Publications
We invite you to explore our publications and discover the impactful work our researchers are undertaking to drive the future of energy storage and electric mobility.
Dataset and Algorithms
Our commitment to advancing battery technology and electric vehicles is reflected in the high-quality, open-source datasets we provide. This section is dedicated to providing access to the datasets and algorithms that underpin our research.
Vehicles and Student Teams
We are proud to support and collaborate with our student design teams at McMaster University, in the development of advanced battery packs. Our hands-on guidance and state-of-the-art facilities empower students to create high-quality, reliable energy storage solutions that power innovative vehicles.
McMaster Battery Workforce Challenge
The Battery Workforce Challenge is a three-year student competition (2023-2026) that challenges universities and vocational schools from across North America to design, build, test, and integrate an advanced EV battery pack. McMaster University was selected as one of the 12 universities across North America, and one of two universities in Canada, to participate in this competition. You can read more about the McMaster Battery Workforce Challenge team here.
McMaster Solar Car Team
The McMaster Solar Car Team is a student-run organization that designs, constructs, and races solar electric vehicles. McMaster students are able to utilize the state-of-the-art equipment at the battery research labs to design, build, and test the battery pack! You can learn more about the team by visiting their website here.
Ford F150, ICE to EV: Dr. Kollmeyer’s Ph.D. Dissertation
Dr. Kollmery’s Ph.D. thesis at the University of Wisconsin includes the conversion of a Ford F-150 truck from an internal combustion engine (ICE) to an advanced, highly instrumented electric research vehicle (EV). You can read the full specifications of the converted vehicle here and his complete dissertation here.
Corbin Sparrow: Dr. Kollmeyer’s Masters Project
Stellantis Project
Patents
Battery State of Charge (SOC) Estimator Standardized Testing Tool
There are hundreds of approaches to estimating battery state of charge (SOC), some better suited for specific applications, which makes it very hard to fairly compare results reported in different papers. SOC estimation techniques such as coulomb counting or OCV vs. SOC relationships can be subject to nuances in data and conditions that skew results. Some of these issues include sensor errors, initial value errors, or other complex factors affecting battery performance (e.g., cold weather, cell internal resistance).
This standardized model testing tool is an effective method developed to enable a fair comparison of SOC estimators from various sources and authors, eliminating bias as a result of human and/or external factors by testing models over a wide range of conditions on data which is blinded from the user. The submission process is detailed here, making it convenient for students or researchers to access the standardized model testing tool and open access data and to validate their algorithms.
The initial step is to download the open data here and to create an SOC estimator with the data. For more detailed information see the User’s Guide.
Then follow these procedures to utilize the SOC Estimation Standardized Blind Model Testing Tool :
Expandable List
**Create .p version of a .m file by typing ‘pcode model.m’ in the command window
*Learn more about p-code file here.
NOTE: All code submitted to the blind modelling tool will be kept confidential and will only be used to determine results.
*Access the form here.
*If email is not received within one hour, the test system is likely down. Send email to Dr. Kollmeyer requesting support.
***If email is not received within one hour and confirmation email (step 6) was received, then the model has an error
**Investigate and correct cause of error and re-submit (see step 4)
*If issues cannot be resolved, send email to Dr. Kollmeyer requesting support.
*See ITEC 2022 paper for complete description of test cases: A Blind Modeling Tool for Standardized Evaluation of Battery State of Charge Estimation Algorithms.
*Charging error high for coulomb counting because charge starts around 0% SOC, and coulomb counting code assumes all test cycles start at 100% SOC.
*LTSM more accurate, but somewhat noisy.
*Charging case starts at 0% SOC – causing high error for coulomb counting which assumes 100% SOC at start of file.
*LSTM performs well, except for HWGRADE where high power results in high error.
*Coulomb counting error higher at low temperature due to cell aging (cold temperature tests done last) LSTM error higher at low temperature due to nonlinear, higher resistance of cell at low temperatures.
*Coulomb counting error higher at low temperature due to cell aging (cold temperature tests done last) LSTM error higher at low temperature due to nonlinear, higher resistance of cell at low temperatures.
*Coulomb counting can’t adjust to initial SOC (assumes 100% at start of file) LSTM is able estimate the correct SOC value.
*Coulomb counter integrates current sensor error, LSTM handles offset fairly well at higher temperatures.
*Drive cycle error values can be used for making general comparisons and finding trouble cases (high max error, etc.).
