Cooperative Optimization Research Articles

Powertrain parameters and control strategy optimization of a novel master-slave electric-hydraulic hybrid vehicle

ABSTRACT The electric-hydraulic hybrid vehicle (EHHV) is an important research area of hybrid electric vehicles (HEV), which provides a competitive project compared to other hybrid technologies. This paper conducts comprehensive research on a master-slave electric-hydraulic hybrid vehicle (MSEHHV). After an integrated driving cycle, the battery state of charge (SOC) values for MSEHHV and electric vehicle (EV) are 44.65% and 38.27%. The economy of the MSEHHV is verified, which is obviously superior to the EV. To further explore the energy conservation potential of the MSEHHV, the research proposes a cooperative optimization method of powertrain parameters and control strategy. Specifically, the optimization objective is to improve SOC. The response surface method (RSM) fits the functional relation between design variables and optimization objective. An optimization model is constructed based on the response surface model. Ultimately, the particle swarm optimization (PSO) algorithm is used for the optimal solution to obtain the optimal parameter combination. To evaluate the adaptability of the method, the performance of three models in the actual driving cycle is compared. Simulation results suggest that the energy consumption of the optimized MSEHHV is 33.41% and 6.33% lower than that of EV and initial MSEHHV. The research provides a valuable reference for the optimal design of electric-hydraulic hybrid technology.

Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV.

This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was established. According to the target bearing-only information obtained by each unmanned underwater vehicle's (UUV) detection, the extended Kalman filter algorithm based on interacting with multiple model bearing-only data was used to estimate the target state in a distributed way, and the federal fusion algorithm was used to fuse the estimated results of each UUV. The fused target state was predicted, and, based on the predicted target state, to achieve the persistent tracking of the target, the particle swarm optimization algorithm was used for the online collaborative optimization of the UUV tracking path. The simulation results showed that the multi-UUV distributed fusion filtering algorithm could obtain a better target state estimation effect, and the online path collaborative optimization method based on the prediction of the target state could achieve persistent target tracking.

Calculation and optimization of inter-regional available transfer capability considering wind power uncertainty and system improvement measures

The intermittency and uncertainty of wind energy make transmission congestion management increasingly complex. In this paper, we investigate the impact of wind energy on available transmission capacity (ATC) and then explore ways to mitigate transmission congestion from the grid and load side, respectively. The impact of a stochastic variable is considered by applying Latin Hypercube Sampling (LHS) and backward curtailment techniques to generate typical scenarios. ATC is improved by the optimal allocation of Thyristor Controlled Series Compensation (TCSC) on the grid side and the use of Demand Response (DR) on the load side. The source-grid-load cooperative optimisation model for ATC is solved by an improved particle swarm optimisation (PSO) algorithm. Based on the IEEE-30 bus system, an experimental scheme is designed and analytical calculations are performed. The results show that the joint application of TCSC and DR can help to improve ATC in a comprehensive way. The work done in this paper can achieve the purpose of promoting renewable energy consumption by improving the utilisation efficiency of the transmission network without changing the network structure.

Metal foam reinforced phase change material energy storage device: A collaborative optimization strategy for porosity and container shape

Latent heat thermal energy storage (LHTES) is often employed in solar energy storage systems to improve efficiency. This method uses phase change materials (PCM) as heat storage medium, often augmented with metal foam to optimize heat transfer. In this paper, we introduce a novel approach of altering the container shape to enhance the heat storage effectiveness. LHTES tank with different coning coefficients (C) is designed by reducing the hard-to-melt bottom region. Based on the finite volume, volume averaging, and enthalpy porosity method, the transformation process of a simplified two-dimensional model of the LHTES tank is numerically simulated. Analysis focuses on the evolution of liquid-solid interface, liquid fraction, temperature field, and heat storage performance. The results indicate that the bottom region area enhances the heat storage performance when C < 0.375, whereas for C ≥ 0.375, it hinders heat transfer. When the porosity is 0.93, the newly deformed LHTES tank demonstrates a reduction of up to 6.2% in melting time compared with the basic model. Additionally, it achieves 98% of the basic thermal energy storage. Furthermore, metal foams with varying porosity are analyzed, and a cooperative optimization matching strategy between porosity and shape is obtained. By reducing the porosity to 0.87, the melting time can achieve a 61% reduction at C = 0.125. This study is expected to provide new insights and references for optimizing the design of LHTES devices.

Implementasi Penguatan Pendidikan Karakter Melalui Pembelajaran Jarak Jauh di Masa Pandemi Covid-19

Strengthening character for students is the main task of the world of education starting from pre-school, secondary school, and higher education, because it is a mandate of the Law. Strengthening character must be carried out even in the conditions of the Covid-19 pandemic, where all educational processes must be carried out through distance learning as stated in the Circular Letter of the Minister of Education and Culture. This research aims to photograph and describe how the implementation of character strengthening through distance learning (PJJ) during the co-19 pandemic. This research is a field research with a qualitative descriptive approach. This research is located at Muhammadiyah University Prof. Dr. HAMKA with research informants are the AIKA Institute, lecturers, and students. In obtaining the data, the authors used observation, interview, and documentation techniques. And in analyzing the data obtained by the author using the Miles and Huberman model analysis, with analytical activities including data reduction, data display, and conclusion drawing/verification. The results of this study are (1) the implementation of character values ​​through Al-Islam and Kemuhammadiyahan learning. AIKA is a core curriculum that specifically teaches students deep Islamic values. All AIKA courses must be taken by every student. The output of this learning is the formation of human learners who are pious, have noble character, are progressive and excel in science and technology as the embodiment of tajdid da'wah amar makruf nahi munkar. (2) Optimization of campus cooperation with parents of students. the role of the campus and parents is carried out as an effort to control the process of internalizing character values ​​during learning, because the campus and parents are an inseparable part of the development of students. (3) Through special character improvement programs, such as Student Baitul Arqam (BAM) activities, student recitations, learning to read the Koran, and dhuafa family assistance programs which are carried out in groups by each student taking Muhammadiyah courses.

Distributed Shared Energy Storage Double-Layer Optimal Configuration for Source-Grid Co-Optimization

Shared energy storage is an energy storage business application model that integrates traditional energy storage technology with the sharing economy model. Under the moderate scale of investment in energy storage, every effort should be made to maximize the benefits of each main body. In this regard, this paper proposes a distributed shared energy storage double-layer optimal allocation method oriented to source-grid cooperative optimization. First, considering the regulation needs of the power side and the grid side, a distributed shared energy storage operation model is proposed. Second, a distributed shared energy storage double-layer planning model is constructed, with the lowest cost of the distributed shared energy storage system as the upper-layer objective, and the lowest daily integrated operation cost of the distribution grid-distributed new energy stations as the lower-layer objective. Third, a double-layer iterative particle swarm algorithm combined with tide calculation is used to solve the distributed shared energy storage configuration and distribution grid-distributed new energy stations’ economic operation problem. Finally, a comparative analysis of four scenarios verifies that configuring distributed shared energy storage can increase the new energy consumption rate to 100% and reduce the net load peak-valley difference by 61%. Meanwhile, distributed shared energy storage operators have realized positive returns.

A discrete-time distributed optimization algorithm for cooperative transportation of multi-robot system

Multi-robot cooperation is a typical application of multi-robot system, which has strong potential applications in many special occasions. However, few scholars have considered cooperative optimization transportation from the perspective of optimization. This paper investigates the cooperative transportation optimization problem for multiple mobile robots with kinematically redundant manipulators. First, a discrete-time distributed optimization algorithm with fixed step size is proposed to achieve linear convergence. Second, by introducing a rotation variable into the optimization problem, the joint angular velocity of manipulator and the velocity of moving platform change more smoothly. Third, a virtual leader-follower transport strategy is used in this paper to improve the stability of the multi-robot system. In the physical experiment, the trajectory of each end-effector matches the expected trajectory, and the center trajectory of the transport object almost coincides with the virtual leader trajectory with acceptable error. Moreover, the simulation and physical example are provided to demonstrate the effectiveness of the proposed algorithm.

A bimetallic induced enhanced 3D electron transport network supported by micro constrain area of balls-in-ball structure used for high performance sodium storage

Metal selenides' reversible capacity and rate performance are greatly constrained by their low intrinsic conductivity, severe volume expansion, and low initial coulombic efficiency, therefore selenides with high theoretical capacities cannot be employed as efficient anode for sodium ions batteries. Here, we report a simple and feasible methodology for synthesizing (M−Mn)Se/C (M = Co, Ni) heterogeneous anode materials based on Kirkendall diffusion process. A heterogeneous balls-in-ball structure is created when the nanospheres are concentrated in a microsphere. The confinement effect of the nano-scale enables each microsphere to act an effective reaction micro-region. The transport of sodium ions is constrained more by electrons when the electrode material reaches the nanoscale. In terms of composition and structure, the balls-in-ball structure with heterogeneous composition greatly promotes the electron transfer. The bimetal-regulated heterogeneous (M−Mn)Se/C electrode exhibits bonding energy changed under the synergistic effect between the two metal metals, thus forming significant quantity of built-in electric field to promote electron transport. The amount of MnSe is distributed uniformly around each MSe in every single micro-sphere. The built-in electric field establish an efficient 3D electron transport network, thus making the optimization in composition substantially boosted by a sensible structural design. (M−Mn)Se/C showed excellent sodium storage performance through the cooperative optimization of composition and structure. To our expectation, (CoMn)Se/C delivered a high reversible capacity (597 mAh·g−1 at 100 mA⋅g−1) and enduring cyclic stability (retention rate of 97.8% after 2000 cycles at 5000 mA⋅g−1). In addition, a full cell assembled by Na3V2(PO4)3 showed a high capacity of 350 mAh/g and a ultra-high energy density of 162.2Wh/kg. This work provides a simple method for constructing the anode electrode of high-performance sodium ions batteries.

Weight Factor Graph Co-Location Method for UAV Formation Based on Navigation Performance Evaluation

At present, multiple unmanned aerial vehicles (UAVs) collaboration have attracted people’s more and more attention. The abundant relative distance information in UAV cooperative network can be employed to further improve UAV’s positioning accuracy. However, the traditional distance-based cooperative localization (CL) algorithm does not consider the performance of cooperative navigation information. Cooperative information with poor navigation performance will affect follower UAV’s state estimation accuracy. Thus, this paper proposes a weight factor graph CL method based on navigation performance evaluation. A novel navigation performance evaluation strategy based on Fisher information and relative entropy is proposed. The distance observations and cooperative nodes’ positions with higher estimation accuracy and contribution are selected to estimate follower UAV’s position. Considering the difference contributions of the selected cooperative information to follower UAV’s state estimation, we employ the information weight processing to achieve higher state estimation accuracy. Based on the selected cooperative information and weight coefficient, a multi-UAVs cooperative optimization graph model based on navigation performance is constructed. The global optimization cost function is constructed using relative distance constraints from the current time to historical time in the sliding window. The global optimal navigation solution of follower UAV is achieved using iterative weighted least squares method. Simulation and experiment results show that the proposed navigation performance evaluation strategy can effectively screen cooperative information with higher estimation accuracy and contribution for CL. Using the selected high-quality cooperative information, the system computational complexity is reduced without significantly affecting system positioning accuracy.

An Energy-Saving and Efficient Deployment Strategy for Heterogeneous Wireless Sensor Networks Based on Improved Seagull Optimization Algorithm.

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and environmental protection and can also avoid invasive damage caused by traditional data acquisition methods. An adaptive cooperative optimization seagull algorithm for optimal coverage of heterogeneous sensor networks is proposed in order to address the issue of coverage blind zone and coverage redundancy in the initial random deployment of heterogeneous sensor network nodes in the sensing layer of the Internet of Things. Calculate the individual fitness value according to the total number of nodes, coverage radius, and area edge length, select the initial population, and aim at the maximum coverage rate to determine the position of the current optimal solution. After continuous updating, when the number of iterations is maximum, the global output is output. The optimal solution is the node's mobile position. A scaling factor is introduced to dynamically adjust the relative displacement between the current seagull individual and the optimal individual, which improves the exploration and development ability of the algorithm. Finally, the optimal seagull individual position is fine-tuned by random opposite learning, leading the whole seagull to move to the correct position in the given search space, improving the ability to jump out of the local optimum, and further increasing the optimization accuracy. The experimental simulation results demonstrate that, compared with the coverage and network energy consumption of the PSO algorithm, the GWO algorithm, and the basic SOA algorithm, the coverage of the PSO-SOA algorithm proposed in this paper is 6.1%, 4.8%, and 1.2% higher than them, respectively, and the energy consumption of the network is reduced by 86.8%, 68.4%, and 52.6%, respectively. The optimal deployment method based on the adaptive cooperative optimization seagull algorithm can improve the network coverage and reduce the network cost, and effectively avoid the coverage blind zone and coverage redundancy in the network.

Effect of composite methods on the phase transition and microwave performances of ZnZrNb1.99(Sn0.5W0.5)0.01O8–TiO2 system

AbstractTri‐layer ZnZrNb1.99(Sn0.5W0.5)0.01O8–TiO2–ZnZrNb1.99(Sn0.5W0.5)0.01O8 and random distribution ZnZrNb1.99(Sn0.5W0.5)0.01O8@TiO2 ceramics with different mass fractions of TiO2 were initially synthesized. The effects of the laminated cofiring and the random distribution processes on the crystal structure and microwave dielectric properties of the composite ceramics were investigated. The advantages of the unique tri‐layer architecture were fully demonstrated. It not only allows the effects of the chemical reactions between ZnZrNb1.99(Sn0.5W0.5)0.01O8 and TiO2 can be effectively limited to a narrow region (∼20 μm in width) within the ZnZrNb1.99(Sn0.5W0.5)0.01O8/TiO2 interfaces, and acts as the “glues” to bond each layer well. The layers are well connected and the possibility of deterioration of Q× f values during the modification process can be greatly reduced. When compared with ZnZrNb1.99(Sn0.5W0.5)0.01O8@TiO2 ceramics with random distribution type, the tri‐layer design can produce a roughly 60% improvement in Q× f value with no noticeable loss in dielectric constant while maintaining temperature stability. After sintering at 1340°C for 6 h, ZnZrNb1.99(Sn0.5W0.5)0.01O8–TiO2–ZnZrNb1.99(Sn0.5W0.5)0.01O8 tri‐layer ceramics exhibited excellent dielectric properties (εr = 30.29, Q× f = 56,880 GHz, and τf = −5.73 ppm/°C) with 0.05 wt% TiO2, and the cooperative optimization of microwave dielectric properties was achieved. The current research provides a strategy for synthesizing microwave dielectric devices with high dielectric properties for applications in 5G network communications.

Rapid cooperative optimization of continuous trajectory for electric sails in multiple formation reconstruction scenarios

In satellite formation missions, conventional low-thrust spacecraft need to continuously consume fuel to maintain a stable formation configuration, which limits the mission period. The electric sail is more conducive to long-term scenarios and multiple formation reconstruction due to its propellant-free characteristic. However, electric sails rely on the solar wind to adjust attitude, which results in a slower control. Therefore, the propulsive acceleration of the electric sail is required to be continuous. Besides, a fast and reasonable initial guess is necessary for accurate design methods such as direct and indirect methods in complex formation missions. Accordingly, the rapid optimization of continuous cooperative trajectory of electric sails in multiple formation reconstruction scenarios with the integrative Bezier shaping approach (IBSA) was implemented in this study. Multiple deputy electric sails arrived at multiple determined positions relative to the chief electric sail at the same time and avoided collisions through distance constraints during multiple formation reconstructions. During multiple reconstructions of the formation, the velocity and acceleration of deputy electric sails are constrained to be continuous. In the proposed method, the relative motion is described without simplified steps, which is more suitable for general elliptic orbits. For electric sail formation, the analytical state and analytical acceleration based on the IBSA are derived. Additionally, in the IBSA, the relationship between Bezier coefficients and relative states replaces the boundary constraints and intermediate constraints of the proposed NLP problem, which can greatly improve the solution efficiency. However, in this paper, IBSA not only represents the multi-segment trajectory optimization problem as an overall optimization problem, but also considers multiple electric sails at the same time. To verify the rapidity and accuracy of the proposed method, numerous numerical simulations were carried out on the electric sail formation. Compared with traditional BSA, the IBSA can get better overall index in a shorter computing time. Taking the result of IBSA as the initial value, the Gauss pseudospectral method (GPM) can further optimize the cooperative trajectories and converge to a better result. Through the comparison of the results, it is found that the overall index of the IBSA is only 16.81% different from the GPM but the calculation time is only 0.28% of the GPM, which is of great significance for the design of multiple formation reconstruction missions with long period.

Robust Multi-objective optimal dispatching model for a novel island micro energy grid incorporating biomass waste energy conversion system, desalination and power-to-hydrogen devices

To realize renewable and self-sustainable energy supply in island region, based on geographical characteristics with abundant renewable resources, an optimal model for island micro energy grid (MEG) is designed incorporating biomass waste energy conversion system (ECS), desalination, and power-to-hydrogen (BSP-MEG) Firstly, the mathematical model is designed, including models of power generators, ECS, desalination and power-to-hydrogen (P2H) devices, etc. Next, the multi-objective scheduling optimization model is designed, containing conventional scheduling model (Scheduling optimization objectives and constraints established with minimum operation and environment costs) and stochastic scheduling model (Minimum Conditional Value-at-Risk objective specific to volatility and uncertainty of renewable generations based on robust stochastic optimization method). Then, to solve the multi-objective optimization problem (MOP), a hybrid differential evolution algorithm is proposed based on local optimal and external archiving strategies. Finally, the MEG of YongXing Island is selected as an example. The results show (1) BSP-MEG effectively realized multi-energy cooperative optimization, and promote intra-day peak shaving. (2) BSP-MEG reduced operating costs, environmental costs and Conditional Value-at-Risk (CVaR) by 78.2%, 61.8% and 77.9% respectively, while curtailment rate by 25.6 to 0.9%. (3) Whether in general scenario or worst, BSP-MEG can realize self-production and self-sale of energy and material, of which risk resistance ability is better. (4) By designing local optimal and external archiving strategies, hybrid differential evolution algorithm performs better in solving complex MOP. In general, the optimization model proposed in this paper can improve the utilization of renewable resources, alleviate the shortage of fresh water, and help realize renewable and sustainable energy supply.

Cooperative Optimization of Bus Service and Charging Schedules for a Fast-Charging Battery Electric Bus Network

Owing to the development of fast-charging technologies, the problems of limited driving range and long charging time for battery electric buses (BEBs) can be alleviated. However, fast charging may lead to a high energy cost because of charging during periods of high electricity rates and a high-power peak and consequently enhanced electricity demand cost. The reason is that the bus service and charging schedules can both affect the charging behavior and there is strong interaction between them. However, there is lack of researches to consider these two aspects jointly to optimise the charging behavior of large-scale BEB network. In this study, a BEB network charging optimisation (BEB–NCO) model is proposed to cooperatively optimise the bus service and charging schedules to minimise the charging cost for a fast-charging BEB network. To enhance the computing efficiency for large-scale networks, a heuristic algorithm, an integration of adaptive large neighbourhood searching and branch & bound (ALNS–BB), has been developed. The developed model and the heuristic algorithm were applied to a real-world BEB network in Shenzhen, China. The results show that the ALNS–BB algorithm can reduce the computational time by at least 82.56% and the bus service and charging schedule generated by BEB–NCO model can reduce the charging cost by up to 53.35% compared with the existing strategy. Furthermore, sensitivity analyses were conducted to investigate the impact of the charging power on the charging cost. It is suggested that a charging power of 115 kW is recommended in this case.

Multi-parameter cooperative optimization and solution method for regional integrated energy system

As an energy system that uses the power grid and heat network to coordinate the energy interaction between subsystems in the region, the regional integrated energy system can further reduce the system's operating cost and improve the energy utilization rate. However, in the optimization of regional integrated energy systems, there is difficulty in balancing the accuracy of the results and the calculation time. Therefore, a nonlinear collaborative optimization model of a regional integrated energy system is established in this paper, which fully considers the electricity and heat sharing between energy stations. Subsequently, a three-layer-nested co-optimization method for a regional integrated energy system is proposed. The outer layer, middle layer, and inner layer optimize the rated capacity of the internal combustion engine, electric refrigeration ratio, and partial load rate of the internal combustion engine, respectively. Finally, the three-layer-nested co-optimization method is verified from the total system cost, energy utilization rate, and calculation time. The results show that compared with the exhaustive search method and genetic algorithm, the cost per unit area of the system is about 102.99 ¥/m2 (reduced by 1.00 ¥/m2), the energy utilization rate is 70.47% (increased by 2.43%), and the calculation time is about 50–100 h. The three-layer-nested co-optimization method significantly shortens the model's solution time while ensuring the optimization results' accuracy. Finally, this paper can provide a research idea for optimizing regional integrated energy system design.

Bio-inspired cellular thin-walled structures under impact loading: Modeling and multi-stage cooperative optimization algorithm

Bio-inspired cellular structures provide excellent elements and design concepts for designing thin-walled energy-absorbing structures of vehicles, trains and aircrafts, due to their special structural characteristics and excellent mechanical properties. This study conducts a literature review of bio-inspired cellular energy-absorbing structures with a double-circular shape and multi-objective optimization problems of energy-absorbing structures. A multi-stage cooperative optimization algorithm, which combines the theories of multi-objective optimization and multi-criteria decision making, is proposed for solving the selection and optimization problems of bio-inspired cellular thin-walled structures. Multi-performance characterization analysis and evaluation of seven types of bio-inspired cellular structures under impact loading are carried out, including horsetail grass, bamboo, lotus leaf vein, beetle elytra, lotus root and shrimp dactyl club. Subsequently, comparative analysis and discussion are conducted to verify that this optimization method is feasible and practical. The results show that the optimized results obtained by this proposed algorithm are reliable, and the optimized bio-inspired cellular thin-walled energy-absorbing structure shows better overall characteristics in collisions.

Cooperative optimization of speed planning and energy management for hybrid electric vehicles based on Nash equilibrium

The longitudinal automatic driving of hybrid electric vehicles (HEVs) is a complex multi-objective problem, and the coupling relationship between speed planning and energy management is difficult to reveal using the commonly used hierarchical control algorithm. Thus, in this paper, a collaborative control strategy is proposed for cruise speed optimization and energy management of HEVs based on Nash equilibrium. The strategy in this paper can optimize the control of vehicle-level and hybrid-system-level at the same time. First, this paper builds the key component model of HEV under the Simulink environment, which lays the foundation for vehicle control. Then, a multi-objective problem-solving framework based on game theory is proposed aiming at the economy and safety of HEV cruising. Afterward, a weight factor adjustment method is proposed based on fuzzy logic to judge the brake/drive logic. Finally, the utility functions are used for coordinated control based on Nash equilibrium. The simulation results show that the control method proposed in this paper has improved in various aspects as compared with hierarchical model predictive control, including vehicle following, safety, and comfort. In addition, this strategy can significantly reduce energy consumption costs while meeting the cruise speed requirements of vehicles. In the whole driving cycle, the strategy proposed in this paper has a high termination of SOC and a fuel consumption reduction of 15.33%. It can better deal with the relationship between speed planning and torque distribution than the traditional hierarchical control method.

Co-Optimization of Eco-Driving and Energy Management for Connected HEV/PHEVs near Signalized Intersections: A Review

Currently, road transport constitutes a considerable proportion of global fossil fuel consumption, as well as CO2 and pollutant emissions. To mitigate transportation energy consumption, two primary approaches have emerged: the large-scale adoption of Hybrid Electric Vehicles (HEVs) and Plug-In Electric Vehicles (PHEVs), as well as the implementation of eco-driving strategies, which present an immediate and low-cost solution. In this context, this paper provides a comprehensive review of these two technologies and their integration for connected HEV/PHEVs. We summarize the framework of recent approaches to incorporate fusion road information in single-vehicle and multi-vehicle scenarios, respectively, wherein we compare their advantages, their disadvantages, and their effectiveness in reducing energy consumption. Additionally, we reflect on the future development directions of cooperative optimization in EMS and eco-driving strategies from various perspectives. This comprehensive review underscores the importance and potential impact of these approaches in addressing environmental challenges in transportation systems, thereby offering useful insights for new researchers and practitioners in this area.

Co-optimization method research and comprehensive benefits analysis of regional integrated energy system

The nearly-zero energy community composed of contiguously nearly-zero energy buildings can effectively reduce the operational energy consumption of buildings by adopting appropriate active and passive technologies, which is the main development direction of future buildings. However, there are few studies on the collaborative optimization of nearly-zero energy community supply systems. Therefore, a nonlinear cooperative optimization model of the regional integrated energy system with multi-region energy sharing and multi-energy storage is constructed in this paper. A two-layer collaborative optimization method is proposed, which optimizes the upper layer's renewable energy and energy storage capacity and the operation dispatching in the underlayer. Finally, the overall benefit, typical daily energy scheduling, and the energy sharing and storage impact on renewable energy utilization of the system when it supplies energy to a nearly-zero energy community are studied. The research results show that compared with the isolated integrated energy system, the supply cost, primary energy consumption, carbon emission and interactive power per unit area of the regional integrated energy system are reduced by 3.45 CNY/m2, 3.95 kWh/m2, 1.35 kg/m2 and 1.66 kWh/m2, respectively. In addition, multi-region energy sharing and multi-energy storage can effectively improve the self-consumption/sufficiency ratio of photovoltaic and wind power generation and solar thermal collector heat collection. This study provides a feasible solution for applying nearly-zero energy communities in regional integrated energy systems.

Landing gear condition monitoring based on back propagation neural network-based on multi-strategy cooperative optimization

To effectively monitor the operation state of landing gear, a back propagation neural network-based on multi-strategy cooperative optimization(MSCO-BPNN) is proposed. The multi-strategy optimization algorithm is composed of chaotic mapping strategy, adaptive spiral capture strategy, crossover mutation strategy and whale optimization algorithm(WOA). WOA is applied to find the optimal hyperparameters of back propagation neural network(BPNN). The search efficiency, multi-local search ability and global search performance of model can be improved by using chaotic mapping strategy, adaptive spiral capture strategy and crossover mutation strategy. The BPNN with optimal hyperparameters is introduced to establish the implicit model of input parameters and output responses. Based on quick access recorder(QAR) data, landing gear left side brake temperature is act as the monitoring objective of this paper. The validity and applicability of MSCO-BPNN are verified by compared with WOA-BPNN, particle swarm optimization BPNN and traditional BPNN. The results show that MSCO-BPNN can monitor the operation status of landing gear with high efficiency and accuracy. The efforts of this paper provide a promising insight for the precise condition monitoring of complex structures.