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Insulation is the foundation for the safe operation of battery systems. However, the working condition of the battery system is complex, which challenges insulation fault detection. This article presents an online estimation algorithm of insulation resistance based on an adaptive filtering algorithm for a battery energy storage system (BESS). Specifically, the insulation detection model is developed based on the Thevenin model. Aiming at the problem of system noise, a joint estimation algorithm for battery parameters and voltage is proposed based on the recursive least square and unscented Kalman filter (RLS-UKF) algorithm. The full climate models of capacity and temperature are developed. Furthermore, an insulation resistance estimation algorithm is proposed based on the UKF algorithm. The proposed method is verified by different dynamic experiments. Experimental results show that the RLS-UKF algorithm has a better voltage filtering effect than the UKF algorithm with fixed model parameters. The proposed insulation resistance algorithm can accurately estimate the system’s insulation resistance under dynamic and static conditions.

Charging control is one of the essential functions of battery management systems. Battery charging involves behavioral changes such as electricity, heat, and aging. Balancing these factors is crucial for the safe and efficient operation of batteries. This work proposes an intelligent charging scheme for lithium-ion batteries that considers charging time, temperature rise, and health losses. First, charging aging experiments are conducted to investigate the effect of charging rate on battery aging. Specifically, half-cell experiments are carried out to construct an electrode open circuit voltage model to explore battery aging modes. The aging mode attenuation model is proposed based on empirical formulas. Then, the charging multiobjective function is established based on the equivalent circuit, thermal, and aging empirical models (AEMs). A multistage constant current (CC) charging optimization scheme has been developed based on the multiobjective gray wolf optimizer (MOGWO). Finally, the feasibility of three targets and dual targets fast charging schemes is verified through simulation and experiments.

Switched capacitor (SC) circuits have been widely used for low-power and high-power areas, such as the integration circuit power supply, energy conversion for wearable devices, and power supply for data centers and electrical vehicles. The dc–dc conversion, dc–ac inversion, ac–dc rectification, and ac–ac conversion of SC topologies have been explored and discussed. This article provides a review of different topologies, application areas, mathematical models, and control methods for SC circuits. Compared with other review papers on SC converters, this article deeply and comprehensively surveys the topological structure classification and the equivalent circuits. It explores the application fields and the operation performance of SC circuits. The modeling method is presented as the steady-state modeling method and the transient modeling method. It will help to achieve accurate simulation and prediction and lay the foundation for theory analysis. The different mathematical models’ construction algorithms and the suitable application range are analyzed in this article. The control methods determine the quality of the output voltage and the output power for SC circuits. They are classified as the linear control methods and the nonlinear control methods. The nonlinear control methods will help to achieve high-quality output voltage or current regulation with low ripples and fast dynamical response speed. The comparison of different topologies, application areas, modeling methods, and control methods is provided and discussed. What is more, the simulation accuracy of different modeling strategies and static and dynamical regulation performance of different control algorithms are presented in this article, respectively. The challenges and opportunities for SC circuits are prospected.

The grouping and large-scale of battery energy storage systems lead to the problem of inconsistency. Practical consistency evaluation is significant for the management, equalization and maintenance of the battery system. Various evaluation methods have been developed over the past decades to better assess battery pack consistency. In these research efforts, the accuracy of the assessment results is often of paramount importance. In this work, a battery pack consistency evaluation approach is proposed based on multi-feature information fusion. Ohmic resistance, polarization resistance and open circuit voltage are identified as feature parameters from electric vehicle operation data. An adaptive forgetting factor recursive least squares (AFFRLS) algorithm is developed using fuzzy logic to modify the forgetting factor for parameter identification. Grey correlation analysis is applied to calculate the dispersion of features (DF). The DF is weighted to evaluate the inconsistency of the battery pack. Further, the weights are assigned through the CRITIC-G1 method. Moreover, a mapping model between the extracted voltage features and the DF is established through a cost-sensitive support vector machine (CS-SVM) algorithm, which is used to evaluate and predict the consistency distribution of battery parameters. Finally, the proposed algorithm is verified by experimental data. The results indicate that the proposed parameter identification, consistency evaluation and prediction methods have high accuracy.

Maximizing the utilization of lithium-ion battery capacity is an important means to alleviate the range anxiety of electric vehicles. Battery pack inconsistency is the main limiting factor for improving battery pack capacity utilization, and poses major safety hazards to energy storage systems. To solve this problem, a maximum capacity utilization scheme based on a path planning algorithm is proposed. Specifically, the reconfigurable topology proposed is highly flexible and fault-tolerant, enabling battery pack consistency through alternating cell discharge and reducing the increased risk of short circuits due to relay error. The Dijkstra algorithm is used to find the optimal energy path, which can effectively remove faulty cells and find the current path with the best consistency of the battery pack and the lowest relay loss. Finally, the effectiveness of the scheme is verified by hardware-in-the-loop experiments, and the experimental results show that the state-of-charge SOC consistency of the battery pack at the end of discharge is increased by 34.18%, the relay energy loss is reduced by 0.16%, and the fault unit is effectively isolated.

Due to the nonlinear characteristics of photovoltaic (PV) cells, it remains as a challenge to generate stable maximum power from PV conversion systems. Under partial shading circumstances, PV characteristics present multiple local maximum power points. In this paper, a neural network improved dragonfly algorithm (NNIDA) based approach is proposed to improve the performance of tracking the global maximum power point (GMPP). Specifically, the specific points on the $I-V$ curves are sampled and analyzed such that the various ranges of irradiance and temperature are covered. Then, the iteration best solution of MPP (MPP$^\mathrm{IB}$) can be quickly acquired by utilizing the fast approximation characteristics of NNIDA based approach. This process is independent of the configurations of PV modules and has no requirement for irradiance and temperature sensors. Further, the NNIDA based approach can locate the global best solution of MPP (MPP$^\mathrm{GB}$) rapidly and precisely in a small interval. Particularly, an adaptive convergence factor is introduced into the NNIDA based approach to accelerate the convergence rate. Furthermore, the inertia weight is developed to improve the tracking accuracy. The comparative simulation and hardware-in-loop (HIL) examples validate the effectiveness of the proposed approach.

The large-scale grouping of the battery system leads to the inconsistency of the battery pack. Aiming at tacking this issue, an inconsistency evaluation method is deployed for the battery pack based on an improved Gaussian mixture model (GMM) and feature fusion approach. Specifically, the proposed adaptive forgetting factor recursive least squares (AFFRLS) algorithm allows the open-circuit voltage and other parameters to be jointly identified without the open circuit voltage-state of charge (OCV-SOC) test. An online capacity estimation approach with the extended Kalman particle filter (EPF) is put forward for capacity estimation. Further, an improved GMM is proposed to visualize battery pack inconsistency, using the K-means++ algorithm to initialize category centers. The standard deviation coefficient approach quantifies the inconsistency. Finally, the real-life vehicle data are performed to validate the effectiveness of the proposed method. The experimental results show that the proposed method can evaluate the battery parameters accurately. With the increase in service time, the inconsistency of the battery pack is gradually deteriorating.

This paper provides a special flexible graphene film based capacitive wireless power transfer (FGCPT) system for powering biomedical sensors of smart wearable devices. The graphene conductive material is flexible, transparent, highly conductive, and impermeable to most gases and liquids. Generally, the coupling structure of capacitive wireless power transfer (CPT) system is consisted of metal plates. However, it is hard to use for the biomedical sensors as the low power density and big volume. The shape of graphene conductive material could be easily built and changed according to the application requirements. In this paper, the power supply of biomedical sensing system could be accomplished by a single graphene film which is acted as the receiver of FGCPT system. The 200 mW power level is achieved with the maximum 9 V output voltage. The theory and calculation are verified by the simulated and experimental results.

Effective state of health (SOH) estimation is of great significance for the maintenance and management of lithium-ion battery. A method for life prediction of lithium-ion batteries based on long short-term memory (LSTM) neural network is presented in this paper. To simulate the actual scene of the electric vehicle (EV), the dynamic aging experiment is carried out. In order to enhance the accuracy of parameter identification, the RLS algorithm is improved using fuzzy logic, the forgetting factor is adaptive according to the voltage error. Further, the internal parameters strongly related to SOH are extracted, and the SOH prediction model with LSTM neural network is established. The performance of the proposed algorithm is verified by comparing different algorithms with training sets of different scales.

This paper proposes a method for retired batteries based on SOC consistency matching. This method takes the SOC consistency of the battery pack as the standard, which calculates the model parameter matching relationship to optimize the performance of the pack. Based on Thevenin equivalent circuit model, the mathematical model of lithium ion battery is established and the model is parameterized. Additionally, a battery pack reconfiguration criterion based on SOC consistency is proposed to reveal the mapping relationship between cell parameters and battery pack SOC consistency. Finally, the battery parameters are identified by the recursive least squares (RLS) algorithm to select two groups of batteries. Dynamic and constant current tests are performed on battery packs with different topologies, which are used to verify the feasibility of the proposed method. The experimental results show that the proposed reorganization scheme can better ensure the consistency of the SOC of the battery pack during charging and discharging.

The development of power batteries has driven the popularity of electric vehicles (EVs). For EV, charging management directly affects battery pack performance and vehicle portability. In this paper, a multi-stage constant current charging mode considering the temperature rise, health loss, and charging time is proposed. Based on the equivalent circuit model, thermal model and aging empirical model of the battery, the objective function of charging optimization is constructed. The solution is solved by with different optimization algorithms. A comparative analysis of the different solutions on the Pareto front is performed, and the optimization results show that the non-dominated sorting genetic algorithm-II (NSGA-II) results are significantly better than the non-dominated sorting moth flame optimization (NSMFO). Finally, comparing the optimization results with the conventional constant current (CC) charging method shows that the charging optimization algorithm proposed in this work is able to reduce health loss and temperature rise. Although, there is a mutually binding relationship between charging time, temperature rise and health loss. However, the best charging optimization can be obtained on the Pareto front according to different charging requirements.

Echelon utilization is the optimal solution for handling retired lithium-ion batteries. How to ensure the economy and safety of retired batteries for echelon utilization, consistency sorting technology is crucial and valuable. However, batteries are influenced by multiple factors and the mapping of internal and external parameters is complex. Finding external features with discrimination and interpretability, and using effective sorting methods for consistency sorting is still challenged. To address the above problem, a multistage deep sorting strategy for retired batteries based on the clustering of static and dynamic features is proposed in this paper. Firstly, the density-based spatial clustering of applications with noise is used to remove abnormal batteries in the initial sorting stage, which ensures the safety of battery echelon utilization and the efficiency of energy utilization. Secondly, low-correlation static features are selected by Pearson correlation coefficient analysis, and the WK-means clustering algorithm was employed for the secondary sorting of retired batteries. Finally, the sliding window method is used to extract the time-square dynamic features from the dynamic stress test data, and the clustering algorithm is employed to subdivide the retired batteries again with precision. The experimental results indicate that the proposed method in this paper can effectively achieve consistency assessment and sorting of retired batteries. After sorting, the capacity, resistance, and open-circuit voltage of the battery are compactly distributed, resulting in a reduction of their corresponding standard deviations by 36.76 %, 62.97 %, and 71.62 %, respectively. Furthermore, the cut-off time and voltage drop of dynamic profile are reduced by 8.87 % and 65.36 %, respectively.

In [1], this paper was submitted for the Special Issue on Flexible Biomedical Sensors for Healthcare Applications. The paper was instead published in Volume 16, Issue 6, 2022.

The large-scale and high voltage of lithium-ion battery packs have brought severe challenges to the insulation performance of the system. An effective insulation fault diagnosis scheme is of great significance in ensuring the operation of the battery pack. In this work, a battery insulation detection scheme based on an adaptive filtering algorithm is proposed. Firstly, an insulation resistance detection scheme based on signal injection is designed. Then, an insulation resistance estimation algorithm based on the adaptive forgetting factor recursive least square (AFFRLS) algorithm is proposed, which uses fuzzy logic to adaptively correct the forgetting factor. Finally, the proposed insulation detection scheme is verified by four groups of experiments under different dynamic and static conditions. The experimental results show that the AFFRLS algorithm has a significant filtering effect and can suppress the influence of system noise and battery voltage fluctuation on the estimation results. The adaptive forgetting factor ensures the dynamic performance of the algorithm.