Machine Learning-Based Optimization for Battery Pack Cooling
| dc.contributor.author | Abukar, Abubakar | |
| dc.contributor.author | John, Pedro | |
| dc.contributor.author | Boudagh, Francisco | |
| dc.contributor.author | Verde, Salvatore | |
| dc.contributor.author | Wendel, Gabriel | |
| dc.contributor.department | Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Mechanics and Maritime Sciences | en |
| dc.contributor.examiner | Vdovin, Alexey | |
| dc.contributor.supervisor | Vdovin, Alexey | |
| dc.contributor.supervisor | Vivek, Anthony | |
| dc.contributor.supervisor | Koutsimanis, Dimitrios | |
| dc.contributor.supervisor | Alatalo, Viktor | |
| dc.date.accessioned | 2025-02-10T15:15:54Z | |
| dc.date.available | 2025-02-10T15:15:54Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | ||
| dc.description.abstract | 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. | |
| dc.identifier.coursecode | TME180 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/309112 | |
| dc.language.iso | eng | |
| dc.title | Machine Learning-Based Optimization for Battery Pack Cooling | |
| dc.type.degree | Projektarbete, avancerad nivå | sv |
| dc.type.degree | Project Report, advanced level | en |