An improved Sage-Husa extended Kalman filter (SHEKF) algorithm is intended to improve the accuracy and stability of SOC prediction. In this paper, two different exponential weighting algorithms are used to adaptively select the forgetting factor for adaptive noise estimation. Moreover, the OCV-SOC curve is obtained using a 7-segment linear fitting method before the algorithms estimate the SOC. In addition, by combining this improved method with a third-order RC equivalent circuit model in the dynamic stress test (DST) case the convergence time is reduced by 0.15 s compared to the second-order RC equivalent circuit model. Following that, four different types of comparison experiments are carried out by comparing the improved algorithm to EKF and other SHEKF algorithms.The estimation accuracy under DST conditions of 0 °C, 25 °C and 45 °C is approximately 0.5%, 2.2% and 1.3% improvement compared to the EKF algorithm.

Series-connected lithium-ion battery packs are widely used in electric vehicles (EVs). However, inevitable inconsistency among cells can cause charge imbalance, accelerated aging, and reduced system safety. To improve the consistency of series-connected battery packs under complex EV operating conditions, this study proposes a hierarchical active balancing system. Bidirectional Buck–Boost converters are employed for intra-group balancing, and distributed flyback converters are used for inter-group balancing. A multi-stage coordinated balancing control strategy is further developed to reduce control complexity and improve balancing efficiency. A 16-cell series-connected battery pack model is established in MATLAB R2024a/Simulink and evaluated under resting, charging, and discharging conditions. The results show that, compared with the conventional single-layer Buck–Boost balancing topology, the proposed method reduces the balancing time by 58.09%, 57.97%, and 58.06%, respectively. These results indicate that the proposed system can effectively improve the consistency and balancing performance of series-connected battery packs, providing a scalable solution for EV battery management systems.

Data-driven methods have been extensively used for state of charge (SOC) estimation because of their nonlinear regression capability. However, when the driving cycle differs from the training conditions, their estimation accuracy may deteriorate because of insufficient generalization ability. This problem becomes more pronounced under complex unseen dynamic conditions, while some data-driven methods also suffer from high computational complexity, which further limits their practical application. To overcome these issues, this study presents a LightGBM-regulated and residual-compensated adaptive extended Kalman filter method for SOC estimation. In the proposed method, battery dynamics are represented by a second-order equivalent circuit model, while SOC is recursively estimated using an adaptive extended Kalman filter (AEKF). LightGBM is used to predict covariance adjustment coefficients for adaptively regulating $Q$ and $R$, and to compensate for the remaining SOC estimation error after AEKF processing. The proposed method is validated under multiple dynamic driving cycles and different temperature conditions. The experimental results of the main unseen-cycle tests show that the proposed method achieves good estimation accuracy and generalization performance under unseen driving cycles, with the maximum error (ME) and root mean square error (RMSE) not exceeding 2.26% and 1.28%, respectively.

The Flexible Job-shop Scheduling Problem (FJSP), a pivotal NP-hard challenge in intelligent manufacturing, has been increasingly addressed by Deep Reinforcement Learning (DRL) methods. However, existing approaches face a dilemma: Proximal Policy Optimization (PPO) ensures stability but suffers from conservative exploration, while Soft Actor–Critic (SAC) enhances exploration but lacks stability in discrete scheduling spaces. To resolve this trade-off, this study proposes PPO-Graph Explorer, a novel framework that integrates a Graph Isomorphism Attention Network (GIAN) with an Entropy-Adjusted PPO (EAE-PPO). Unlike generic Graph Transformers, our GIAN employs a structure-aware hybrid design specifically tailored for FJSP’s disjunctive graph topology. EAE-PPO introduces a structured exploration curriculum that enables the agent to mimic aggressive search behaviors early in training without sacrificing on-policy stability. Extensive experiments on standard benchmarks (Brandimarte, Hurink, Dauzère–Pérès) demonstrate our method’s superiority. Compared to state-of-the-art DRL baselines, it achieves an average makespan gap reduction of 5.1 percentage points with zero statistical outliers. Qualitative analysis further reveals an 8.95% reduction in makespan on representative instances, accompanied by a significant increase in average machine utilization from 89.0% to 98.1%.

Yan, J.; Gao, L.; Wang, L., and Gong, X., 2019. Design, finite element analysis and utility of automatic weeder for port. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 901–904. Coconut Creek (Florida), ISSN 0749-0208.The expansion of the port area leads to the rapid growth of weeds in less commonly used areas. A weeding machine was designed for port, its structure and principle were introduced, frame stress was analyzed by finite element method, and the effects of weed height and speed on weeding rate were studied. The results showed that the overall frame was relatively safe, but there were some risks in use. When the speed of the weeding machine was 5 km/h and the height weed was 60 cm, the weeding rate was the highest. Finally, the improvement measures were put forward for the shortcomings of weeding machine.