![]() It could be shown that the suggested method is able to precisely extract the set parameters from the simulated voltage. The method's robustness is verified by fitting the voltage response of a mock battery model with pre-defined parameters. The presented results corroborate that this novel heuristic methodology can very accurately fit the voltage response of the battery, while drastically reducing the dependence of the parametrization on set boundary conditions and initial guess values. This article presents a novel approach to extract the equivalent circuit model parameter from a pulse test. Furthermore, because the entropy change is not dependent on the rate or temperature, the characterization can be performed with the data from several experiments, which improves the accuracy of the results. The advantage of the method is that no dedicated characterization experiment is needed. In this dissertation, a novel entropy change characterization method is presented, which uses the empirical temperature data from the galvanostatic intermittent discharging and charging experiments and the corresponding estimated temperature data to extract the entropy change characteristics. The use of a conventional potentiometric method for entropy change characterization takes several weeks to complete. Furthermore, it results in a low-order model that can be scaled for any battery pack configuration. Module-level characterization inherently includes the manufacturing tolerances between the cells in a module as well as the effects of the cooling system into the model. A semi-empirical approach, in which the battery model consists of coupled lumped-parameter electrical and thermal models and the characterization is performed on the basis of current, voltage, and temperature data, is adopted in this dissertation.Ī systematic methodology is presented for the characterization of the capacity, open-circuit voltage, internal impedance, entropy change, and thermal properties by using only two types of experiments, which can be applied for commercial battery modules: (i) galvanostatic intermittent discharging and charging and (ii) continuous thermal loading. The aim is to reduce the time and effort involved in the experimental testing and parameterization as well as to improve the accuracy of the This dissertation proposes efficient and effective methods for the electrical and thermal characterization of lithium-ion batteries. The entropy change characteristics, which can be either exothermic or endothermic, are strongly dependent on the materials and composition of the electrodes, and thus, the entropy change characteristics need to be obtained experimentally. Batteries also exhibit reversible heat generation, which is associated with the entropy change in the electrodes resulting from structural changes caused by the intercalation of lithium ions during charging and discharging. The characterization of batteries is a time-consuming task, because very slow dynamics are present and the characteristics are strongly affected by the state of charge, discharge rate, and temperature. The entire process and various nuances are demonstrated using data collected from a lithium ion battery, and the focus is on applications for energy storage in automotive applications.Ĭommercial lithium-ion battery modules are commonly used to form battery packs for hybrid and electric non-road mobile machinery. ![]() ![]() Compared with previous methods, this approach is much faster and provides the user with information on the order of the system without placing an a priori structure on the system matrices. This paper describes a comprehensive identification algorithm that uses linear-algebra-based subspace methods to identify a parameter varying state variable model that can describe the input-to-output dynamics of a battery under various operating conditions. Consequently, the model identification process can be very laborious and time-intensive. ![]() These models, formally known as linear parameter varying (LPV) models, tend to be difficult to identify because they contain a large amount of coefficients that require calibration. Because models used for these approaches are typically low in order and complexity, the traditional approach is to identify linear (or slightly nonlinear) models that are scheduled based on operating conditions. Among methods used to design various components of a BMS, such as state-of-charge (SoC) estimators, model based approaches offer a good balance between accuracy, calibration effort and implementability. The advent of hybrid and plug-in hybrid electric vehicles has created a demand for more precise battery pack management systems (BMS). ![]()
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