Theses

In recent years, electrification of automotive powertrains has been increasingly considered and implemented as a key element in attaining new affordable, more efficient, and potentially sustainable forms of transportation. Since they represent a defining element of any electrified vehicle, energy storage systems are receiving an increasing amount of attention from the research community as well as the automotive industry. This is most often aimed towards Li-ion batteries and pursues to improve their performance and lifespan while reducing cost and environmental impact through advancements in the development and manufacturing of the system as well as its operation and recycling.

One of the most effective tools in supporting all lifecycle stages of a battery system is represented by simulation models, ranging from static data-driven representations to complex multidimensional virtual depictions of electrochemical cells. Due to the trade-off between the resources required to develop and the ability to replicate the voltage response as well as the vast array of applications, the equivalent circuit model is one of the most popular options employed for simulating battery systems. While the usefulness of any model is always determined by its structure, accurate and effective parametrisation of the structure in relation to the system to be replicated also represents a highly important prerequisite for all applications.

The aim of this thesis is to discuss the development and evaluation of a novel parametrisation methodology suitable for equivalent circuit models and exemplified for the Li-ion battery use case. The research presented fills a substantial research gap for accurate and effective procedures capable of parametrising the specified model in an automated manner. These attributes reliably allow the use of unconstrained time-domain datasets capturing the voltage response of the system and further strengthen the ability to complement the dataset by using prior models as alternative sources of information.

The development and evaluation of the proposed methodology were supported by a readily available equivalent circuit model supplied by the industrial sponsor. The first part of the research focuses on the assembly of the procedure, which creates an estimate of the model and iteratively improves it by refining the values of its parameters through a series of steps. The second part evaluates the resulting methodology using a wide array of cases defined by the time-domain data and the prior model used for training. The two main aspects investigated are the ability of the parametrisation procedure to efficiently convert time-domain information into an accurate model as well as its ability to reduce the requirement for data by using information supplied by the prior model.

The accuracy and computational efficiency demonstrated endorse the proposed methodology as a state-of-the-art solution. The process was highly consistent in achieving model MSE values below 1E−3 V² across large individual data files used for training as well as validation but also presented values as low as 2.83E−5 V². To obtain an accurate evaluation of the computational efficiency, a partially streamlined version of the proposed methodology was implemented. Despite the non-optimised code, the parametrisation of 409 individual model parameter values distributed across 6 lookup tables using 24.543 hours of data was completed in 999.1 seconds. A comparison study published by Savu et al.[1] in a separate article targeting smaller scale versions suitable for small segments of data demonstrates that the proposed parametrisation principle requires 75 times less computational effort than a readily available global solver, while the expansion to the complete model is also likely to increase this factor exponentially. Further work aimed at computational time optimisation also suggests a margin of improvement, potentially by an order of magnitude, that could be achievable through optimisation of the code and parallelisation techniques.

Outside accuracy and computational efficiency, the results also support the ability of the proposed parametrisation methodologies to include prior information towards improving interpolation and extrapolation capabilities of the model outside its trained regions as well as reducing the amount of data required and, implicitly, testing efforts. To exemplify, a model representative of a Samsung 30Q cell was used as prior information for the parametrisation of a model set to replicate a similar Sony VTC6 cell. An accuracy improvement of over 80% that of a model parametrised using the complete training dataset was achievable by using as low as only 31% of the data in the presence of the informed prior. The consistency of the result was also confirmed by reverted cases employing the model parametrised for the Sony VTC6 as the prior information for models replicating the Samsung 30Q cell. Key factors determining the effectiveness of the technique include the level of similarity between the system producing the prior and the system to be modelled, as well as the distribution of the data relative to operating conditions.

The outcome of the research presents a significant positive impact on multiple elements associated with simulation models. The methodology provides a gateway to efficiently obtaining models in an automated manner with limited time, effort and resources required, hence supporting their adoption in research and development activities as well as frontloading these activities in virtual environments. The process also represents a showcase of compiling unconstrained (‘as found’) datasets into accurate models, reducing the requirements imposed on training data and becoming directly suited to effectively using data lakes. The ability to include prior models as alternative sources of information will enhance the model obtained while employing only an unconstrained dataset and will also reduce the amount of data required and, implicitly, testing efforts.

Lastly, the structure of the methodology can also be interpreted as a prior model update process using new data in a computationally efficient manner, which makes it highly compatible with the concept of digital twins. 

[1] V.-I. Savu, C. Brace, G. Engel, N. Didcock, P. Wilson, E. Kural, and N. Zhang, “Linear Regression-based Procedures for Extraction of Li-ion Battery Equivalent Circuit Model Parameters,” Batteries, Vol. 10, No. 10, 343, 2024, doi: 10.3390/batteries10100343