Increased understanding of hybrid vehicle design through modeling, simulation, and optimization

2010 Fall.Includes bibliographical references.Vehicle design is constantly changing and improving due to the technologically driven nature of the automotive industry, particularly in the hybridization and electrification of vehicle drive trains. Through enhanced design vehicle level design constraints can result in the fulfillment of system level design objectives. These constraints may include improved vehicle fuel economy, all electric range, and component costs which can affect system objectives of increased national energy independence, reduced vehicle and societal emissions, and reduced life-cycle costs. In parallel, as computational power increases the ability to accurately represent systems through analytical models improves. This allows for systems engineering which is commonly quicker and less resource consuming than physical testing. As a systems engineering technique, optimization has shown to obtain superior solutions systematically, in opposition to trial-and-error designs of the past. Through the combination of vehicle models, computer numerical simulation, and optimization, overall vehicle systems design can greatly improve. This study defines a connection between the system level objectives for advanced vehicle design and the component- and vehicle-level design process using a multi-level design and simulation modeling environment. The methods and information pathways for vehicle system models are presented and applied to dynamic simulation. Differing vehicle architecture simulations are subjected to a selection of proven optimization algorithms and design objectives such that the performance of the algorithms and vehicle-specific design information and sensitivity is obtained. The necessity of global search optimization and aggregate objective functions are confirmed through exploration of the complex hybrid vehicle design space. Whether the chosen design space is limited to available components or expanded to academic areas, studies can be performed for numerous design objectives and constraints. The combination of design criteria into quantifiable objective functions allows for direct optimization comparison based on any number of design goals. Integrating well-defined objective functions into high performing global optimization search methods provides increased probability of obtaining solutions which represent the most germane designs. Additionally, key interactions between different components in the vehicular system can easily be identified such that ideal directions for gain relative to minimal cost can be achieved. Often times vehicular design processes require lower order representations or consist of time and resource consuming iterations. Through the formulation presented in this study, more details, objectives, and methods become available for comparing advanced vehicles across architectures. The main techniques used for setting up the models, simulations and optimizations are discussed along with results of test runs based on chosen vehicle objectives. Utility for the vehicular design efforts are presented through comparisons of available simulation and future areas of research are suggested

Hybridization of multi-objective deterministic particle swarm with derivative-free local searches

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts

A Powerful Optimization Tool for Analog Integrated Circuits Design

This paper presents a new optimization tool for analog circuit design. Proposed tool is based on the robust version of the differential evolution optimization method. Corners of technology, temperature, voltage and current supplies are taken into account during the optimization. That ensures robust resulting circuits. Those circuits usually do not need any schematic change and are ready for the layout.. The newly developed tool is implemented directly to the Cadence design environment to achieve very short setup time of the optimization task. The design automation procedure was enhanced by optimization watchdog feature. It was created to control optimization progress and moreover to reduce the search space to produce better design in shorter time. The optimization algorithm presented in this paper was successfully tested on several design examples

A comparison of general-purpose optimization algorithms forfinding optimal approximate experimental designs

Several common general purpose optimization algorithms are compared for findingA- and D-optimal designs for different types of statistical models of varying complexity,including high dimensional models with five and more factors. The algorithms of interestinclude exact methods, such as the interior point method, the Nelder–Mead method, theactive set method, the sequential quadratic programming, and metaheuristic algorithms,such as particle swarm optimization, simulated annealing and genetic algorithms.Several simulations are performed, which provide general recommendations on theutility and performance of each method, including hybridized versions of metaheuristicalgorithms for finding optimal experimental designs. A key result is that general-purposeoptimization algorithms, both exact methods and metaheuristic algorithms, perform wellfor finding optimal approximate experimental designs

Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition

In this article the most fundamental decomposition-based optimization method - block coordinate search, based on the sequential decomposition of problems in subproblems - and building performance simulation programs are used to reason about a building design process at micro-urban scale and strategies are defined to make the search more efficient. Cyclic overlapping block coordinate search is here considered in its double nature of optimization method and surrogate model (and metaphore) of a sequential design process. Heuristic indicators apt to support the design of search structures suited to that method are developed from building-simulation-assisted computational experiments, aimed to choose the form and position of a small building in a plot. Those indicators link the sharing of structure between subspaces ("commonality") to recursive recombination, measured as freshness of the search wake and novelty of the search moves. The aim of these indicators is to measure the relative effectiveness of decomposition-based design moves and create efficient block searches. Implications of a possible use of these indicators in genetic algorithms are also highlighted.Comment: 48 pages. 12 figures, 3 table