Lithium-ion batteries are an essential component of space power systems. Preventing lithium plating during the fast charging of lithium-ion batteries can ensure the safety and reliability of space power systems. However, there are some shortcomings in the current research on fast charging strategies. On the one hand, the limitation of lithium plating on the charging current rate is less considered during fast charging. On the other hand, the investigation into fast charging strategies without lithium plating is insufficient; the difference among charging strategies in terms of their performance has not been discussed in depth. In our work, firstly, a pseudo-2-dimensional electrochemical model considering lithium plating side reactions was developed, and a charging optimization cosimulation system was built with a fuzzy-proportional–integral–derivative controller combined. Secondly, 4 optimized fast charging strategies without lithium plating are proposed, substantially shortening the charging time without a marked reduction in charging capacity compared with traditional constant-current charging methods. Thirdly, the differences in the charging effects of differing charging strategies were quantitatively analyzed in 4 dimensions, charging time, charging capacity, charging energy, and charging efficiency, and the applicable scenarios of different charging strategies are discussed. Finally, the proposed fast charging strategies were verified by experiments at 10 and 25 °C. The study results quantitatively reveal the differences between differing fast-charging strategies and provide a valuable reference for the design of charging strategies in different scenarios.

With the increasing demand for battery energy density and safety, solid-state batteries are expected to become one of the ideal energy storage devices. Aiming at compensating for the research lack of modeling and state estimation of solid-state batteries, this paper proposes a state of charge (SOC) estimation method based on the electrochemical impedance spectra (EIS) of sample solid-state batteries that consider both accuracy and speed. The SOC estimation model is established using Gaussian process regression by extracting the low- and medium-frequency semicircular parameters that vary markedly with SOC. Considering the online application of the method, the fixed-frequency test impedance is selected as the characteristic parameter to achieve the SOC estimation that takes both accuracy and speed into account, and the method is still applicable under different state of health (SOH). Considering the existence of 3 impedance spectral semicircles in this solid-state battery, the RQ (parallel connection of resistors and constant phase element) process is added to establish a fractional-order model and the fractional-order extended Kalman filter (FOEKF) algorithm is developed. What is more, the FOEKF algorithm and SOC-OCV curve are constructed as a control group to analyze the accuracy, advantages, and disadvantages of the proposed SOC estimation method. The results show that the unique EIS performance of this solid-state battery is more conducive to achieving the estimation of the battery SOC. The application of the EIS online implementation method is expected to rely on the fixed-frequency test to achieve online accurate estimation of the SOC of solid-state batteries.

The estimation of state of charge (SOC) in lithium-ion batteries is important for ensuring the safe and stable operation of battery systems. Under high-rate pulse conditions, the characteristics of short discharge time, high frequency, large current, strong interference, and complex transient characteristics that make lithium-ion batteries exhibit marked nonlinear characteristics. The existing battery management system has difficulties in capturing the rising and falling edge data of the pulses due to limitations in the sampling frequency. The short idle time makes it challenging to obtain accurate open-circuit voltage, and there are difficulties in identifying the model parameters. Therefore, using a combination of coulomb counting method, open-circuit voltage correction method, and Kalman filtering method to estimate SOC poses certain challenges. This study applies backpropagation neural network (BPNN) combined with Aquila optimizer (AO) algorithm to estimate SOC under high-rate pulse conditions, and experimental verification is performed using special 3-Ah lithium iron phosphate battery. We compared the estimation accuracy of the AO-BPNN model for SOC with the BPNN, support vector machine, extreme learning machine, and Fuzzy neural network models and verified the superiority of AO-BPNN. Furthermore, by utilizing data with larger acquisition intervals, we obtained accurate evaluation results and reduced the data requirements. The effectiveness of the assessment of AO-BPNN was individually verified under different high-rate pulse conditions and different static times through pulse experiments conducted under 9 operating conditions, with the estimation error controlled within 5%. Finally, the robustness of the proposed model was validated using test data with different sampling intervals and random measurement errors.