Component Sizing Optimization Research Articles

Health-Aware Bi-level Optimization of Component Sizing and Real-Time Energy Management in Fuel Cell Hybrid Electric Trucks

Health-Aware Bi-level Optimization of Component Sizing and Real-Time Energy Management in Fuel Cell Hybrid Electric Trucks

Research on Parameter Optimization Design Method for Dual-Motor Coupled Drive System

To improve energy utilization efficiency and extend the driving range of electric vehicles, this paper proposes a Dual-Motor Coupled Drive System (DMCDS) with a simple structure and establishes a dynamic mathematical model to analyze power flow characteristics under different driving modes. Considering the interdependence between the optimization of component sizes and system control in multi-motor drive systems, a two-layer hybrid optimization method is proposed to determine the optimal component sizes, balancing vehicle performance with minimal system energy losses. To evaluate the effectiveness of the proposed optimization design method, extensive simulation analysis was carried out in MATLAB. The results demonstrate that the optimization of motor sizes and gear ratios can enhance the energy efficiency of the drive system. In comparison with prototype scheme before optimization, the high-efficiency region utilization of motors EM_R and EM_S increased by 45% and 48%, respectively. Compared with the prototype and single-motor drive system, the average drive efficiency after optimization increased by 2.5% and 4.2%, respectively, and the energy consumption per 100 km decreased by 3.6% and 6.8%, respectively. These results confirm the efficacy of the proposed optimization design method in achieving an energy-saving effect.

Fuel-Efficiency Improvement by Component-Size Optimization in Hybrid Electric Vehicles

Hybrid electric vehicles (HEV) play an important role in sustainable transportation systems. The component size of HEV plays a vital role in the fuel efficiency of vehicles. This paper presents a divided rectangle (DIRECT) method for component sizing of vehicles to ensure better fuel efficiency and satisfying drivability. A state–space model was used to represent the design problem. A constraint multi-input multi-output optimization problem was solved by our DIRECT optimization algorithm. Efficacy of the algorithm was tested with standard drive cycles, including drive cycles for Indian urban and highway conditions representing various driving scenarios in the country. The simulation results illustrated the effectiveness of the proposed algorithm.

Analysis of the influence of power coupling type and transmission type of the powertrain on the performance of single-motor hybrid electric vehicles

Existing research on parallel hybrid electric vehicles (HEV) mainly focuses on optimizing the component sizes and control strategies of the single-motor parallel hybrid electric powertrain (SMPHP), and less analyzes the influence of powertrain configuration on the performance of the vehicle. Therefore, the influence of the power coupling type and transmission type of the powertrain configuration on the fuel economy and drivability performance of parallel HEVs is studied in this paper. Considering three types of powertrain topologies (P2 torque-coupled, P2 dual-mode coupled and P3 torque-coupled) and two types of automatic transmissions (DCT and CVT), six typical types of SMPHP configurations to be discussed are determined. To obtain their optimal fuel economy and drivability performance, a multi-objective optimization and analysis method based on dynamic programming and multi-objective particle swarm optimization algorithm is proposed to optimize the component sizes and control variables of powertrain configurations. Finally, the optimal performance and component size optimization results of six typical SMPHP configurations are analyzed and compared, and the influence of powertrain configuration on the performance and components sizing of the SMPHP is obtained, which contributes to the configuration design of the parallel hybrid electric powertrain.

Modelling and Analysis of Hybrid PV-PEM Fuel Cell Power System

Among the renewable energy sources, photovoltaics seems to be the most promising source. It has the advantage of being available everywhere, friend of the environment and easily maintainable. Nevertheless, the major disadvantages are high manufacturing cost, low energy conversion efficiency and non-linear characteristics. On the other hand, the fuel cell is undoubtedly the least polluting means of making electrical energy. Indeed, apart from electricity, the reaction between hydrogen and air produces only water vapour. As such, the process does not reject any greenhouse gases, unlike the combustion of fossil fuels. Whether in a transport-type application (bus or car, or stationary, energy systems including PV or Fuel Cell systems have a in such systems, several levels of control are to be studied, modelled and optimized; this last point, according to one or more criteria of the whole system, can intervene at several levels: optimization of the topology as well as optimization of component sizing. This paper addresses the modelling of hybrid PV-PEM fuel cell power system where the whole system components are implemented using Matlab/Simulink environment. The main parts (PV and PEM fuel cell) are analyzed and investigated alone and in hybrid mode using different scenarios tests. The simulation results show the advantages of using such hybrid system in providing electrical energy in different use cases.

A Novel Framework for Simultaneous Topology and Sizing Optimization of Complex, Multi-Domain Systems-of-Systems

Abstract This article presents a novel design framework for topology and component sizing optimization of multi-domain dynamic systems described by conservation laws. Multidisciplinary design optimization (MDO) is a powerful tool for minimizing metrics such as inefficiency and cost for these systems-of-systems (SoS). However, quality of the designs identified from the optimization procedure depends on model accuracy and ability to capture inter-system interactions. This work utilizes a conservation-based, graphical modeling approach to capture physical system dynamics and interactions, and expands it to be used in MDO techniques. This yields three contributions to the literature. First, an augmented graph-based model is provided, expressing continuous and discrete design variable values as changes to vertex size, edge size, and edge connections of the dynamic system graph. Second, a sizing and topology optimization framework is developed using the augmented graph-based model as a basis. Third, analytical and numerical sensitivity functions are derived for a cooling system design problem, stemming from application of the design framework. The design framework is applied to two case studies for cooling subsystem design and electric vehicle (EV) powertrain design, with the goal of optimizing thermal and electrical component sizes, as well as discrete choices in the topology of the system being designed. These case studies provide examples for how the design framework enables analysis of alternatives (AoA) during early design stages.

Optimization of Component Sizing for a Fuel Cell-Powered Truck to Minimize Ownership Cost

In this study, we consider fuel cell-powered electric trucks (FCETs) as an alternative to conventional medium- and heavy-duty vehicles. FCETs use a battery combined with onboard hydrogen storage for energy storage. The additional battery provides regenerative braking and better fuel economy, but it will also increase the initial cost of the vehicle. Heavier reliance on stored hydrogen might be cheaper initially, but operational costs will be higher because hydrogen is more expensive than electricity. Achieving the right tradeoff between these power and energy choices is necessary to reduce the ownership cost of the vehicle. This paper develops an optimum component sizing algorithm for FCETs. The truck vehicle model was developed in Autonomie, a platform for modelling vehicle energy consumption and performance. The algorithm optimizes component sizes to minimize overall ownership cost, while ensuring that the FCET matches or exceeds the performance and cargo capacity of a conventional vehicle. Class 4 delivery truck and class 8 linehaul trucks are shown as examples. We estimate the ownership cost for various hydrogen costs, powertrain components, ownership periods, and annual vehicle miles travelled.

Optimization and operation of integrated homes with photovoltaic battery energy storage systems and power-to-heat coupling

Abstract Renewable energies from residential photovoltaic systems can be used in the electricity sector as well as in the residential heating sector. Therefore, sector coupling, for example by using heat pump systems, is mandatory. Integrated homes use photovoltaic energy for space heating and domestic hot water to contribute to the decarbonization of the heating sector. The economics are a key to increasing the market penetration of integrated homes. Combined operation strategies for the electricity sector as well as for the heating sector can enhance the economics of these systems. This paper analyses different operation strategies for both electrical and thermal systems and examines the gain in efficiency by combined strategies. Furthermore, the dimensioning of the different system components heavily influences the economics of the integrated home. Capital-intensive components, such as photovoltaic generators, battery storage systems, heat pumps, and thermal storage units, play a major role for the profitability. Therefore, the optimization of component sizing is necessary in order to enhance the economics of the system. This paper presents optimization results based on the covariance matrix adaptation evolution strategy. The results of the paper indicate that combined operation strategies as well as the optimization of the component sizes strongly influence the economics of photovoltaic battery energy storage systems with power-to-heat coupling. The impact of the optimization on the economic efficiency is higher compared to the influence of advanced combined operation strategies. The optimization indicates that relatively small storage units are more economical. The use of the heating sector as additional storage for excess photovoltaic energy is only economical to a minor degree, because it reduces the coefficient of performance of the heat pump.

Component sizing optimization of plug-in hybrid electric vehicles with the hybrid energy storage system

The Pontryagin's minimum principle is utilized in this paper to determine the best solution of component sizing and energy management strategy for a plug-in hybrid electric vehicle which is equipped with a hybrid energy storage system. The hybrid energy storage system, including batteries and supercapacitors, is an effective solution to extend battery life span and reduce the vehicle operating cost. The operating costs of different hybrid energy storage system candidates, including fuel cost, electricity cost, and battery degradation cost over 6 consecutive China bus driving cycles, are minimized by using a 2-dimensional Pontryagin's minimum principle algorithm proposed in this paper. The proposed Pontryagin's minimum principle algorithm not only determines the optimal energy management strategy, but also globally finds the optimal battery and supercapacitor sizes. It is shown that the operating cost strictly decreases with increasing battery and supercapacitor sizes. In addition, simulation results show that the operating cost is reduced by up to 28.6% when compared to a conventional hybrid powertrain without supercapacitors. Thus the effectiveness of adopting supercapacitors in plug-in hybrid electric vehicles is verified.

Economic Optimization of Component Sizing for Residential Battery Storage Systems

Battery energy storage systems (BESS) coupled with rooftop-mounted residential photovoltaic (PV) generation, designated as PV-BESS, draw increasing attention and market penetration as more and more such systems become available. The manifold BESS deployed to date rely on a variety of different battery technologies, show a great variation of battery size, and power electronics dimensioning. However, given today’s high investment costs of BESS, a well-matched design and adequate sizing of the storage systems are prerequisites to allow profitability for the end-user. The economic viability of a PV-BESS depends also on the battery operation, storage technology, and aging of the system. In this paper, a general method for comprehensive PV-BESS techno-economic analysis and optimization is presented and applied to the state-of-art PV-BESS to determine its optimal parameters. Using a linear optimization method, a cost-optimal sizing of the battery and power electronics is derived based on solar energy availability and local demand. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, feed-in remuneration, and battery aging. Using up to date technology-specific aging information and the investment cost of battery and inverter systems, three mature battery chemistries are compared; a lead-acid (PbA) system and two lithium-ion systems, one with lithium-iron-phosphate (LFP) and another with lithium-nickel-manganese-cobalt (NMC) cathode. The results show that different storage technology and component sizing provide the best economic performances, depending on the scenario of load demand and PV generation.

Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response

Regarding the importance of supplying energy to regions that are far from power systems, this study is devoted to analyzing and modeling of a stand-alone micro-grid. In spite of many studies in the case of demand response programming for optimal management and operation cost reduction of the micro-grids, and regarding the importance of size optimization of micro-grids, this paper seeks to examine and investigate the ability of demand response programming in the case of component size optimization of a micro-grid. Due to deficiency or unavailability of dispatchable energy recourses, only the nondispatchable renewable energy resources (wind and solar energy) are considered to supply the required energy. Applied strategy for effective component size optimization as well as relevant costs reduction is implemented by reducing or eliminating the mismatch between the generation and consumption profiles by time shift and schedule of dispatchable loads. Furthermore, the effect of demand response utilization on loss of generated energy reduction is studied. The optimized results with and without demand response are extracted and compared to each other.For each case, the optimum configuration was determined. Obtained results indicated that application of the demand response program, reduced the number of required batteries, the required inverter and photovoltaic cells capacity, and, consequently, the total net present costs. Furthermore, demand response implementation reduced the peak of consumed loads and increased the consumed load factor and correlation factor. The micro-grid components have been modeled mathematically within the framework of the mixed integer linear programming method. The optimization program has been performed by HOMER software together with GAMS software via the CPLEX solver.

Layout and Component Size Optimization of Sewer Network Using Spanning Tree and Modified PSO Algorithm

In the paper, a new method is introduced for optimally solve the problem of the layout and component size determination of sewer network. Simultaneously Layout and component size optimization of sewer network problem consists of many hydraulic constraints which are generally nonlinear and discrete; which creates a challenge even to the modern heuristic search methods. An algorithm generation of a predefined number of spanning trees is introduced to generate a predefined number of sewer layouts of a base sewer network in order of increasing length. These generated layouts are sorted in ascending order of total cumulative flow and sorted layouts are individually optimized for sewer components sizing. It has been found that the optimal sewer layout for total system optimization is one where the total cumulative flow has the minimal value. The modified particle swarm optimization (MPSO) algorithm has been used to optimally determine the component sizes of the selected layouts. The proposed method is applied to the Sudarshanpura sewer network (situated in Jaipur, India) design problem. The results are presented for optimal cost vs cumulative flow of the layouts. Further results of MPSO has been compared with the original PSO algorithm.

Bees-algorithm-based optimization of component size and control strategy parameters for parallel hybrid electric vehicles

This paper presents the optimization of key component sizes and control strategy for parallel hybrid electric vehicles (parallel HEVs) using the bees algorithm (BA). The BA is an intelligent optimization tool that mimics the food foraging behavior of honey bees. Parallel HEV configuration and electric assist control strategy were used to conduct the research. The values of the key component size and the control strategy parameters were adjusted according to the BA to minimize the weighted sum of fuel consumption (FC) and emissions, while the vehicle performance satisfies the PNGV constraints. In this research, the software ADVISOR was used as the simulation tool, and the driving cycles FTP, ECE-EUDC and UDDS were employed to evaluate FC, emission and dynamic performance. The results demonstrate that the BA is a powerful tool in parallel HEV optimization to determine the optimal parameters of component sizes and control strategy, resulting in the improvement of FC and emissions without sacrificing vehicle performance. In addition, the BA is able to define a global solution with a high rate of convergence.

Multiobjective Optimization of HEV Fuel Economy and Emissions Using the Self-Adaptive Differential Evolution Algorithm

This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE+EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design.

Simultaneous partial topology and size optimization of a wing structure using ant colony and gradient based methods

This article presents a methodology and process for a combined wing configuration partial topology and structure size optimization. It is aimed at achieving a minimum structural weight by optimizing the structure layout and structural component size simultaneously. This design optimization process contains two types of design variables and hence was divided into two sub-problems. One is structure layout topology to obtain an optimal number and location of spars with discrete integer design variables. Another is component size optimization with continuous design variables in the structure FE model. A multi city-layer ant colony optimization (MCLACO) method is proposed and applied to the topology sub-problem. A gradient based optimization method (GBOM) built in the MSC.NASTRAN SOL-200 module was employed in the component size optimization sub-problem. For each selected layout of the wing structure, a size optimization process is performed to obtain the optimum result and feedback to the layout topology process. The numerical example shows that the proposed MCLACO method and a combination with the GBOM are effective for solving such a wing structure optimization problem. The results also indicate that significant structural weight saving can be achieved.

Component sizing optimization of plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) are considered as one of the most promising means to improve the near-term sustainability of the transportation and stationary energy sectors. This paper describes a methodology for the optimization of PHEVs component sizing using parallel chaos optimization algorithm (PCOA). In this approach, the objective function is defined so as to minimize the drivetrain cost. In addition, the driving performance requirements are considered as constraints. Finally, the optimization process is performed over three different all electric range (AER) and two types of batteries. The results from computer simulation show the effectiveness of the approach and the reduction in drivetrian cost while ensuring the vehicle performance.

Simultaneous Layout and Size Optimization of Water Distribution Networks: Engineering Approach

A heuristic method is presented for the simultaneous layout and component size optimization of water distribution networks. The method is based on the engineering concept of reliability in which the number of independent paths from source nodes to each of the consumption nodes is considered as a measure of reliability. The method starts with a predefined maximum layout which includes all possible and useful connections. An iterative design-float procedure is then used to move from the current to a cheaper layout satisfying a predetermined reliability set by the user. This is achieved via identifying the hydraulically least important pipes and floating the one which would lead to the cheapest layout. A pipe is floated by relaxing its minimum diameter constraint requirement so that the optimization process could eliminate the pipe from the layout by assigning a zero value to its diameter if required. An iterative penalty method is used for design purpose at each iteration. Three different floating procedures are developed and their efficiencies are tested. A heuristic method is also developed to convert the continuous pipe size solution to a set of discrete solutions. The performance of the method for layout optimization of pipe networks is tested against the benchmark example in the literature and the results are presented.