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Die Erfindung betrifft ein Verfahren und eine Vorrichtung zur Bestimmung des Ladezustandes (SOC) einer aufladbaren Batterie (106) eines vorgegebenen Batterietyps oder eines damit in einem physikalischen Zusammenhang stehenden Parameters, insbesondere einer in der Batterie enthaltenen Restladungsmenge Q, wobei das Verfahren mittels eines spannungsgeführten Batteriemodells (102) arbeitet, welches für die betreffende Batterie (106) oder einen entsprechenden Batterietyp parametriert wird. Es muss lediglich die Batteriespannung Umess gemessen und dem Batteriemodell (102) als Eingangsgröße zur Verfügung gestellt werden. Weiterhin betrifft die Erfindung ein Verfahren und eine Vorrichtung zur Bestimmung des Gesundheitszustandes (SOH) einer Batterie (102), wobei das Batteriemodell (102), das auch zur Bestimmung des SOC verwendet wird, einen modellierten Batteriestrom Imodliefert. Aus diesem können modellierte Ladungsmengen während Lade- und Entladephasen der Batterie (106) bestimmt und mit gemessenen Ladungsmengen, die aus dem gemessenen Batteriestrom Imessbestimmt werden, verglichen werden. Da das Batteriemodell (102) nicht altert, kann hierdurch der SOH der Batterie bestimmt werden.
Im Batterielabor der Hochschule Offenburg wurde ein neues Verfahren zur Bestimmung von Ladezustand und Gesundheitszustand von Lithium-Ionen-Batterien entwickelt. Es beruht auf der Auswertung von Spannungs- und Strommessungen mit einem mathematischen Batteriemodell. Das Verfahren ist genauer und robuster als Standardverfahren, die auf Ladungszählung beruhen. Zudem ist es numerisch einfacher umzusetzen als andere modellbasierte Verfahren. Wir demonstrieren die Methode mit einer Heimspeicherzelle und einer Elektrofahrzeugzelle.
Die Erfindung betrifft ein Verfahren und eine Vorrichtung zur Bestimmung des Ladezustandes (SOC) einer aufladbaren Batterie (106) eines vorgegebenen Batterietyps oder eines damit in einem physikalischen Zusammenhang stehenden Parameters, insbesondere einer in der Batterie enthaltenen Restladungsmenge Q, wobei das Verfahren mittels eines spannungsgeführten Batteriemodells (102) arbeitet, welches für die betreffende Batterie (106) oder einen entsprechenden Batterietyp parametriert wird. Es muss lediglich die Batteriespannung Umess gemessen und dem Batteriemodell (102) als Eingangsgröße zur Verfügung gestellt werden. Weiterhin betrifft die Erfindung ein Verfahren und eine Vorrichtung zur Bestimmung des Gesundheitszustandes (SOH) einer Batterie (102), wobei das Batteriemodell (102), das auch zur Bestimmung des SOC verwendet wird, einen modellierten Batteriestrom Imodliefert. Aus diesem können modellierte Ladungsmengen während Lade- und Entladephasen der Batterie (106) bestimmt und mit gemessenen Ladungsmengen, die aus dem gemessenen Batteriestrom Imessbestimmt werden, verglichen werden. Da das Batteriemodell (102) nicht altert, kann hierdurch der SOH der Batterie bestimmt werden.
The invention relates to a method and to a device for determining the state of charge (SOC) of a rechargeable battery (106) of a specified battery type or a parameter physically related thereto, in particular a remaining charge amount Q contained in the battery, the method operating by means of a voltage-controlled battery model (102), which is parameterized for the battery (106) in question or a corresponding battery type. It is merely necessary to measure the battery voltage Umess and to provide said battery voltage to the battery model (102) as an input variable. The invention further relates to a method and to a device for determining the state of health (SOH) of a battery (102), wherein the battery model (102) also used to determine the SOC provides a modeled battery current Imod. Modeled charge amounts during charging and discharging phases of the battery (106) can be determined from said modeled battery current and can be compared with measured charge amounts, which are determined from the measured battery current Imess. Because the battery model (102) does not age, the SOH of the battery can thereby be determined.
Grey-box modelling combines physical and data-driven models to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling, as differential equations given by physical laws and neural networks can be combined in a single modelling framework. This simplifies the simulation and optimization and allows to consider irregularly-sampled data during training and evaluation of the model. We demonstrate this approach using two levels of model complexity; first, a simple parallel resistor-capacitor circuit; and second, an equivalent circuit model of a lithium-ion battery cell, where the change of the voltage drop over the resistor-capacitor circuit including its dependence on current and State-of-Charge is implemented as NODE. After training, both models show good agreement with analytical solutions respectively with experimental data.
Lithium-ion batteries show strongly nonlinear behaviour regarding the battery current and state of charge. Therefore, the modelling of lithium-ion batteries is complex. Combining physical and data-driven models in a grey-box model can simplify the modelling. Our focus is on using neural networks, especially neural ordinary differential equations, for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis for the grey-box model. Unknown parameters and dependencies are then replaced by learnable parameters and neural networks. We use experimental full-cycle data and data from pulse tests of a lithium iron phosphate cell to train the model. Finally, we test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.
Proton Exchange Membrane Fuel Cells (PEMFC) are energy efficient and environmentally friendly alternatives to conventional energy conversion systems in many yet emerging applications. In order to enable prediction of their performance and durability, it is crucial to gain a deeper understanding of the relevant operation phenomena, e.g., electrochemistry, transport phenomena, thermodynamics as well as the mechanisms leading to the degradation of cell components. Achieving the goal of providing predictive tools to model PEMFC performance, durability and degradation is a challenging task requiring the development of detailed and realistic models reaching from the atomic/molecular scale over the meso scale of structures and materials up to components, stack and system level. In addition an appropriate way of coupling the different scales is required.
This review provides a comprehensive overview of the state of the art in modeling of PEMFC, covering all relevant scales from atomistic up to system level as well as the coupling between these scales. Furthermore, it focuses on the modeling of PEMFC degradation mechanisms and on the coupling between performance and degradation models.
Nickel cobalt aluminum oxide (NCA) based lithium-ion battery electrodes exhibit a distinct asymmetry in discharge/charge behavior towards high bulk stoichiometry (low state of charge). We show that basic electrochemical relationships, that is, the Nernst equation and the Butler-Volmer equation, are able to reproduce this behavior when a two-step reaction mechanism is assumed. The two-step mechanism consists of (1) lithium-ion adsorption from the electrolyte onto the active material particle surface under electron transfer, and (2) intercalation of surface-adsorbed lithium atoms into the bulk material. The asymmetry of experimental half-cell data of an NCA electrode cycled at 0.1 C-rate can be quantitatively reproduced with this simple model. The model parameters show two alternative solutions, predicting either a saturated (highly-covered) or a depleted surface for high bulk lithiation.
Battery degradation is a complex physicochemical process that strongly depends on operating conditions and environment. We present a model-based analysis of lithium-ion battery degradation in smart microgrids, in particular, a single-family house and an office tract with photovoltaics generator. We use a multi-scale multi-physics model of a graphite/lithium iron phosphate (LiFePO4, LFP) cell including SEI formation as ageing mechanism. The cell-level model is dynamically coupled to a system-level model consisting of photovoltaics, inverter, power consumption profiles, grid interaction, and energy management system, fed with historic weather data. The behavior of the cell in terms of degradation propensity, performance, state of charge and other internal states is predicted over an annual operation cycle. As result, we have identified a peak in degradation rate during the battery charging process, caused by charging overpotentials. Ageing strongly depends on the load situation, where the predicted annual capacity fade is 1.9 % for the single-family house and only 1.3 % for the office tract.
Multi-phase management is crucial for performance and durability of electrochemical cells such as batteries and fuel cells. In this paper we present a generic framework for describing the two-dimensional spatiotemporal evolution of gaseous, liquid and solid phases, as well as their interdependence with interfacial (electro-)chemistry and microstructure in a continuum description. The modeling domain consists of up to seven layers (current collectors, channels, electrodes, separator/membrane), each of which can consist of an arbitrary number of bulk phases (gas, liquid, solid) and connecting interfaces (two-phase or multi-phase boundaries). Bulk and interfacial chemistry is described using global or elementary kinetic reactions. Multi-phase management is coupled to chemistry and to mass and charge transport within bulk phases. The functionality and flexibility of this framework is demonstrated using four application areas in the context of post-lithium-ion batteries and fuel cells, that is, lithium-sulfur (Li-S) cells, lithium-oxygen (Li-O) cells, solid oxide fuel cells (SOFC) and polymer electrolyte membrane fuel cells (PEFC). The results are compared to models available in literature and properties of the generic framework are discussed.