Tianqing Yuan

With the widespread deployment of large-scale energy storage systems, this paper proposes a state of health (SOH) estimation method for lithium-ion batteries operating under fluctuating renewable power conditions. First, a random forest algorithm is employed to eliminate false data from the raw dataset. Then, the maximum mean discrepancy method is introduced to efficiently identify aging domains and detect knee points, thereby reducing the distribution discrepancy between the source and aging domains and enhancing model robustness. Subsequently, a wrapper-based feature selection approach is utilized to extract highly correlated features and establish mapping relationships with the prediction model. Finally, a hybrid model integrating the horned lizard optimization algorithm, bidirectional temporal convolutional network, and bidirectional gated recurrent unit is developed for SOH prediction. Experimental results demonstrate that the proposed method achieves mean absolute percentage error and root mean square error within 0.6% and 1.4% on the Oxford dataset, respectively, and exhibits strong performance on the National Aeronautics and Space Administration dataset, confirming its high accuracy and robustness.

As a core component of the new power system, energy-storage batteries undergo capacity degradation, increasing internal resistance and declining state of health (SOH) as a result of calendar aging. These changes compromise the operational safety and life-cycle economic performance of energy-storage stations. To address the need for calendar-aging risk management in stationary batteries and accurate SOH estimation from cycling data, this study develops two complementary components: a field priority-charging strategy for stationary cells and a data-driven SOH prediction model evaluated on public cycling datasets. The strategy defines criteria for low-energy stationary batteries and establishes weighted priority-charging rules for risk-cell locking and power replenishment. We construct an SOH prediction model that integrates interactive convolution and adaptive spectral modules, and we use the Dream Optimization Algorithm to optimize its hyperparameters and address multi-scale feature entanglement and limited prediction accuracy. The results show that the strategy replenishes SOC and increases cell voltage in stationary batteries, and that the risk score is significantly correlated with SOH. On the CALCE and Oxford cycling datasets, the model yields lower errors than the evaluated baselines, although the reduction depends on the battery and comparator. Ablation tests confirm the superiority of each module. This study provides a field risk-management scheme for stationary batteries and a high-precision SOH prediction method validated on public cycling datasets.

Reliable battery state awareness is essential for energy management and power allocation in hybrid electric ships. However, battery management systems are increasingly exposed to False Data Injection Attacks (FDIAs) in intelligent connected environments, which can distort State of Charge (SOC) estimation and compromise the operational reliability of shipboard power systems. To address this challenge, this paper proposes a closed-loop “Modeling-Detection-Defense” framework for secure SOC estimation in marine cyber-physical energy systems. First, a stealthy FDIA model is developed based on battery dynamics and physical consistency constraints. Second, a hybrid detection method combining unsupervised and supervised learning is proposed to identify attacks. Finally, a long short-term memory network is employed to reconstruct compromised measurements and provide reliable SOC information for continuous energy management. Experimental results demonstrate that the proposed framework mitigates SOC estimation deviations caused by FDIAs. In addition, it effectively reduces power allocation errors and energy losses, thereby improving the cyber-resilience, operational reliability, and energy efficiency of hybrid ship power systems.

With the wide application of battery energy storage systems (BESSs) in DC microgrids, BESSs are facing increasingly severe cyber threats, among which, false data injection attacks (FDIAs) seriously undermine the accuracy of battery state estimation by tampering with sensor measurement data. To address this problem, this paper proposes an improved generative adversarial network (WGAN-GP)-based detection and defence method for FDIAs in battery energy storage systems. Firstly, a more perfect FDIA model is constructed based on the comprehensive consideration of the dual objectives of circumventing the bad data detection (BDD) system of microgrid and triggering the effective deviation of the system operating state quantity; subsequently, the WGAN-GP network architecture introducing the gradient penalty term is designed to achieve the efficient detection of the attack based on the anomalous scores output from the discriminator, and the generator reconstructs the tampered measurement data. Finally, the state prediction after repair is completed based on Gaussian process regression. The experimental results show that the proposed method achieves more than 92.9% detection accuracy in multiple attack modes, and the maximum reconstruction error is only 0.13547 V. The overall performance is significantly better than that of the traditional detection and restoration methods, and it provides an effective technical guarantee for the safe and stable operation of the battery energy storage system.

The ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic factor recursive least squares (DFFRLS) algorithm and the strong tracking H-infinity filtering (STF-HIF) algorithm. To address the issue of fixed forgetting factors in recursive least squares (RLS) that struggle to maintain both fast convergence and stability in battery parameter identification, we introduce dynamic forgetting factors. This approach adjusts the forgetting factor based on the residuals between the model’s estimated and actual values. To improve the H-infinity filtering (HIF) algorithm’s poor performance in tracking sudden state changes, we propose a combined STF-HIF algorithm, integrating HIF with strong tracking filtering (STF). Simulation experiments indicate that, compared to the HIF algorithm, the STF-HIF algorithm achieves a maximum absolute SOC estimation error (MaxAE) of 0.69%, 0.72%, and 1.22%, with mean absolute errors (MAE) of 0.27%, 0.25%, and 0.38%, and root mean square errors (RMSE) of 0.33%, 0.30%, and 0.46% under dynamic stress testing (DST), federal urban driving schedules (FUDS), and Beijing dynamic stress testing (BJDST) conditions, respectively.

The control of Brushless DC (BLDC) motor has many challenges. A large number of harmonics are generated during motor operation, while it is susceptible to external disturbances such as noise. The large torque ripple is also a constraint to the large-scale popularity of BLDC motors in some high-end industries. Taking the BLDC motor control system as the research object, this paper proposes a control method of H-infinity repetitive control. First, the state space expression is derived from the mathematical model of the BLDC motor. Then, the parameters of the low-pass filter in the internal mode link are obtained for the design of the internal mode link in the repetitive control. In addition, the H-infinity repetitive control system for the BLDC motor is constructed and converted into the standard form of the H-infinity system. Finally, bringing the generalized controlled object in the standard form of the H-infinity into MATLAB to derive the compliant compensator. The simulation results show that the proposed method can not only suppress harmonics but also improve the robustness of the BLDC motor control system.

To address the control performance and harmonic suppression issues in maritime vessel rudder permanent magnet servo systems, a fractional-order PID controller was introduced into the existing improved repetitive control strategy. We used the Oustaloup approximation algorithm and particle swarm optimization for tuning the fractional-order PID controller. The optimized parameters substantially improved the control performance. By integrating the fractional-order PID controller with the improved repetitive controller, a composite fractional-order PID repetitive control strategy was formed. Finally, MATLAB/Simulink simulations were conducted to compare and verify the disturbance rejection and harmonic suppression capabilities of the improved control strategy. The results demonstrate its superior control performance, thereby increasing the practicality of the control system in dealing with various situations.