Lightweight design of an electric bus body structure with analytical target cascading

Lightweight designs of new-energy vehicles can reduce energy consumption,thereby improving driving mileage.In this study,a lightweight design of a newly developed multi-material electric bus body structure is examined in combination with analytical target cascading(ATC).By proposing an ATC-based two-level optimization strategy,the original lightweight design problem is decomposed into the system level and three subsystem levels.The system-level optimization model is related to mass minimization with all the structural modal frequency constraints,while each subsystem-level optimization model is related to the sub-structural performance objective with sub-structure mass constraints.To enhance the interaction between two-level systems,each subsystem-level objective is reformulated as a penalty-based function coordinated with the system-level objective.To guarantee the accuracy of the model-based analysis,a finite element model is validated through experimental modal test.A sequential quadratic programming algorithm is used to address the defined optimization problem for effective convergence.Compared with the initial design,the total mass is reduced by 49 kg,and the torsional stiffness is increased by 17.5%.In addition,the obtained design is also validated through strength analysis.

Parameters matching and optimization of parallel hybrid electric vehicle powertrain

Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.

A Multi-Layer Collaboration Framework for Industrial Parks with 5G Vehicle-to-Everything Networks

The fifth-generation(5G)wireless communication networks are expected to play an essential role in the transformation of vertical industries.Among many exciting applications to be enabled by 5G,logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything(V2X)communications.In this paper,a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks.The proposed framework includes three layers:a perception and execution layer,a logistics layer,and a configuration layer.In addition to the collaboration among these three layers,this study addresses the collaboration among devices,edge servers,and cloud services.For effective logistics in industrial parks,task collaboration is achieved through four functions:environmental perception and map construction,task allocation,path planning,and vehicle movement.To dynamically coordinate these functions,device–edge–cloud collaboration,which is supported by 5G slices and V2X communication technology,is applied.Then,the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks.Finally,a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.

Peer-to-Peer Trading in Distribution Systems with the Utility's Operation

To realize a liberalized peer-to-peer (P2P) electricity market in distribution systems with network security, this paper develops a general framework for P2P trading in distribution systems with the utility's operation. The model is formulated as a bi-level programming. The utility's operation is an upper level problem, where a calculation method of network usage charges for P2P trading is also proposed. Peers' P2P trading is a lower level problem. An iterative algorithm based on analytical target cascading (ATC) is proposed to solve the model, where the interactions between utility and peers are presented. Numerical results on the IEEE 33-bus system demonstrate that the proposed method realizes a liberalized P2P market and ensures network security in distribution systems.

Exploring self-organization and self-adaption for smart manufacturing complex networks

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch,short-cycle,and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments,which poses great challenges to manufacturing enterprises.Fortunately,recent advances in the Industrial Internet of Things(IIoT)and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart,flexible,and resilient manufacturing systems.In this context,this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes.Specifically,a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels.Moreover,the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology,which can be added to or removed from the networks in a plug-and-play manner.Materials,information,and financial assets are passed through interactive links across the networks.Subsequently,analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices.Consequently,an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions.The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method,reducing manufacturing cost,manufacturing time,waiting time,and energy consumption,with reasonable computational time.This work potentially enables managers and practitioners to implement active perception,active response,self-organization,and self-adaption solutions in discrete manufacturing enterprises.

Risk-aware Distributed Optimal Power Flow in Coordinated Transmission and Distribution System

Active distribution grids cause bi-directional power flow between transmission system(TS)and distribution system(DS),which not only affects the optimal cost but also the secure operation of the power system.This paper proposes a hybrid coordination method to solve the risk-aware distributed optimal power flow(RA-DOPF)problem in coordinated TS and DS.For operation risk evaluation,the weather-based contingencies are considered in both TS and DS.A hybrid coordination method is developed that entails analytical target cascading(ATC)and Benders decomposition(BD).Moreover,the risk-aware optimal power flow(RAOPF)in TS and risk-based security-constrained optimal power flow in DS have been performed using the BD method considering basic optimal power flow as a master problem,whereas N-1 and N-2 contingencies are considered as sub-problems.Different case studies are performed using the IEEE 30-bus system with generation reserves as a TS and a 13-bus system as a DS.The results demonstrate the efficacy of the proposed method.