A multi-physics, fully liquid-cooled battery pack model development for winter-summer driving using a holistic reverse-engineering method
Ratnak Sok, Jin Kusaka
Abstract
Controlling battery temperature can reduce cell aging, internal resistance, and overheating, and improve pack performance. These require advanced battery thermal management systems (BTMS) for all-weather driving. A full liquid-cooled pack has hundreds to thousands of cells and coolant channels. Therefore, designing a full pack requires a thorough understanding of battery thermal, flow, and electrical responses under various driving and thermal conditions. This work presents a holistic reverse-engineering method to model and validate a production-based, liquid-cooled, 75-kWh lithium-ion battery pack, including its BTMS via multi-physics simulation. The model includes 4416 cells, 28 side-coolant lines, 784 coolant flow channels, and all plate bends. The channel geometries (width, height, bend angle, radius, and length) are optimized using a genetic algorithm. Firstly, a design-of-experiment is performed by changing the inlet coolant flow rate ( V c o o l = 0–16 L/min) to measure steady-state and transient pressure drops. A sensitivity analysis of the channel geometries to the coolant flow characteristics is performed for the pack's flow model validation. A full battery-electric SUV equipped with the battery pack and dual e-motors was tested under a 60 km/h driving (winter test with ambient temperature T a = −10 °C) and repeated WLTC and (FTP75+HWFET) cycles in summer (25–40 °C). The pack performances were recorded under battery heating (initial T a , i < initial T b , i ) and cooling ( T a , i > T b , i ) modes. The battery model is based on the 2RC equivalent-circuit model, calibrated against an electrochemical NCA/Gr-SiO x cell model to accelerate simulation time. Using the optimized flow and cell models, the model accurately (over 90 % accuracy) predicts the pack's responses (voltage, state of charge, flow, temperature) under steady-state and dynamic conditions. The detailed approach to building a comprehensive pack model can serve as a guideline for future BTMS development.