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Against the backdrop of urbanization and land use policy formulation, the accurate identification of changes is imperative for the effective monitoring and control of urban growth, conservation of natural resources, and environmental protection. Traditional remote sensing techniques for change detection, foundational to these applications, typically face significant challenges such as pronounced errors and inadequate parsing of complex environments. To surmount these obstacles, this study proposes a cutting-edge, deep learning-based strategy with a Siamese network framework, and utilizing a modified U-Net++ network (SIAM-CDNET) to improve change detection capabilities. This model integrates an attention mechanism to enhance the acuity and distinguishing features of remote sensing imagery, particularly for subtle variations and intricate structures in urban settings. Furthermore, an edge-detection component designed for multiscale feature amalgamation and a change detection-specific composite loss function are devised to increase the precision and generalizability of the model. Evaluations based on the LEVIR-CD,CDD and GZ-CD datasets indicate that the proposed SIAM-CDNET markedly surpasses existing methods in terms of accuracy and F1-score, with values of 90.50% and 90.82% (LEVIR-CD dataset),89.95% and 91.18% (CDD dataset) and 93.81% and 90.8%(GZ-CD dataset), respectively. These results confirm the exceptional performance of SIAM-CDNET in urban development and land use change detection, potentially making a significant technical contributions to the field.

Plug-in hybrid electric vehicles (PHEVs) have been validated as a preferable solution to transportation due to its great advantages in fuel economy promotion, harmful emission reduction and mileage anxiety mitigation. While, designing an effective energy management strategy to allocate the power between battery and engine is critical to improve the performance of powertrain in PHEVs. To this end, a real-time energy management strategy is proposed via incorporating double-delay Q-Learning and model predictive control (MPC). First, the energy management for PHEV is transformed into a nonlinear optimal control problem, and the vehicle speed predictor based on convolutional neural network is proposed to forecast vehicle speed in MPC. Then, based on the predicted vehicle speed, the double-delay Q-Learning algorithm is implemented to solve the receding horizon optimal problem in the MPC module. The simulation is conducted to verify the performance of the proposed strategy, and the results showcase that incorporating the double-delay Q-Learning into MPC can effectively improve the adaptability of energy management to dynamic environment, and meanwhile achieve a similar fuel consumption of the offline stochastic dynamic programming-based strategy. In addition, the single-step computation time of the proposed strategy is less than 23 milliseconds, highlighting its significant potential in online implementation.

In this paper, an improved online particle swarm optimization (PSO) is proposed to optimize the traditional search controller for improving the operating efficiency of the permanent magnet synchronous motor (PMSM). This algorithm combines the advantages of the attraction and repulsion PSO and the distributed PSO that can help the search controller to find the optimal d - axis air gap current quickly and accurately under non-stationary operating conditions, thereby minimizing the air gap flux and then improving the motor efficiency. To verify the effectiveness and stability of this proposed algorithm, the operating efficiency of PMSM as using this proposed algorithm is compared with that of traditional search controller under non-stationary operating conditions. The results show that the proposed algorithm can improve the operating efficiency of PMSM by 6.03% on average under non-stationary operation conditions. This indicates that the search controller based on the improved PSO has a better adaptation to the variation of external operating conditions, and can improve the operation efficiency of PMSM under non-stationary condition.

This work presents the control strategies for the configuration involving an open-end winding induction motor (OW-IM) fed by dual two-level inverters, which can realize the controllable power sharing between two isolated power sources without using the DC/DC converter. Based on the mathematical model of the OW-IM and the principles of the voltage vector distribution, two rotor flux calculation algorithms, namely the motor loss minimization (MLM) algorithm and the maximum power sharing capability (MPSC) algorithm are proposed and compared, with their selection strategy to select the suitable algorithm depending on the operating condition. The MLM algorithm has the optimal efficiency performance, while the MPSC algorithm can maximize the power sharing capability of the dual inverter while holding the widest operation range of the motor. Based on the dual space vector pulse-width modulation (SVPWM) control scheme, the golden section (GS) search method is introduced to allocate the stator voltage vector to the dual inverter for the power sharing demand, with a flexible iteration number to obtain a stable and efficient computational performance, which fully utilizes the power sharing capability via the voltage vector distribution. Simulation results prove the validity and the advantages of the proposed strategy. This system also lays a foundation for energy management function of the dual-power electric vehicles.

Capacity prediction of lithium-ion batteries represents an important function of battery management systems. Conventional machine learning-based methods for capacity prediction are inefficient to learn long-term dependencies during capacity degradations. This paper investigates the deep learning method for lithium-ion battery's capacity prediction based on long short-term memory recurrent neural network, which is employed to capture the latent long-term dependence of degraded capacity. The neural network is adaptively optimized by the Adam optimization algorithm, and the dropout technique is exploited to prevent overfitting. Based on the offline cycling aging data of batteries, the capacity prediction performance is validated and evaluated. The experimental results demonstrate that the proposed algorithm can accurately track the nonlinear degradation trend of capacity within the whole lifespan with a maximum error of only 2.84%.

The current research on ensemble clustering mainly focuses on integration strategies, but the attention regarding the measurement and optimization of basic cluster is less emphasized. Based on the information entropy theory, this paper proposes a quality metric of basic cluster, and the clusterings are further selected by incorporating two-branch decisions and three-way decisions respectively. Determined by preset threshold(s), mechanism of two-branch based basic clustering filtering (BCF2BD) and three-way based basic clustering filtering (BCF3WD) are developed. Concretely, the basic clustering in BCF2BD is deleted if the quality metric of it is less than the preset threshold ξ, and the new clustering member is added to maintain the basic cluster set count. The basic clustering in BCF3WD is deleted if the quality metric of it is less than the preset threshold β, retained if the quality metric of it is greater than the preset threshold α, recalculated if the quality metric of it is greater than β and less than α. Both mechanism executed repeatedly until either non-decrement of basic clusters occurred or maximum iteration count reached. Contrastive experiments show that both methods of filtering algorithms can effectively improve the performance of ensemble clustering, and the three-way decisions filtering algorithm get less time consumption.

The vehicle-environment cooperative (VEC) control has shown a great potential to improve vehicle performance. Consequently, it is desirable to further investigate the incorporation of the VEC control. In this context, a novel method is proposed to predict the velocity profile; meanwhile, the potential of the proposed method is exploited to improve energy management performance of plug-in hybrid electric vehicles (PHEVs). In particular, a specific VEC control framework is first introduced based on the mobile edge computation (MEC). On this basis, a compound velocity profile prediction (CVPP) algorithm is developed, which merges the cloud server (CS), MEC servers, and on-board vehicle control unit (VCU), and provides more accurate and reasonable prediction results. Finally, a case study is conducted that applies the proposed CVPP method to energy management of PHEVs. The simulation results manifest that the performance of the proposed energy management strategy (EMS) is dramatically improved after incorporating the forecasted velocity profile information given by the proposed CVPP method.

This paper presents a prescient energy management strategy based on the model predictive control (MPC) for the parallel plug-in hybrid electric vehicles (PHEVs). In this hierarchical strategy, dynamic programming (DP), with its improved calculation speed, is chosen as the solution algorithm to calculate the optimal power distribution combinations in the predicted receding horizon and under the given terminal battery state-of-charge (SOC) terminal constraint. A synthesized velocity profile prediction (SVPP) method is adopted. The macroscopically and microcosmically predicted velocities obtained by the participatory sensing data (PSD)-based method and the Markov chain (MC), respectively, are synthesized by the linear regression method, obtaining the final velocity profile. In the linear regression step, a particle filter (PF) is implemented for the parameter estimation. According to the characteristics of the driving conditions and components, the terminal battery SOC in each control horizon is constrained by a novel method. Finally, we demonstrate the capability of the proposed scheme in terms of fuel economy improvement by comparing the value of this metric with those of other strategies through simulation.