Electric vehicle battery model identification and state of charge estimation in real world driving cycles

This paper describes a study demonstrating a new method of state-of-charge (SoC) estimation for batteries in real-world electric vehicle applications. This method combines realtime model identification with an adaptive neuro-fuzzy inference system (ANFIS). In the study, investigations were carried down on a small-scale battery pack. An equivalent circuit network model of the pack was developed and validated using pulse-discharge experiments. The pack was then subjected to demands representing realistic WLTP and UDDS driving cycles obtained from a model of a representative electric vehicle, scaled match the size of the battery pack. A fast system identification technique was then used to estimate battery parameter values. One of these, open circuit voltage, was selected as suitable for SoC estimation, and this was used as the input to an ANFIS system which estimated the SoC. The results were verified by comparison to a theoretical Coulomb-counting method, and the new method was judged to be effective. The case study used a small 7.2 V NiMH battery pack, but the method described is applicable to packs of any size or chemistry

Accuracy versus simplicity in online battery model identification

This paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, “identification surfaces” are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery’s ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack

Control-oriented thermal modeling methodology for water-cooled PEM fuel-cell-based systems

In this paper, a new control-oriented modeling methodology for the thermal dynamics of water-cooled Proton Exchange Membrane Fuel Cells (PEMFCs) is presented and validated. This methodology is not only useful for control applications, but also can be used for predicting the temperature variation across the stack, allowing to monitor its operation. The methodology has been validated in a real 600-W, 20-cells, water cooled PEMFC, with encouraging results for both the stationary and the transient states. Results show that the proposed methodology is accurate and suitable for control purposes.Peer Reviewe

A Design for Controllability Methodology for PEM Fuel Cells including the effect of Material Surface Defects on the Dynamic System Performance

Electric power has become indispensable for the development of society. Our quality of life is entirely dependent on the availability of electric energy in the industrial, commercial, and residential sectors. Most of this energy is currently obtained from non-renewable sources (oil, natural gas, and coal mainly). Unfortunately, the continuous combustion of these fuels has severely impacted the environment due to the continuous emissions of greenhouse gases. Therefore, the need to explore alternative energy sources in a wide array of applications is essential for the sustainability of our way of life. Hydrogen is one of the promising fuels of the future, which would allow a transition to a cleaner generation matrix. Although hydrogen is mostly obtained from reforming of natural gas, different pathways from renewable resources are developed and being researched. Therefore, the study of devices operating with hydrogen contributes to the construction of a sustainable future. Fuel cells are one of the most effective ways to transform hydrogen into electrical power. By definition, a fuel cell is an electrochemical device capable of producing electrical energy from a fuel and an oxidant. For Proton Exchange Membrane (PEM) fuel cells the fuel is hydrogen, which is supplied to the anode, and the oxidant agent is oxygen (or air) supplied to the cathode. In this research, a methodology is developed for the selection of fuel cells materials, considering how their properties influence the cell dynamic response. To achieve this, a test bench was designed and constructed to characterize the PEM fuel cells dynamic response, and laboratory tests were developed to perform defect characterization. Different membrane assemblies were tested to analyze the impact of their properties on the cell settling time, and therefore, determine its effect on the controllability of the system

Control-oriented model of a membrane humidifier for fuel cell applications

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Improving the humidification of polymer electrolyte membrane fuel-cells (PEMFC) is essential to optimize its performance and stability. Therefore, this paper presents an experimentally validated model of a low temperature PEMFC cathode humidifier for control/observation design purposes. A multi-input/multi-output non-linear fourth order model is derived, based on the mass and heat dynamics of circulating air. In order to validate the proposed model and methodology, experimental results are provided. Finally, a non-linear control strategy based on second order sliding mode is designed and analyzed in order to show suitability and usefulness of the approach.Peer ReviewedPostprint (author's final draft

Data-driven diagnosis of PEM fuel cell: A comparative study

International audienceThis paper is dedicated to data-driven diagnosis for Polymer Electrolyte Membrane Fuel Cell (PEMFC). More precisely, it deals with water related faults (flooding and membrane drying) by using pattern classification methodologies. Firstly, a method based on physical considerations is defined to label the training data. Secondly, a feature extraction procedure is carried out to pick up the significant features from vectors constructed by individual cell voltages. Finally, a classification is adopted in the feature space to realize the fault diagnosis. Various feature extraction and classification methodologies are employed on a 20-cell PEMFC stack. The performances of these methodologies are compared