Jianfang Jia

In grid-based robotic path planning, traditional A* algorithms are often constrained by severe path redundancy, low search efficiency, and poor adaptability to complex environments with varying obstacle densities. To overcome these limitations, this article proposes the Hierarchical Adaptive Neighborhood Search A* (HANSA*) algorithm, which integrates a dual adaptive search mechanism with local environmental complexity evaluation. This mechanism enables dynamic switching between a 16-neighborhood pattern for rapid global exploration and a directional 5-neighborhood pattern for localized refinement based on real-time planning progress and local obstacle density. Phase-specific heuristic functions are introduced to balance search efficiency, path quality, and trajectory smoothness across different planning stages. Furthermore, a two-stage path optimization strategy is employed to reduce path redundancy, remove unnecessary waypoints, and alleviate sharp corners. Experimental results in dense and complex environments demonstrate that the HANSA* algorithm outperforms conventional approaches, exhibiting improved adaptability and robustness under varying obstacle-density distributions while generating paths with lower redundancy and fewer sharp corners. The results also show that HANSA* can replan paths after newly detected obstacles are incorporated into the updated occupancy grid map. These findings confirm the effectiveness of HANSA* for grid-based global path planning of mobile robots in complex environments.

Lithium-ion batteries have become an important part of the energy storage field. Accurately estimating their state of health (SOH) is a key precondition for ensuring the safe and efficient operation of the system. The performance of most SOH estimation methods based on data-driven generally relies on the assumption that the training data and the test data have the same distribution. However, in practical applications, differences in manufacturing processes, operating conditions, and battery types lead to varying degradation patterns and inconsistent data distributions among different batteries. This affects the estimation accuracy of such methods in cross-domain scenarios. To address this issue, a time-frequency deep domain adaptation (TFDDA) method is proposed. Specifically, the method constructs a dual-branch convolutional neural network that extracts and integrates the time-domain signal of the charging curve and the frequency-domain features transformed by the Fast Fourier transform (FFT) separately. It utilizes frequency-domain components with stronger domain invariance to compensate for the limitations of relying solely on time-domain features. This enables the model to automatically learn aging features with more discriminability and robustness. At the same time, maximum mean discrepancy (MMD) is employed to align feature distributions between source and target domains, thereby achieving cross-domain knowledge transfer. To verify the effectiveness of the proposed method, three experimental scenarios are designed: transfer within the same battery type under identical and different operating conditions, and transfer between different battery types. The experimental results demonstrate that the proposed method maintains good performance even as the data distribution differences gradually increase.

Lithium-ion batteries have become an indispensable part of human industrial production and daily life. For the safe use, management and maintenance of lithium-ion batteries, the state-of-health (SOH) of lithium-ion batteries is an important indicator, so that the SOH estimation is of significant practical value. To accurately predict SOH, this article proposes a fusion prediction model that combines the particle swarm optimization (PSO) algorithm, bi-directional long short-term memory network (BiLSTM), and adaptive boosting (AdaBoost) algorithm. In the proposed prediction model, indirect health indicators (HIs), which characterize battery degradation, are obtained with the help of incremental capacity analysis (ICA) and are fed into BiLSTM to extract time-series features, whose parameters are optimized by employing the PSO algorithm. On this basis, the AdaBoost algorithm is applied to reduce the risk of overfitting the PSO–BiLSTM model. The study based on lithium-ion battery data from the Center for Advanced Life Cycle Engineering (CALCE) shows that the PSO–BiLSTM–AdaBoost model has higher accuracy, better robustness, and generalization ability.

Ash deposition on economizer heating surfaces degrades convective heat transfer efficiency and compromises boiler operational stability in coal-fired power plants. Conventional time-scheduled soot blowing strategies partially mitigate this issue but often cause excessive steam/energy consumption, conflicting with enterprise cost-saving and efficiency-enhancement goals. This study introduces an integrated framework combining real-time ash monitoring, dynamic process modeling, and predictive optimization to address these challenges. A modified soot blowing protocol was developed using combustion process parameters to quantify heating surface cleanliness via a cleanliness factor (CF) dataset. A comprehensive model of the attenuation of heat transfer efficiency was constructed by analyzing the full-cycle interaction between ash accumulation, blowing operations, and post-blowing refouling, incorporating steam consumption during blowing phases. An optimized subtraction-based mean value algorithm was applied to minimize the cumulative attenuation of heat transfer efficiency by determining optimal blowing initiation/cessation thresholds. Furthermore, a bidirectional gated recurrent unit network with quantile regression (BiGRU-QR) was implemented for probabilistic blowing time prediction, capturing data distribution characteristics and prediction uncertainties. Validation on a 300 MW supercritical boiler in Guizhou demonstrated a 3.96% energy efficiency improvement, providing a practical solution for sustainable coal-fired power generation operations.

Ash deposition on economizer heating surfaces degrades convective heat transfer efficiency and compromises boiler operational stability in coal-fired power plants. Conventional time-scheduled soot blowing strategies partially mitigate this issue but often cause excessive steam/energy consumption, conflicting with enterprise cost-saving and efficiency-enhancement goals. This study introduces an integrated framework combining real-time ash monitoring, dynamic process modeling, and predictive optimization to address these challenges. A modified soot blowing protocol was developed using combustion process parameters to quantify heating surface cleanliness via a cleanliness factor (CF) dataset. A comprehensive heat loss model was constructed by analyzing the full-cycle interaction between ash accumulation, blowing operations, and post-blowing refouling, incorporating steam consumption during blowing phases. An optimized subtraction-based mean value algorithm was applied to minimize cumulative heat loss by determining optimal blowing initiation/cessation thresholds. Furthermore, a bidirectional gated recurrent unit network with quantile regression (BiGRU-QR) was implemented for probabilistic blowing time prediction, capturing data distribution characteristics and prediction uncertainties. Validation on a 300 MW supercritical boiler in Guizhou demonstrated a 3.96% energy efficiency improvement, providing a practical solution for sustainable coal-fired power generation operations.

The accurate prediction of the state of health (SOH) for lithium-ion batteries is a key factor for improving the performance of battery management systems (BMS). However, traditional point prediction methods are difficult to effectively eliminate errors due to the uncertainty of variables and application environments. This paper presents a model for predicting the interval of lithium-ion batteries based on health indicators (HIs) during charging, which addresses the limitations of current point prediction in practical applications. First, twelve HIs are extracted from the current and voltage variables of the charging process. Secondly, feature selection is performed by random forest (RF) training, and the selected HIs are dimensioned using partial least squares (PLS). Finally, a long short-term memory network (LSTM) combined with quantile regression (QR) is used to derive the quantile values of the prediction points and each quantile is employed as input information for Gaussian kernel density estimation (KDE) to obtain the SOH probability density distribution. The experimental results based on the NASA PCOE Li-ion battery dataset and CALCE Li-ion battery dataset show that the SOH interval coverage is more than 90% and the average width of the interval is less than 0.294.