{"id":55,"date":"2023-09-26T11:23:45","date_gmt":"2023-09-26T11:23:45","guid":{"rendered":"https:\/\/iea-es.org\/task-32\/?page_id=55"},"modified":"2024-06-07T09:51:24","modified_gmt":"2024-06-07T09:51:24","slug":"storage-models","status":"publish","type":"page","link":"https:\/\/iea-es.org\/task-32\/storage-models\/","title":{"rendered":"Storage models"},"content":{"rendered":"\n

Overview electrical storage models<\/h2>\n\n\n\n
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Model<\/th>Developers & Institution<\/th>Storage Technology<\/th>Model Type<\/th>Software<\/th>External Repository<\/th><\/tr><\/thead>
SoXery<\/a><\/td>BFH<\/a> CSEM<\/a><\/td>Li-Ion (NMC\/LFP\/LTO)<\/td>Ageing (behavioral)<\/td>Python<\/td>github<\/a><\/td><\/tr>
sesame-seed<\/a><\/td>Fraunhofer UMSICHT<\/td>PHS, CAES<\/td>optimisation model – reservoir model<\/td>Python<\/td>github<\/a><\/td><\/tr><\/tbody><\/table>
Overview of electrical storag models developed in Task 32<\/figcaption><\/figure>\n<\/div><\/div>\n\n\n\n

Model descriptions<\/h2>\n\n\n\n

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SoXery<\/h2>\n\n\n\n

The CSEM+BFH Li-ion Battery (LIB) Model, known as SoXery, is an online tool designed to assess battery aging under specific usage conditions. This tool employs a semi-empirical model that takes user-provided power and temperature profiles of the battery, along with cell chemistry, to calculate the aging that the cell will experience over a defined period. SoXery’s outputs include calendar and cycle aging, internal resistance increase, and an unfulfillment map indicating if the cell adhered to the usage profile. It serves as a preliminary tool for users seeking appropriate cell chemistry for a given usage profile or sizing batteries for specific applications. The model consists of two components: the dynamics model and the degradation model. The dynamics model controls voltage, internal resistance, and state of charge (SoC) changes during charge and discharge. It is based on an equivalent circuit model consisting of a voltage source and a variable resistor. The degradation model accounts for calendar and cycle aging. It utilizes stress factors based on parameters like SoC, temperature, c-rate, average SoC, and depth of discharge (DoD). Stress factors determine the degradation rate, allowing the computation of total degradation over time. The model is developed at the cell level and assumes a perfect Battery Management System (BMS). It ensures that the e-rate for the battery and c-rate for the cell are equivalent. The stress factor dependencies on various parameters are derived from experimental data. For example, the temperature dependency for calendar aging is obtained by collecting data for aging at different temperatures. To develop the dynamics model, data from GITT (Galvanostatic Intermittent Titration Technique) tests and EIS (Electrochemical Impedance Spectroscopy) measurements are used to extract OCV-SoC curves and resistance-SoC-temperature maps, respectively. The model is coded in Python and accepts inputs like battery size, chemistry, and usage profile. It outputs battery degradation over the simulation period, including changes in state of health (SoH) and state of resistance (SoR). It also estimates SOC profiles, power input\/output, and unfulfillment of power profile due to SoC limits. <\/p>\n\n\n\n

sesame seed<\/h2>\n\n\n\n

Sesame seed is a toolchain to develop consistent parametrization for Level 1 Reservoir Models fpr optimization models. <\/p>\n\n\n\n

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Overview electrical storage models<\/a><\/div>\n<\/div>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Overview electrical storage models Model Developers & Institution Storage Technology Model Type Software External Repository SoXery BFH CSEM Li-Ion (NMC\/LFP\/LTO) Ageing (behavioral) Python github sesame-seed Fraunhofer UMSICHT PHS, CAES optimisation model – reservoir model Python github Overview of electrical storag models developed in Task 32 Model descriptions SoXery The CSEM+BFH Li-ion Battery (LIB) Model, known […]<\/p>\n","protected":false},"author":569,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-55","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/iea-es.org\/task-32\/wp-json\/wp\/v2\/pages\/55","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/iea-es.org\/task-32\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/iea-es.org\/task-32\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/iea-es.org\/task-32\/wp-json\/wp\/v2\/users\/569"}],"replies":[{"embeddable":true,"href":"https:\/\/iea-es.org\/task-32\/wp-json\/wp\/v2\/comments?post=55"}],"version-history":[{"count":21,"href":"https:\/\/iea-es.org\/task-32\/wp-json\/wp\/v2\/pages\/55\/revisions"}],"predecessor-version":[{"id":137,"href":"https:\/\/iea-es.org\/task-32\/wp-json\/wp\/v2\/pages\/55\/revisions\/137"}],"wp:attachment":[{"href":"https:\/\/iea-es.org\/task-32\/wp-json\/wp\/v2\/media?parent=55"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}