results demonstrate that under dynamic stress test (DST), highway fuel economy test (HWFET), and Japan working conditions, the proposed algorithm achieves a maximum mean absolute error (MAE) of 0.53% and a maximum root mean square error (RMSE) of 0.61% for SOC estimation, outperforming existing methods such as FOMIUKF-UKF and FOMIUKF. Additionally, under various initial SOC (50%-90%) and aging levels (capacity decay to 72.2%), the estimation error of SOC remains below 1%, validating the algorithm's high accuracy and robustness. The proposed method offers a reliable and effective solution battery state estimation under complex operating conditions and aging scenarios.

Molecular dynamics (MD) was used to build a simulation model for skiving single crystal silicon (SCS) with different tool radius. Through the analysis of phase change, instantaneous atomic position, temperature, Wigner–Seitz defects, stress and other variables, and the mechanical state distribution of SCS during processing is studied. The results show that the amorphous phase transition process occurs in the process of skiving SCS with different tool radii, the difference is that amorphous atoms use chips during sharp corner cutting. The form is removed, and the amorphous atoms in the rounded tool are further compressed directly under the tool, causing subsurface damage. The removal mechanism of sharp corner cutter chips is shear, and the removal mechanism of round corner cutter chips is shear and extrusion. And as the radius of the tool increases, the number of compressed atoms below the tool increases directly, resulting in a decrease in the quality of the processed surface.

Accurate estimation of SOC for lithium-ion batteries is a very important job in battery management systems, but under complex dynamic operating conditions, model misalignment often happens, and filtering algorithms usually do not make enough use of historical data, so the estimation accuracy is lowered. This paper puts forward a lithium-ion battery SOC estimation method that is based on weighted multi-innovation unscented Kalman filtering (WMIUKF); a hybrid parameter identification strategy that combines FFRLS and PSO is introduced to supply initial values for the global optimization of the model and to track dynamic drifts. To deal with the problems that the unscented Kalman Filter (UKF) does not make effective use of historical information and lacks an adaptive correction mechanism, multi-innovation theory and exponentially decaying weighting factors are incorporated into it; then, by fusing current and historical multi-step prediction residuals, a weighted freshness matrix can be constructed, and through this the method, we can improve the utilization efficiency of historical data and the system’s ability to resist interference. The performance of the proposed algorithm was validated through comparative experiments under various typical dynamic operating conditions, as well as at different temperatures (0 °C–45 °C) and discharge rates (0.5 C–2 C). The results indicate that the PSO-FFRLS hybrid parameter identification effectively improves model accuracy; compared to the UKF, MIUKF, and PSO-MIUKF algorithms, the WMIUKF achieved optimal SOC tracking under all types of dynamic operating conditions, with a root mean square error (RMSE) of no more than 0.58%. Even under extreme temperatures and high-rate discharge conditions, the error remained stable at a low level, demonstrating good environmental adaptability and robustness.

Accurate and rapid estimation of power battery state of charge (SOC) and state of health (SOH) is crucial for ensuring the safe and reliable operation of electric vehicles. Ajoint SOC and SOH estimation method proposed in the study based on a fractional-order model incorporatesan adaptive multi-innovations unscented H-infinity filter (FOAMIUHF) to address challenges such as insufficient model accuracy and algorithm robustness in SOC and SOH co-estimation for lithium-ion batteries. First, afractional-order second-order equivalent circuit model (FOM) is established, thenthe sparrow search algorithm (SSA) is employed in order to identify model parameters and fractional orders. By integrating the FOAMIUHF algorithm, dynamic adjustments of fading and weightingfactors is introducedto suppress noise and enhance the accuracy of the SOC estimation process. Meanwhile, the unscented Kalman filter (UKF) is employed for SOH prediction and battery capacity updates, enabling joint SOC/SOH estimation. Experimental results demonstrate that under dynamic stress test (DST), highway fuel economy test (HWFET), and Japan workingconditions, the proposed algorithm achieves a maximum mean absolute error (MAE) of 0.53% and a maximumroot mean square error (RMSE) of 0.61% for SOC estimation, outperforming existing methods such as FOMIUKF-UKF and FOMIUKF. Additionally, under variousinitial SOC (50%–90%) and aging levels (capacity decay to 72.2%), the estimation error of SOC remains below 1%, validating the algorithm’s high accuracy and robustness. The proposed method offers a reliable and effective solution battery state estimation under complex operating conditions and aging scenarios.