Multi-Objective Optimal Design of Lithium-Ion Battery Cells

Lithium ion battery has been considered as a promising candidate to improve the current fossil fuels based energy economy. Massive efforts have been put in the optimal design of lithium ion batteries with the assistance of simulation models. But to our knowledge, the application of multi-objective optimization in this process has not been well discussed. The purpose of this thesis is to study the multi-objective optimization problems that could be applied on the optimal design of lithium ion batteries with the assistance of simulation models. A two-objective problem is firstly constructed with the performance measures of energy per unit separator area for the discharge rate of 0.5C and the mass per unit separator area. The reaction zone model and genetic algorithm are employed to solve this problem qualitatively. The resulted Pareto front comes out to be a concave curve in the 2D plane of the two performance measures. Three case studies are guided to illustrate the advantages and applications of employing the multi-objective optimization in the design process. A DAE based simulation model is then employed and tuned to have a satisfying fit to the charge and discharge curves for the cycling rates up to 4C. With the assistance of this precise simulation model, the properties of the Pareto front of the two-objective optimization is then validated quantitatively. A three-objective optimization problem with the objectives of energy performance of 0.25C and 4C discharge and mass performance is then constructed to extend the analysis of applications of multi-objective oriented studies in lithium-ion battery designs. The problem is quantitatively resolved with the assistance of the DAE based simulation model and genetic x algorithm. The Pareto front comes out to be a curved surface in the 3D space of the three objectives. The properties of the Pareto front are expected to offer perspectives and references to product designs in the industry.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136070/1/Multi-Objective Optimization Problems for Lithium-Ion Battery Designs.pdfDescription of Multi-Objective Optimization Problems for Lithium-Ion Battery Designs.pdf : Master of Science in Engineering Thesi

Automated system design optimisation

The focus of this thesis is to develop a generic approach for solving reliability design optimisation problems which could be applicable to a diverse range of real engineering systems. The basic problem in optimal reliability design of a system is to explore the means of improving the system reliability within the bounds of available resources. Improving the reliability reduces the likelihood of system failure. The consequences of system failure can vary from minor inconvenience and cost to significant economic loss and personal injury. However any improvements made to the system are subject to the availability of resources, which are very often limited. The objective of the design optimisation problem analysed in this thesis is to minimise system unavailability (or unreliability if an unrepairable system is analysed) through the manipulation and assessment of all possible design alterations available, which are subject to constraints on resources and/or system performance requirements. This thesis describes a genetic algorithm-based technique developed to solve the optimisation problem. Since an explicit mathematical form can not be formulated to evaluate the objective function, the system unavailability (unreliability) is assessed using the fault tree method. Central to the optimisation algorithm are newly developed fault tree modification patterns (FTMPs). They are employed here to construct one fault tree representing all possible designs investigated, from the initial system design specified along with the design choices. This is then altered to represent the individual designs in question during the optimisation process. Failure probabilities for specified design cases are quantified by employing Binary Decision Diagrams (BDDs). A computer programme has been developed to automate the application of the optimisation approach to standard engineering safety systems. Its practicality is demonstrated through the consideration of two systems of increasing complexity; first a High Integrity Protection System (HIPS) followed by a Fire Water Deluge System (FWDS). The technique is then further-developed and applied to solve problems of multi-phased mission systems. Two systems are considered; first an unmanned aerial vehicle (UAV) and secondly a military vessel. The final part of this thesis focuses on continuing the development process by adapting the method to solve design optimisation problems for multiple multi-phased mission systems. Its application is demonstrated by considering an advanced UAV system involving multiple multi-phased flight missions. The applications discussed prove that the technique progressively developed in this thesis enables design optimisation problems to be solved for systems with different levels of complexity. A key contribution of this thesis is the development of a novel generic optimisation technique, embedding newly developed FTMPs, which is capable of optimising the reliability design for potentially any engineering system. Another key and novel contribution of this work is the capability to analyse and provide optimal design solutions for multiple multi-phase mission systems. Keywords: optimisation, system design, multi-phased mission system, reliability, genetic algorithm, fault tree, binary decision diagra

Multi-Criteria Optimization Manipulator Trajectory Planning

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Bäck et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint and end effector traveled distance, trajectory ripple and energy required by the manipulator to reach at destination point. Bearing this ideas in mind, the paper addresses the planning of robot trajectories, meaning the development of an algorithm to find a continuous motion that takes the manipulator from a given starting configuration up to a desired end position without colliding with any obstacle in the workspace. The chapter is organized as follows. Section 2 describes the trajectory planning and several approaches proposed in the literature. Section 3 formulates the problem, namely the representation adopted to solve the trajectory planning and the objectives considered in the optimization. Section 4 studies the algorithm convergence. Section 5 studies a 2R manipulator (i.e., a robot with two rotational joints/links) when the optimization trajectory considers two and five objectives. Sections 6 and 7 show the results for the 3R redundant manipulator with five goals and for other complementary experiments are described, respectively. Finally, section 8 draws the main conclusions

Global Multi-Objective Optimisation utilising Surrogate Models

While global multi-objective optimization problems continue to emerge in aerospace engineering, conventional optimization methods, in particular, evolutionary algorithms such as the Non-dominated Sorting Genetic Algorithm, have shown their capability to solve such problems. However, one distinctive disadvantage of these conventional methods is that they generally require a large number of function evaluations, which makes them incompatible with computationally intensive numerical simulations that are often employed in aerospace design problems. This thesis substantiates the idea that this limitation can be overcome by using surrogate based optimization, in particular multi-objective Bayesian global optimization that utilizes Kriging as a surrogate model and Expected Hypervolume Improvement as an infill criteria. With this approach, it is possible to obtain the Pareto front with a relatively small computational budget. This is demonstrated through test cases that are conducted by solving analytical optimization problems. The results show that Bayesian optimization is able to reduce the function evaluations by 51 times for the bi-objective problem, and by 91 times for the three-objectives problem compared to genetic algorithms. Furthermore, its applicability is tested in two aerospace design problems, where function evaluations were performed through Computational Fluid Dynamics (CFD) and Computational Aeroacoustic (CAA) simulations. The proposed optimization method returns Pareto fronts which contain various design trade-offs that result in improved performance in terms of the desired objectives, with a reasonable number of function evaluations. Firstly, in the aerodynamic shape optimization, it is able to obtain the Pareto front, which contains airfoil designs with a combination of reduced drag and reduced pitching moment. Secondly, the aerodynamic-aeroacoustic shape optimization is performed where the Pareto front is obtained for airfoil designs with three objectives: reduced drag, reduced pitching moment and reduced aeroacoustic noise. This thesis demonstrates the efficiency of the Bayesian global optimization framework by showing how the Pareto front can be obtained at a relatively smaller number of function evaluations compared to some of the conventional multi-objective optimization methods. Moreover, the results obtained from the applied problems verify its capability for practical applications in aerospace design. Hence, the outcomes of this thesis highlight the potential of multi-objective Bayesian global optimization for multidisciplinary design optimization problems in the field of aerospace engineering

Facility layout problem: Bibliometric and benchmarking analysis

Facility layout problem is related to the location of departments in a facility area, with the aim of determining the most effective configuration. Researches based on different approaches have been published in the last six decades and, to prove the effectiveness of the results obtained, several instances have been developed. This paper presents a general overview on the extant literature on facility layout problems in order to identify the main research trends and propose future research questions. Firstly, in order to give the reader an overview of the literature, a bibliometric analysis is presented. Then, a clusterization of the papers referred to the main instances reported in literature was carried out in order to create a database that can be a useful tool in the benchmarking procedure for researchers that would approach this kind of problems

Online and Offline Approximations for Population based Multi-objective Optimization

The high computational cost of population based optimization methods has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise approaches that can significantly reduce the number of function (or simulation) calls required in such optimization methods. This dissertation presents some new online and offline approximation approaches for design optimization. In particular, it presents new DOE and metamodeling techniques for Genetic Algorithm (GA) based multi-objective optimization methods along four research thrusts. The first research thrust is called: Online Metamodeling Assisted Fitness Evaluation. In this thrust, a new online metamodeling assisted fitness evaluation approach is developed that aims at significantly reducing the number of function calls in each generation of a Multi-Objective Genetic Algorithm (MOGA) for design optimization. The second research thrust is called: DOE in Online Metamodeling. This research thrust introduces a new DOE method that aims at reducing the number of generations in a MOGA. It is shown that the method developed under the second research thrust can, compared to the method in the first thrust, further reduce the number of function calls in the MOGA. The third research thrust is called: DOE in Offline Metamodeling. In this thrust, a new DOE method is presented for sampling points in the non-smooth regions of a design space in order to improve the accuracy of a metamodel. The method under the third thrust is useful in approximation assisted optimization when the number of available function calls is limited. Finally, the fourth research thrust is called: Dependent Metamodeling for Multi-Response Simulations. This research thrust presents a new metamodeling technique for an engineering simulation that has multiple responses. Numerous numerical and engineering examples are used to demonstrate the applicability and performance of the proposed online and offline approximation techniques

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

A service oriented architecture for engineering design

Decision making in engineering design can be effectively addressed by using genetic algorithms to solve multi-objective problems. These multi-objective genetic algorithms (MOGAs) are well suited to implementation in a Service Oriented Architecture. Often the evaluation process of the MOGA is compute-intensive due to the use of a complex computer model to represent the real-world system. The emerging paradigm of Grid Computing offers a potential solution to the compute-intensive nature of this objective function evaluation, by allowing access to large amounts of compute resources in a distributed manner. This paper presents a grid-enabled framework for multi-objective optimisation using genetic algorithms (MOGA-G) to aid decision making in engineering design