Design optimization and optimal control for hybrid vehicles

In the context of growing environmental concerns, hybrid-electric vehicles appear to be one of the most promising technologies for reducing fuel consumption and pollutant emissions. This paper presents a parametric study focused on variations of the size of the powertrain components, and optimization of the power split between the engine and electric motor with respect to fuel consumption. To take into account the ability of the engine to be turned off, and the energy consumed to start the engine, we consider a second state to represent the engine: this state permits to obtain a more realistic engine model than it is usually done. Results are obtained for a prescribed vehicle cycle thanks to a dynamic programming algorithm based on a reduced model, and furnish the optimal power repartition at each time step regarding fuel consumption under constraints on the battery state of charge, and may then be used to determine the best components of a given powertrain. To control the energy sources in real driving conditions, when the future is unknown, a real-time control strategy is used: the Equivalent Consumption Minimization Strategy (ECMS). In this strategy, the battery is being considered as an auxiliary reversible fuel reservoir, using a scaling parameter which can be deduced from dynamic programming results. Offline optimization results and ECMS are compared for a realistic hybrid vehicle application.     

Design optimization and fabrication of a hybrid composite flywheel rotor

This paper discusses three different rim design cases of a hybrid composite flywheel rotor using strength ratio optimization. The rotor is composed of four hybrid composite rims. These rims are made from carbon–glass/epoxy with varying volume fractions of hoop wound reinforcements. Optimization is performed to reduce the maximum strength ratio during two rotor states: stationary and the maximum allowable rotational speed. The input specifications for optimization are: maximum useable energy (35 kW h), rotational speed (15,000 rpm), height, and inner radius. In the first case, the rims are wound simultaneously by continuous winding. However, in the second case, the rims are wound separately, and interferences are incorporated for their assembly by press fit. In the third case, a hybrid version of the first two cases is used, whereby two pairs of rims are wound at the same time, and in a secondary operation, the first pair is press fitted to the second pair. Each case has different fabrication costs and different strength ratios. The third case rotor has been successfully manufactured by filament winding with in situ curing, followed by press fit assembly of machined rims.     

Design and multi-objective optimization for a broad self-amplified 2-DOF monolithic mechanism

This paper proposes a structural design and multi-objective optimization of a two-degree-of-freedom (DOF) monolithic mechanism. The mechanism is designed based on compliant mechanism with flexure hinge and is compact in size (126 mm by 107 mm). Unlike traditional one-lever mechanisms, a new double-lever mechanism is developed to increase the working travel amplification ratio of the monolithic mechanism. The ideal amplification ratio, the working travel, the statics and the dynamics of the mechanism are taken into consideration. The effects of design variables on the output responses such as the displacement and first natural frequency are investigated via finite-element analysis based on response surface methodology. The fuzzy-logic-based Taguchi method is then used to simultaneously optimize the displacement and the first natural frequency. Experimental validations are conducted to verify the optimal results, which are compared to those of the original design. On using a finite-element method, the validation results indicated that the displacement and frequency are enhanced by up to 12.47% and 33.27%, respectively, over those of the original design. The experiment results are in a good agreement with the simulations. It also revealed that the developed fuzzy-logic-based Taguchi method is an effectively systematic reasoning approach for optimizing the multiple quality characteristics of compliant mechanisms. It was noted that the working travel/displacement of the double-lever mechanism is much larger than that of the traditional one-lever mechanism. It leads to the conclusion that the proposed mechanism has good performances for manipulations and positioning systems.     

Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

This article proposes an efficient metaheuristic based on hybridization of teaching–learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching–learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.     

Solving constrained optimization problems with a hybrid particle swarm optimization algorithm

This article presents a particle swarm optimization algorithm for solving general constrained optimization problems. The proposed approach introduces different methods to update the particle's information, as well as the use of a double population and a special shake mechanism designed to avoid premature convergence. It also incorporates a simple constraint-handling technique. Twenty-four constrained optimization problems commonly adopted in the evolutionary optimization literature, as well as some structural optimization problems are adopted to validate the proposed approach. The results obtained by the proposed approach are compared with respect to those generated by algorithms representative of the state of the art in the area.     

Hessian approximation algorithms for hybrid optimization methods

This article introduces Hessian approximation algorithms to estimate the search direction of the quasi-Newton methods for solving optimization problems of continuous parameters. The proposed algorithms are quite different from other well-known quasi-Newton methods, such as symmetric rank-one, Davidon–Fletcher–Powell, and Broyden–Fletcher–Goldfarb–Shanno, in that the Hessian matrix is not calculated from the gradient information, rather directly from the function values. The proposed algorithms are designed for a class of hybrid algorithms that combine evolutionary search with the gradient-based methods of quasi-Newton type. The function values calculated for the evolutionary search are used for estimation of the Hessian matrix (or its inverse) as well as the gradient vector. Since the estimation process of the Hessian matrix is independent of that of the gradient vector, more reliable Hessian estimation with a small population is possible compared with the previous methods based upon the classical quasi-Newton methods. Numerical experiments show that the proposed algorithms are very competitive with state-of-the-art evolutionary algorithms for continuous optimization problems.     

Synthesis of hybrid spatial coherence

Spatial coherence plays a major role in characterizing quasi-monochromatic, partially coherent, optical signals. Here a fairly simple system for synthesizing special cases of spatial coherence is proposed. The special cases, called hybrid, include the case of total coherence in the x direction and, simultaneously, total incoherence in the y direction. The optical setup is based on a quasi-monochromatic, spatially coherent light source, such as a laser, and a simple moving optical element.     

基于混合遗传算法的转子系统优化设计

转子系统的临界转速是航空发动机设计过程中的重要参数。在临界转速远离工作转速时,转子系统才能安全可靠的工作。如何设计转子系统的结构使设计后的临界转速达到要求,而且结构改变量尽可能小,是转子动力学最优化设计研究的重点之一。在分析已有研究模型的基础上增加约束条件提出一种更完善的临界转速最优化设计模型,无需考虑设计变量个数和设计临界转速个数的关系,及预先给定的临界转速是否可在设计变量对应的临界转速空间内取到,均能找到满意的设计方案。针对该模型的最优化求解,设计出一种结合遗传算法和复合形方法的混合遗传算法,可以有效的提高搜索到全局最优解的搜索速度。对一转子系统进行临界转速优化设计,验证了该模型可以有效的取得满足设计要求的最优设计方案,适用于工程实际的转子系统临界转速最优化设计过程。     

Engineering optimization using a real-parameter genetic-algorithm-based hybrid method

A hybrid optimization algorithm which combines the respective merits of the genetic algorithm and the simulated annealing algorithm is proposed. The proposed algorithm incorporates adaptive mechanisms designed to adjust the probabilities of the cross-over and mutation operators such that its hill-climbing ability towards the optimum solution is improved. The algorithm is used to optimize the weight of four planar or space truss structures and the results are compared with those obtained using other well-known optimization schemes. The evaluation trials investigate the performance of the algorithm in optimizing over discrete sizing variables only and over both discrete sizing variables and continuous configuration variables. The results show that the proposed algorithm consistently outperforms the other optimization methods in terms of its weight-saving capabilities. It is also shown that the global searching ability and convergence speed of the proposed algorithm are significantly improved by the inclusion of adaptive mechanisms to adjust the values of the genetic operators. Hence the hybrid algorithm provides an efficient and robust technique for solving engineering design optimization problems.     

燃料电池汽车并行复合制动系统开发

通过对复合制动系统功能需求的分析,提出了基于反比例溢流控制的并行复合制动系统方案及其控制系统结构。通过对再生制动控制算法约束条件的分析,提出了基于再生制动系统外特性的再生制动控制算法及其ECE法规修正方法。通过试验获取了控制算法过程控制参数,采用硬件在环的方式对系统的控制效果和制动能量回收效率进行了仿真测试。结果表明,液压制动力能够良好地跟随再生制动控制算法分配的结果,在一次制动过程中,改进后的系统和算法能够提高能量回收效率6.3%。     

Computer controlled piezo micromanipulation system for biomedicalapplications

This paper presents the development of a computer-controlled piezo manipulation system for biomedical applications such as intra-cytoplasmic sperm injection (ICSI). The hardware set-up and control strategies are described in detail to illustrate the advantages of this approach compared to manually based injection methods. When used for ICSI an improved success rate in terms of oocyte survival and fertilisation is achieved     

A hybrid topology optimization algorithm for structural design

A hybrid algorithm for solving structural topology optimization problems is presented. This hybrid algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This hybrid algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value during the optimization. This hybrid algorithm has improved calculation efficiency and accelerated convergence when compared with the MMA or MGCMMA algorithm, which is demonstrated with three examples.     

基于混合PSO算法的桁架动力响应优化

摘 要：本文针对以结构动力响应为约束,最小重量为目标的桁架拓扑优化问题,提出了一种将微粒群算法和优化准则法结合的混合PSO算法。利用优化准则法的迭代关系找出群体中适应度最好的微粒,将其作为特殊微粒,其他微粒的寻优采用PSO的基本进化规则,位移响应约束利用特殊微粒的灵敏度信息近似计算。算例的计算结果表明,混合PSO算法适用于受简谐荷载以及脉冲荷载作用桁架结构的拓扑优化。混合PSO的计算效率比PSO算法高,其优化效果比优化准则法好。     

A two-point spatial mapping method for hybrid vision systems

We propose a new spatial mapping method for hybrid vision systems consisting of an omnidirectional camera and an active perspective camera. We calculate the relative position between the optical centers of the two cameras based on pre-acquisition parameters of two sample points from the observed scene, avoiding the difficulty and error in direct measurement, and at the same time using fewer points. We derive the formula of the relative position between the omnidirectional camera’s optical center and the active camera’s optical center from the distance between two points, and give a method for calculating the corresponding pan and tilt angles of the active camera from the coordinates in the omnidirectional image. Simulation and real experiments show that the two-point spatial mapping method has higher estimation accuracy than the homography calibration method, and at the same time has higher consistency among test points.     

一种高效的匹配场反演混合优化算法

将下山单纯形算法引入差异进化算法,提高了差异进化算法对目标函数梯度信息的利用,改善了差异进化算法的优化效率,由于下山单纯形算法与差异进化算法都是并行算法,混合算法同时具备了并行高效的特点.     

A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.     

Solving constrained optimization problems with hybrid particle swarm optimization

Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder–Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.     

Design optimization and fatigue testing of an electronically-driven mechanically-resonant cantilever spring mechanism

A light scanning device consisting of an electronically-driven mechanically-resonant cantilever spring-mirror system has been developed for innovative lighting applications. The repeated flexing of the cantilever spring during operation can lead to premature fatigue failure. A model was created to optimize the spring design. The optimized spring design can reduce stress by approximately one-third from the initial design. Fatigue testing showed that the optimized spring design can operate continuously for over 1 month without failure. Analysis of failures indicates surface cracks near the root of the spring are responsible for the failures.     

Multiobjective design of actively controlled structures using a hybrid optimization method

A multiobjective approach to the combined structure and control optimization problem for flexible space structures is presented. The proposed formulation addresses robustness considerations for controller design, as well as a simultaneous determination of optimum actuator locations. The structural weight, controlled system energy, stability robustness index and damping augmentation provided by the active controller are considered as objective functions of the multiobjective problem which is solved using a cooperative game-theoretic approach. The actuator locations and the cross-sectional areas of structural members are treated as design variables. Since the actuator locations are spatially discrete, whereas the cross-sectional areas are continuous, the optimization problem has mixed discrete-continuous design variables. A solution approach to this problem based on a hybrid optimization scheme is presented. The hybrid optimizer is a synergetic blend of artificial genetic search and gradient-based search techniques. The computational procedure is demonstrated through the design of an ACOSS-FOUR space structure. The optimum solutions obtained using the hybrid optimizer are shown to outperform the optimum results obtained using gradient-based search techniques.     

Topology optimization of double- and triple-layer grids using a hybrid methodology

In this article, a hybrid methodology combining evolutionary structural optimization (ESO) and gravitational particle swarm (GPS) methods is proposed for topology optimization of double- and triple-layer grids. In the present methodology, which is called the ESO-GPS method, the size optimization of double- and triple-layer grids is first performed by ESO. Then, the outcomes of the ESO are used to improve the GPS through four modifications. Structural weight is minimized against constraints on the displacements of nodes, internal stresses and element slenderness ratio. The GPS is used to investigate the optimum topology of large-scale skeletal structures with discrete variables whose agents update their respective positions by the particle swarm optimization velocity and the acceleration of the gravitational search algorithm. The numerical results show that the proposed algorithm, the ESO-GPS, performs better than the GPS and the other methods presented in the literature.