It is challenging for a battery management system to estimate the State-Of-Charge (SOC) of batteries. A novel model-based method, using a Dual Extended Kalman Filtering algorithm (DEKF) and Back Propagation Neural Network (BPNN), is proposed to estimate and correct lithium-ion batteries. The results of acceptable SOC estimation are achieved using the DEKF to estimate the battery SOC and simultaneously update model parameters online, while the SOC estimation error is in real-time predicted by the trained BPNN. To further reduce the SOC estimation error, the SOC estimated by the DEKF is corrected by adding the predicted estimation error. The SOC estimation results between the original DEKF and BPNN-based updated DEKF methods under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST), the Beijing Dynamic Stress Test (BJDST) and the US06 Highway Driving Schedule are compared. Experimental results show that the SOC error reduces considerably after correcting the estimated SOC. The corrected SOC Root-Mean Square Errors (RMSEs) decrease by an average of seven times compared with the case of no correction. The constant current discharge test verifies the generality and robustness of the proposed method. The modification to the SOC estimation results using ordinary EKF under the above four sophisticated dynamic tests verifies the effectiveness of the proposed method.

Accurate state-of-charge (SOC) estimation of lithium batteries is of great significance for electric vehicles. In this paper, a combined estimation method of multi-innovation unscented Kalman filter (MIUKF) and fractional order square root cubature Kalman filter (FSRCKF) for lithium batteries is proposed. Firstly, the adaptive genetic algorithm (AGA) is applied to carry out offline parameter identification for the fractional order model (FOM) of a lithium battery under the Dynamic Stress Test (DST). Then, battery SOC is estimated by FSRCKF, while the Ohm internal resistance R₀ of the fractional order battery model is estimated and updated by MIUKF in real time. The results show that MIUKF-FSRCKF is better than FSRCKF, FCKF and SRCKF in estimating the SOC of lithium batteries under the Federal Urban Driving Schedule (FUDS), Beijing Dynamic Stress Test (BJDST) and US06 Highway Driving Schedule tests, especially when R₀ is inaccurate.

The state of health (SOH) of lithium-ion batteries (LIBs) needs to be accurately estimated to ensure the safety and stability of electric vehicles (EVs) while in operation. In this paper, we proposed a SOH estimation method based on Improved Aquila Optimizer (IAO) and Support Vector Regression (SVR) to achieve an accurate estimation of SOH. During the charging and discharging phases of the battery, we analyzed the trends in current, voltage, and energy, then extracted four features. We used the Kendall coefficient and gray relational grade to prove that features and SOH were highly correlated. On the other hand, IAO was used to optimize the penalty factor and kernel function parameters of the SVR to further improve the generalization and mapping ability. The proposed method was verified under different operating conditions using the CACLE battery data set; the results show that high accuracy can be achieved in SOH estimation via IAO–SVR, and the estimation error of mean MAE is remaining within 2%.

Based on photoacoustic spectroscopy and finite element simulation technology, a simulation model of sound field excitation in a cylindrical resonant photoacoustic cell was established. The finite element simulation method was used to analyze the acoustic mode and sound pressure distribution of the cavity structure of the photoacoustic cell. The effects of the geometric parameters of the resonator and the buffer cavity on the performance of the photoacoustic cell were compared. The frequency response characteristics of the photoacoustic cell and the effects of the air intake and the air outlet were studied. Based on the simulation results, a cylindrical resonant photoacoustic cell was designed, and a photoacoustic sensor for NO₂ detection was built. NO₂ with a volume fraction of 10⁻⁵ was taken as the sample gas through frequency scanning to obtain the frequency response curve of the system. The resonant frequency is 1730 Hz, and the cell constant is about 542.3 (Pa*cm)/W. The quality factor is 10.05. By linear fitting the calibration curve of the sensor, the fitting slope is 0.012 µV/ppb, and R² is 0.998. Atmospheric NO₂ detection was carried out for two consecutive weeks, whose findings are in good agreement with the data released by a Huainan environmental monitoring site. The experimental results show that the system can detect NO₂ in the atmosphere with high sensitivity.

Lithium batteries are widely used due to their advantages such as high energy density, stable performance, low pollution, and long recyclable life. Accurate SOC estimation is important for the use of lithium batteries. To address the problem of low accuracy of SOC estimation by traditional methods, this paper proposes a joint method of fractional order robust unscented Kalman filter (FORUKF) and robust unscented Kalman filter (RUKF) to estimate SOC. The method is based on a fractional order model (FOM) that combines the unscented transformation (UT) technique, the H∞ observer and the joint estimator. Specifically, an adaptive genetic algorithm (AGA) was first used to identify the parameters of the FOM for lithium batteries and to verify the accuracy of the model. Estimation and updating of the ohmic resistance R0 and the capacity QN in the model in real-time by RUKF and then estimation of the SOC by FORUKF. Finally, the accuracy of FORUKF-RUKF was verified under the Federal Urban Driving Schedule (FUDS), US06 Highway Driving Schedule and Beijing Dynamic Stress Test (BJDST). According to the results, the FORUKF-RUKF estimated SOC was found to have better accuracy and robustness than the RUKF, FOUKF and EKF.