In unstructured environment, the robot faces the Precise Peg-in-Hole (PPiH) assembly as a non-cooperative issue. The posture uncertainty of the peg presents challenges in searching and inserting the hole. The purpose of the research is to eliminate the posture deviation between the peg and the hole with the force feedback. In this paper, the posture adjustment is divided into rough and fine processes. Firstly, for the rough adjustment, the force-angle samples from the end-effector are trained using a Multi-Layer Perceptron (MLP) model under peg-hole non-contact. The robot is guided by the MLP and adjusts the peg to roughly compensate for the posture deviation. Then, the robot brings the peg toward and contacts the hole. Secondly, for the fine adjustment, a Fuzzy-Finite Variable Admittance Control (FFVAC) model is established to estimate and adjust posture for different peg-hole contact states accurately. By integrating force information with fuzzy logic, the fuzzy inference system with fuzzy rules is developed based on the peg-hole contact states. According to the contact states, the twin delayed deep deterministic policy gradient (TD3) model finds the optimal admittance control parameters to achieve the surface close fitting of the peg and hole. Finally, comprehensive experiments are conducted under the unknown initial posture of the peg. The results are analysed by comparing with the stated of the arts of the posture adjustment methods regarding adjustment accuracy and operation time. The proposed method quickly reduces the posture deviation angle less than 0.2°, facilitates the following hole search and insertion works.

Lithium-ion batteries are widely used in portable devices and mobile medical equipment due to their high energy density and long cycle life. However, long charging times for lithium-ion batteries can limit their usability. This paper proposes a multi-stage constant current charging protocol.

With the rapid advancement of technologies such as electric vehicles, the demand for energy storage devices has surged, leading to the widespread adoption of supercapacitors due to their numerous advantages. In practical applications, supercapacitors are often arranged in series or parallel configurations to form capacitor banks, which cater to higher voltage or capacity requirements. However, inconsistencies in the manufacturing processes and materials can lead to variations in the electrical performance of individual supercapacitors, necessitating effective balance management. Existing balancing methods are generally categorized into passive and active approaches. While passive balancing circuits are simple and cost-effective, they tend to be inefficient. On the other hand, active balancing methods, although offering high control precision, are typically more complex and expensive. This paper introduces a collaborative balancing strategy based on Deep Deterministic Policy Gradient (DDPG) using a switch resistor circuit, which serves as an intermediate approach between passive and active methods by combining their respective advantages. By integrating deep reinforcement learning with the switch resistor circuit for supercapacitor balancing, the proposed method addresses the slow balancing speed of traditional circuits under significant voltage disparities, enhancing the robustness of the balancing process and achieving superior performance. A simulation environment is established in Simulink to evaluate the effectiveness of the proposed method under various initial voltage conditions. The results demonstrate that the supercapacitor bank achieves balance within a short time frame. Moreover, comparative experiments indicate that the collaborative strategy significantly reduces overshoot and improves the robustness of supercapacitor balancing compared to noncollaborative approaches.

The estimation of the state-of-charge (SOC) in lithium-ion batteries has garnered significant attention, with current research primarily concentrating on individual batteries. In practice, however, lithium-ion batteries often require connection to a balancing circuit to correct battery imbalance. In such cases, the system topology differs from that of an individual battery. Therefore, it is essential to account for this change. This article proposes a method for estimating the SOC of lithium-ion battery cells within reconfigurable circuits. We established a switching system model for lithium-ion batteries in reconfigurable circuits. We then design a nonlinear switching observer and examine its stability. Additionally, we conducted extensive experiments to evaluate the proposed observer’s performance and compared it with other observers.

The aging process of lithium-ion batteries is a complex nonlinear process involving multiple electrochemical reactions. During different charge-discharge cycles, the battery exhibits different degradation characteristics. Accurate State of Health (SoH) estimation is a fundamental requirement for battery prediction and health management. Existing SoH estimation methods often rely on manually selected features, which introduce subjective bias, limiting their generalizability and robustness. This paper proposes a feature extraction framework based on a Bayesian optimization algorithm. First, partial incremental capacity (IC) analysis is performed within a specific voltage range, and this framework searches for effective Cycle-Voltage-IC features in the multidimensional feature space based on the self-fluctuation of the features and their correlation with battery capacity. Bayesian optimization is used to search for feature indices and intervals to extract features. Then a stacking ensemble model is developed that combines Bayesian ridge regression and randomized consistent sampling regressor to enhance the robustness of SoH estimation. This method provides interpretable feature extraction and improves the accuracy of SoH estimation. Compared with the feature extraction method based on manual experience, the feature extraction framework and SoH estimation model proposed in this paper can better fit the capacity degradation process of the four battery models, and the absolute errors of their SoH estimation are reduced by 21.88%, 66.67%, 8.92%, and 32.59%, respectively.

Batteries are pivotal in electric vehicles (EVs), serving as the primary source of power. To ensure the safe and efficient operation of EVs, it is essential to accurately predict the state of health (SOH) of the battery, typically achieved through a battery management system (BMS). However, the complex coupling reactions and nonlinear degradation processes inherent in lithium-ion batteries (LIBs) present significant challenges in SOH prediction. Current data-driven models often require extensive datasets and prolonged training periods, while also exhibiting limited generalization capabilities. To address these challenges, this paper proposes a novel method based on a foundation model which named Lag-Llama for predicting SOH and other critical battery characteristics synchronously, such as temperature, internal resistance. Remarkably, under zero-sample conditions, our approach achieves a Continuous Ranked Probability Score (CRPS) of 0.0079, demonstrating robust zero-shot generalization capabilities. Furthermore, the model’s predictive performance is significantly enhanced following fine-tuning.

In the field of energy storage, precise estimation of the state-of-charge (SOC) of lithium-ion batteries is crucial for maximizing their efficiency and extending their lifespan. Existing research has predominantly focused on SOC estimation for individual battery cells. However, in practical applications, a balancing circuit is typically integrated into the battery management system (BMS) to mitigate cell imbalance. When the balancing circuit is activated, the battery cell transitions into a new operational mode, rendering conventional SOC estimation techniques ineffective. Reconfigurable circuits, recognized for their adaptability across diverse application environments, present a novel approach for SOC estimation in lithium-ion batteries, leveraging their dynamic reconfiguration capabilities. This paper introduces an innovative method for SOC estimation of reconfigurable lithium-ion batteries, employing an extended Kalman filter (EKF) within the context of reconfigurable circuits. The proposed methodology begins with the design of a switching system for lithium-ion batteries, facilitating equivalent circuit modeling. Subsequently, a nonlinear observer, based on the extended Kalman filter, is developed to estimate the SOC. An experimental platform was also constructed to validate the feasibility and efficiency of the proposed method. Experimental results demonstrate that this approach significantly enhances the accuracy and robustness of SOC estimation compared to traditional methods.

To ensure optimal functionality of the super-capacitor management system in practical applications, the accurate and robust state of charge (SOC) estimation is crucial, particularly to account for aging effects and varying operating conditions. This paper proposes a switched system-based approach for the co-estimation of SOC and parameters of supercapacitors coupled with balancing resistor circuits. A switched model incorporating an equivalent circuit model is developed to accommodate the activation of equalization within a series-connected supercapacitor pack. The method combines a modified recursive least squares (RLS) algorithm with a switching sliding mode observer (SMO) for real-time parameter adaptation and SOC estimation. The experimental verification under a multi-balancing charging scenario demonstrates sig-nificant enhancements in accuracy and robustness compared to traditional methods employing fixed model configurations and parameters.

This paper explores the estimation of the State of Charge (SoC) of lithium-ion batteries. Currently, the majority of research efforts focus on the SoC estimation of individual lithium-ion batteries. However, in practical scenarios, lithium-ion batteries are commonly connected with balancing circuits to address battery imbalances. Upon activation of the equalization circuit, the battery's system dynamics transition to a new mode. Therefore, it is difficult f o r c l assical S o C estimation algorithms to accurately estimate the real SoC value. In this paper, we employ a switched system methodology to estimate the battery's SoC. We describe the switched system of the Thevenin equivalent circuit model of a lithium-ion battery using a switched resistance balance circuit. Then we use the method of nonlinear switching observer to analyze the convergence and divergence. Finally, we set up an experimental platform and verify the performance of the observer through several sets of experiments.

Predicting accurate capacity degradation and re-maining useful life (RUL) of lithium-lon battery is critical to health management and safe operation. However, variations in operating conditions and the variety of battery types present challenges to data-driven predictive models. Most data-driven methods rely on traditional machine learning models, which often have constrained predictive and generalization abilities. In this paper, a foundation model: Lag-Llama is used to predict capacity and RUL of battery with auto-correlation analysis. Firstly, the tokenization scheme of Lag-Llama is improved by auto-correlation analysis, which calculate the most probable periods in history capacity sequence. It is helpful for model to comprehend the capacity fluctuation pattern. Then, Lag-Llama is pre-trained to learn battery capacity degradation, and thus calculate the RUL. Additionally, the model is fine-tuned with a small amount of data to update the top-level module for application to the target cell. Finally, experimental results show that the proposed model exhibits accurate RUL prediction and strong transfer capability, within the average mean square error and absolute error less than 0.035 and 9 respectively.

The filter performance will be degraded in the measurements with time-varying and unknown noise statistics. To combat the above challenges, a modified strong tracking slide window variational adaptive Kalman filter algorithm is proposed in this article. First, the multiple fading factors are integrated into the proposed algorithm to adjust the error covariance. Next, an improved adaptive slide window method is designed for variational Bayesian (VB) Kalman filtering by adaptively adjusting the slide window size and correcting the previous state according to the later state, which improves the estimation accuracy and computational efficiency. Finally, the inverse Wishart distribution is considered for modeling process and measurement noise, and the state vector, as well as noise statistics, are inferred via the VB technique without prior noise covariance information. Simulation results demonstrate that the proposed filter algorithm is more robust than existing filters in counteracting measurement and process noise uncertainties.

The braking system is the key part of trains, and its full life-cycle of health status is essential to ensure the safety of trains. How to accurately assess real-time health status throughout the full life-cycle of the train braking system is a challenge. In this paper, a digital twin-driven hybrid estimate method for health status of the braking system is proposed. Firstly, an equivalent model of the braking system is built in the digital twin platform. Then, a hybrid method of fusing model and data is proposed to assess the health status. Finally, a cloud digital twin experimental platform for health status assessment of the braking system is built, and the health status is shown by visualization framework. The experiments verify the effectiveness and practicality of the proposed scheme.

In this letter, a novel and effective algorithm based on Retinex model is proposed for low-light image enhancement, named Blind Adaptive Structure-Preserving Image Enhancement (BASSY). The low-light image enhancement is still a challenging task because the decomposition of images into light components and reflection components is an ill-posed problem. BASSY adopts a content-adaptive guided filtering based on local variances to estimate the proper illumination map. The salient features of the proposed approach are: (1) For the illumination component, the overall structure in the low-light image is preserved and the texture details are smoothed. (2) The reflectance is estimated without logarithmic transformation to reduce the computational burden and to avoid over-smoothing the reflectance component. (3) The adaptive gamma correction for the illumination map is used to reconstruct the enhanced image. (4) BASSY can be implemented efficiently due to the low computation complexity Ο(N). Experimental results on six public datasets show that the enhanced images by the BASSY exhibit higher naturalness and better visual quality than six state-of-the-art methods.

By deploying unmanned surface vehicles (USVs), the efficiency and reliability of mission execution can be improved. This paper is concerned with the problem of formation autonomous navigation system (FANS) for USVs. The decision layer of the system is composed of path planning subsystem and navigation control subsystem to realize fast and effective autonomous navigation system. The FANS is constructed by leader-follower structure and distributed control strategy, which make the individuals in the formation have certain autonomy. The dynamic domain tunable fast marching square algorithm proposed in the path planning subsystem can not only adjust the safety and length of the path planned, but also continuously re-plan the path according to the motion information of the USV formation and target-vessels. Navigation control subsystem based on finite control set model predictive control can quickly and safely guide and control formation in local sea environment. The simulation tests are carried out in static and dynamic harbor environments respectively, which verifies the ability of the FANS to achieve stable formation tracking and autonomous safe navigation.

Traditional lighting systems suffer from the problem of low energy efficiency and low illumination quality due to its disappointing management. To address this issue, in this paper, a novel traffic-flow adaptive scheme of smart lighting systems is proposed on the basis of the cyber-physical cloud system. The cyber-physical cloud system consists of the digital twin and cyber-physical system. The operation of the lighting system is simulated in the counterpart twin system with the digital twin technology. The cyber-physical system realizes data collection, information interaction, analysis, and processing, as well as complex computation and remote control. The traffic adaptive scheme works according to the brightness sequence to improves the energy efficiency of the lighting system and provide higher illumination quality for drivers. Extensive simulation results verify the proposed control scheme could improve the energy efficiency of lighting systems.

The high-speed solenoid valve is a crucial module in train braking system, which is an essential factor to ensure the safe operation of trains. How to estimate the health status of the high-speed solenoid valve accurately to improve the reliability of train braking system is a challenging issue. Most related work relies on accurate physical models or large amounts of historical data. To address this challenge, this paper proposes a hybrid data-fusion estimate method for the health status of train braking system. Firstly, the physical model of the high-speed solenoid valve is established, and physical indicators which represent the working performance are extracted. Then, the dynamic driving current is processed by ensemble empirical mode decomposition (EEMD) to calculate the information entropy. Physical indicators and information entropy indicators are combined into a feature vector, which can be reduced the dimension by the t-distributed stochastic neighbor embedding (T-SNE) algorithm. Finally, the feature vector is input into the probabilistic neural network (PNN) to estimate the health status of train braking system. The proposed method is implemented in the high-speed solenoid valve degradation dataset, which collected by the train brake system experiment platform. The result shows that it is better than other methods in the accuracy and calculation efficiency.