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Tesla Model 3 2170 Li-ion Cell Dataset and Battery SOC Estimation Blind Modeling Tool
Borealis
Phillip J. Kollmeyer; Fauzia Khanum; Mina Naguib; Ali Emadi — 2023-01-13 For this dataset tests were performed on cylindrical 2170 form factor Li-ion battery cells from a Tesla Model 3 electric vehicle. The tests include characterization tests (constant current discharges, HPPC, etc) and electric vehicle drive cycles. A portion of the data is provided openly for use in developing state of charge (SOC) estimation algorithms, and a portion is kept hidden and used for blinded testing of algorithms. Algorithms can be submitted for testing via the portal described in the dataset. The blind modeling tool concept is described in detail in the publication "A Blind Modeling Tool for Standardized Evaluation of Battery State of Charge Estimation Algorithms" and in the included presentation "Tesla 2170 Cell Data and SOC Estimation Blind Modeling Tool – Users Guide".
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Convolutional Neural Network Battery SOH Estimation Example Code
Borealis
Chen, Junran; Manivanan, Manjula; Phillip J. Kollmeyer — 2023-08-23 The provided code files are utilized to construct a convolutional neural network (CNN)-based state of health (SOH) estimator using data from Samsung 30T cylindrical 21700 cells. These files encompass essential functions: 1) Preprocessing of original data, including normalization and data splitting, 2) Training the CNN-based SOH estimator, and 3) Evaluating performance and generating result plots for the CNN-based SOH estimator. The comprehensive functionality of these files, as well as detailed discussion of results, are extensively covered in the IEEE Xplore publication titled "A Convolutional Neural Network for Estimation of Lithium-Ion Battery State-of-Health during Constant Current Operation," and supplemented by the accompanying user guide "CNN based SOH estimation code - Users Guide.pdf". The battery aging data used is also open source: “Fifteen minute fast charge aging dataset - Samsung 30T cells”, Borealis Data, 2023. https://doi.org/10.5683/SP3/UYPYDJ
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Battery Aging Dataset for 15 Minute Fast Charging of Samsung 30T Cells
Borealis
Duque, Josimar; Phillip J. Kollmeyer; Naguib, Mina — 2023-09-25 This aging dataset was designed to be used for training/parameterization and testing of machine learning and conventional filter based state of charge and state of health estimation models. A number of characterization tests (HPPC, C/20 charge discharge, etc) are applied to each cell and are followed by repeating series of drive cycle discharges and a fifteen minute fast charge. The characterization and drive cycle tests are repeated until the battery cells reach 70% SOH (around 1500 to 2000 cycles). The rate of aging for each cell is different because each cell is fast charged using a different method (standard CC/CV, boost charge - higher current at low SOC, and two pulsed charge methods). The four cells tested are brand new 3Ah Samsung INR21700-30T lithium ion battery cells. The testing was performed in a thermal chamber at 25 degrees Celsius using an Arbin battery cycler.
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Multi Temperature Li-ion Battery Data - SB LiMotive 5Ah
Borealis
Phillip J. Kollmeyer; Skells, Michael — 2023-08-23 A new 5.2Ah SBLimotive li-ion battery cell was tested in an 8 cu.ft. thermal chamber. A 75amp, 5 volt Digatron Firing Circuits Universal Battery Tester channel with a voltage and current accuracy of 0.1% of full scale was used to perform the testing. A series of tests were performed at six different temperatures, and the battery was charged after each test at 1C rate to 4.2V, 10mA cut off charge current, at battery temperature of 25degC. Tests include characterization tests (HPPC, constant current discharges, etc) and a series of electric vehicle drive cycle power profiles.
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Battery Aging Dataset for HEV Drive Cycles applied to 2.5Ah A123 LFP Cells
Borealis
Phillip J. Kollmeyer; Lempert, Jeremy; Zhao, Ziyu — 2023-09-05 Three 2.5Ah LFP battery cells are cycled until they reached a very low SOH (70% for the 300k km case, 40% for the 206k km case, and 15% for the 95k km case). The tests begin with an HPPC test and two repeated 1C discharge tests, which are followed by 300 repetitions of a power profile calculated for an HEV following the WLTP drive cycle. Each aging case has a different power profile, which is designed based off of an empirical aging model to acheive approximately 95,000 km, 206,000 km, or 300,000 km of driving before the cell ages to 80% SOH. One possible application of the dataset could be parameterizing/training and testing SOC or SOH estimation models for hybrid electric vehicles. Since there are three cases which age at a different rate, two cases could be used to train a machine learning algorithm and the third case could be used to test it, for example. For full details regarding the design of the tests see the Applied Energy paper "Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation".
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Yasa P400 Electric Traction Machine Characterization and Efficiency Data
Borealis
Allca-Pekarovic, Alex; Phillip J. Kollmeyer; Forsyth, Alexander; Emadi, Ali — 2024-03-27 This dataset has results from various tests performed on the YASA P400HC motor from YASA Motors. The first tabs in the spreadsheet are results of measurements performed in a lab setting to characterize the motor's parameters over a wide operating range across four DC bus voltages. From these measurements, analyses have been made and plots are shown which mimic or are the source of many of the figures found in the associated publication. The last tabs of the spreadsheet are results from simulated and experimental drive cycles performed on a dynamometer.
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Fast Charging and Characterization Dataset for Porsche Taycan LG E66 Battery Cell and Module
Borealis
Uwalaka, Lucia; Yao, Qi; Duque, Josimar; Phillip J. Kollmeyer — 2024-10-10 An LG E66 pouch cell and a battery module containing twelve E66 cells (6p2s) were tested. Both the cell and module were taken from a Porsche Taycan EV from a low mileage vehicle. Characterization tests, including numerous drive cycles, constant current discharge rates, and an OCV and HPPC test, were performed at -20, -10, 0, 10, 25, and 40degC. These tests are useful for creating battery terminal voltage models and for creating SOC estimation algorithms using machine learning and filter based techniques. The battery module was opened and instrumented with a battery management system to measure cell voltages and perform cell balancing. The module was also instrumented with numerous temperature sensors, including across the face of the cells so that temperature distribution throughout the module could be measured. The module was placed on a liquid cooling plate, like that in the vehicle, and tested for fast charging rates of 0.5, 1, 1.5, and 2C, as well as with the profile used in the Porsche Taycan vehicle.

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(source : McMaster University Dataverse) AND (auteur : Phillip J. Kollmeyer)

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