The development of electric and hybrid vehicle technologies is rapidly moving forwards, with the goal of reducing the impact of and dependency on fossil fuel sources. One major hurdle however limits the deployment of the electric vehicle on a massive scale, and that is the battery pack used in these types of vehicles. As the cells composing a battery pack are electrochemical devices, the degradation of these cells is not yet fully understood and quantified. The aging or degradation of the cells causes deterioration of the two main characteristics of any vehicle: driving range and acceleration.
In this PhD research, an extensive state of the art study on various predictive models has been performed, making the need for a general lifetime modeling tool clear. In order to achieve an all-encompassing model, extensive cell aging tests have been carried out on almost 140 cells for more than 2.5 years, with the goal of understanding and quantifying the influence of external operating conditions on the degradation rate of automotive grade lithium-ion cells. These cells are large-format cells with a nominal capacity of 20Ah, comprised of a Nickel Manganese Cobalt Oxide cathode, and a graphite-based anode, and are a prime candidate for storing energy in automotive applications. From the tests, a unique and large database is constructed, tracking the evolution of cyclable capacity and internal resistance. The developed predictive model is able to quantify the influence of highly dynamic and realistic driving profiles on both capacity fade and internal resistance degradation, with a maximum RMSE value of 5% over a simulation time of 18 months.
The models presented in this PhD dissertation are not only standalone models, but can and are linked to cell-level electrical and thermal models.
This provides the opportunity to estimate not only capacity fade and internal resistance degradation, but also for example the evolution of heat generation, which changes as the cell ages. Electrical response behavior is also investigated and simulated, and this combined modeling approach could enable battery pack designers to test thermal management strategies for long-term adequacy for example, or battery management optimization for total lifetime extension.
Date of Award18 May 2018
Original languageEnglish
Awarding Institution
  • Vrije Universiteit Brussel
SupervisorJoeri Van Mierlo (Promotor) & Peter Van Den Bossche (Promotor)

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