A new Training Algorithm for Neuro-Fuzzy Networks

Abstract. In this paper a new iterative construction algorithm for local model networks is presented. The algorithm is focussed on building models with sparsely distributed data as they occur in engine optimization processes. The validity function of each local model is fitted to the available data using statistical criteria along with regularisation and thus allowing an arbitrary orientation and extent in the input space. Local models are consecutively placed into those regions of the input space where the model error is still large thus guaranteeing maximal improvement through each new local model. The orientation and extent of each validity function is also adapted to the available training data such that the determination of the local regression parameters is a well posed problem. The regularisation of the model can be controlled in a distinct manner using only two user-defined parameters. Examples from an industrial problems illustrate the efficiency of the proposed algorithm.

Energy efficient non-road hybrid electric vehicles: advanced modeling and control

Analyzing the main problems in the real-time control of parallel hybrid electric powertrains in non-road applications, which work in continuous high dynamic operation, this book gives practical insight in to how to maximize the energetic efficiency and drivability of such powertrains. The book addresses an energy management control structure, which considers all constraints of the physical powertrain and uses novel methodologies for the prediction of the future load requirements to optimize the controller output in terms of an entire work cycle of a non-road vehicle. The load prediction includes a methodology for short term loads as well as for an entire load cycle by means of a cycle detection. A maximization of the energetic efficiency can so be achieved, which is simultaneously a reduction in fuel consumption and exhaust emissions. Readers will gain a deep insight into the necessary topics to be considered in designing an energy and battery management system for non-road vehicles and that only a combination of the management systems can significantly increase the performance of a controller

Sensitivity based order reduction of a chemical membrane degradation model for low-temperature proton exchange membrane fuel cells

The chemical degradation of the perfluorinated sulfonic acid (PFSA) ion-exchange membrane as a result of an attack from a radical species, originating as a by-product of the oxygen reduction reaction, represents a significant limiting factor in a wider adoption of low-temperature proton exchange membrane fuel cells (LT-PEMFCs). The efficient mathematical modeling of these processes is therefore a crucial step in the further development of proton exchange membrane fuel cells. Starting with an extensive kinetic modeling framework, describing the whole range of chemical processes leading to the membrane degradation, we use the mathematical method of sensitivity analysis to systematically reduce the number of both chemical species and reactions needed to efficiently and accurately describe the chemical degradation of the membrane. The analysis suggests the elimination of chemical reactions among the radical species, which is supported by the physicochemical consideration of the modeled reactions, while the degradation of Nafion backbone can be significantly simplified by lumping several individual species concentrations. The resulting reduced model features only 12 species coupled by 8 chemical reactions, compared to 19 species coupled by 23 reactions in the original model. The time complexity of the model, analyzed on the basis of its stiffness, however, is not significantly improved in the process. Nevertheless, the significant reduction in the model system size and number of parameters represents an important step in the development of a computationally efficient coupled model of various fuel cell degradation processes. Additionally, the demonstrated application of sensitivity analysis method shows a great potential for further use in the optimization of models of operation and degradation of fuel cell components

PROCEEDINGS of the 2013 IFAC Symposium on Intelligent Autonomous Vehicles, IAV 2013

Proceedings of the 2013 Symposium on Intelligent Autonomous Vehicles

Methodology for efficient parametrisation of electrochemical PEMFC model for virtual observers

Determination of the optimal design of experiments that enables efficient parametrisation of fuel cell (FC) model with a minimum parametrisation data-set is one of the key prerequisites for minimizing costs and effort of the parametrisation procedure. To efficiently tackle this challenge, the paper present an innovative methodology based on the electrochemical FC model, parameter sensitivity analysis and application of D-optimal design plan. Relying on this consistent methodological basis the paper answers fundamental questions: a) on a minimum required data-set to optimally parametrise the FC model and b) on the impact of reduced space of operational points on identifiability of individual calibration parameters. Results reveal that application of D-optimal DoE enables enhancement of calibration parameters information resulting in up to order of magnitude lower relative standard errors on smaller data-sets. In addition, it was shown that increased information and thus identifiability, inherently leads to improved robustness of the FC electrochemical model

Reduced-dimensionality nonlinear distributed-parameter observer for fuel cell systems

To ensure reliable and efficient operation of fuel cell systems, it is important to monitor them online. However, placing sensors inside the fuel cell is often challenging, so virtual sensing using an efficient state observer is used in this study. Detecting local internal phenomena, such as reactants’ starvation, membrane dryout/flooding, and nitrogen accumulation, requires knowledge of the spatial distribution of internal states. Lumped-parameter models are not suitable for this, as they use a single variable to describe parameters such as hydrogen concentration. Instead, a high-order distributed-parameter fuel cell model is used to predict the spatial profiles of various internal states. An observer algorithm is employed to correct the predicted quantities using a few measurements taken at the system boundary. This update step only considers dominant dynamics from a reduced model to adjust all system states accordingly, making it computationally efficient and robust. The observer algorithm’s performance was verified against a high-fidelity model through detailed simulations