Machine Learning-Based Optimization for Battery Pack Cooling
Typ
Projektarbete, avancerad nivå
Project Report, advanced level
Project Report, advanced level
Program
Publicerad
2024
Författare
Abukar, Abubakar
John, Pedro
Boudagh, Francisco
Verde, Salvatore
Wendel, Gabriel
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The thermal management of electric vehicle (EV) batteries is a critical factor in safety, performance and durability. Traditional design methods for cooling channels rely on manual
adjustments, trial and error processes and intensive topology optimizations leading to time con suming approaches and significant limitations in efficiency. This study introduces an innovative
framework that combines machine learning (ML) and computational fluid dynamics (CFD) to
optimize cooling channel designs that circumvent the challenges faced by traditional methods.
These improvements are achieved by using two different components:
The first component is a surrogate model. It is a machine learning model trained on a
large dataset produced through CFD simulations using Star-CCM+. This model significantly
reduces computational costs and time by predicting pressure drops and temperature distributions
based on input geometries and system parameters.
The second component is a genetic algorithm for geometry optimization. This component
generates an optimal geometry by balancing pressure drop and an effective thermal regulation by
using a Non-Dominated Sorting Genetic Algorithm (NSGA-II). This algorithm creates a number
of random solutions and iteratively improves on them to find a Pareto front, which represents
the optimal trade-offs between competing objectives: minimizing pressure drop and maximizing
thermal regulation. The role of the surrogate model is to provide instant feedback on potential
solutions throughout the algorithm, which is vital as the algorithm itself is inherently time
consuming.
By combining these two components, the framework accelerates the design process ensuring
the creation of advanced cooling solutions. The results demonstrate the potential to reduce
computational time while achieving superior performance. This work provides a framework for
future advancements in the thermal management of EV batteries, highlighting the importance
of combining CFD with ML in modern engineering solutions.