A full lithium-ion battery (LIB) pack has hundreds to thousands of cells, coolant
flow lines and channels, and channel bends to control cell temperature within
its operating window and minimize cell internal resistance, aging, and fire
risk. A 75 kWh LIB pack has four modules, and each has 23–25 bricks. Two
challenges in battery state predictions for hot and subzero temperatures are
battery temperature (Tbatt
) and coolant flow within the whole pack. In this work, a 1D 75 kWh
full-pack model with its thermal management system is developed using a holistic
reverse-engineering method, which can predict Tbatt
at any bricks/modules and inlet/outlet coolant flow characteristics. A
Tesla Model Y equipped with dual e-motors is tested on an in-house
state-of-the-art chassis dynamometer. The test data at V =
60–80 km/h, 100–150 A constant discharge, and Tbatt
= −10°C to 40°C are used to develop the model. The 75 kWh pack model
features 4000+ cylindrical cells (96S46P, Panasonic 21700-format), 20+ coolant
lines (or plates, tubes), and 700+ flow channels. The model considers heat
exchange from cells to the ambient air via coolant (water-glycol), coolant
channel walls, adhesive bonding, trays, and cases. Four forced convective heat
transfer coefficient correlations (α) from the coolant to the
walls are used to predict coolant outlet temperature (T
cool, out
) and Tbatt
at different bricks. Three coolant flow losses correlations
(K) due to pipe friction, and pipe bends are used to
predict the coolant pressure drop ∆Pcool
across the pack. Optimal α and K
correlations are identified using the fully validated pack model, and the
transient temperatures at any cell in bricks and the inlet/outlet coolant flow
characteristics are well predicted with over 90% accuracy. This work provides
guidelines for selecting optimal α and K
correlations to develop any 1D fully liquid-based battery pack models for
all-weather driving.