# A Strategic Pathway from Cell to Pack-Level Battery Lifetime Model Development
###### Abstract  
The automotive energy storage market is currently dominated by the existing Li-ion technologies that are likely to continue in the future. Thus, the on-road electric (and hybrid) vehicles running on the Li-ion battery systems require critical diagnosis considering crucial battery aging. This work aims to provide a guideline for pack-level lifetime model development that could facilitate battery maintenance, ensuring a safe and reliable operational lifespan. The first of the twofold approach is a cell-level empirical lifetime model that is developed from a lab-level aging dataset of commercial LTO cells. The model is validated with an exhaustive sub-urban realistic driving cycle yielding a root-mean-square error of 0.45. The model is then extended to a 144 SIP modular architecture for pack-level simulation. The second step provides the pack electro-thermal simulation results that are upscaled from a cell-level and validated 1D electrical model coupled with a 3D thermal model. The combined simulation framework is online applicable and considers the relevant aspects into account in predicting the battery system's lifetime that results in over 350,000 km of suburban driving. This robust tool is a collaborative research outcome from two Horizon2020 EU projects--GHOST and Vision xEV, showcasing outstanding cell-level battery modeling accuracies. B 20  
limited operating conditions, and no physical knowledge make the ML methods futile considering safety and reliability [10]. In contrast, upscaling a cell-level model to pack-level considering relevant electrical and thermal aspects is straightforward and computationally inexpensive for the choice of battery management system (BMS) implementation [11; 12]. Such an approach also allows for a multi-condition analysis of the operating environment broadening the model boundaries, thus increasing robustness. This research work proposes an efficient methodological process that would facilitate a pack-level battery lifetime model development strategy. The pathway starts from the cell-level study stretching the boundary limit beyond the possible real-life scenarios including temperature, depth of discharge (DoD), state of charge (SoC) range, current rate (C-rate), and calendar life SoC, temperature, etc., variations. Such studies exist in the literature based on different Li-ion technologies followed by behavioral analysis and modeling [13; 14]. The lifetime prediction types typically follow an imprecise definition of aging that can be classified as either cycle life and/or calendar life degradation in the form of capacity fade and/or internal resistance increase (IR). The model methodological choice also varies depending on the study type and application requirements where a compromise of model complexity, accuracy, and computational effort is accepted [15]. The empirical equation-based models fitted to the laboratory experimental data are found to be a good fit that could ensure high accuracy, online applicability, and real-time computing with a less complex flexible framework [16]. Hence, the representative models are often cell-level demonstrations and only a few researchers have showcased the real-life validation [17; 18]. Nevertheless, the battery system or pack-level upscaling of a cell-level model is rare, challenging, and requires relevant consideration of pack topology, electrical and thermal properties, etc. Investigating the aging of a battery pack that is a multi-cell configuration is usually a more complicated phenomenon where non-linear degradation, cell-to-cell variation, cell imbalance, inefficient thermal management, sudden impedance growth, mechanical stresses, etc., worsen the already existing interrelated aging mechanisms. It is extremely challenging to map all the external factors counting their contributions to the total degradation. However, researchers have studied one or more of these elements to investigate pack-level health [19].