Thermal management in electrical vehicle batteries using evolutionary algorithms
The escalating concerns over greenhouse gas emissions have catalysed the development and widespread adoption of electric vehicles (EVs) and smart grid systems. This transition is buoyed by global environmental regulations, growing consumer climate consciousness, and the expansion of EV charging infr...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/177205 |
| _version_ | 1811681747176259584 |
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| author | Shaurya, Baid |
| author2 | Hung Dinh Nguyen |
| author_facet | Hung Dinh Nguyen Shaurya, Baid |
| author_sort | Shaurya, Baid |
| collection | NTU |
| description | The escalating concerns over greenhouse gas emissions have catalysed the development and widespread adoption of electric vehicles (EVs) and smart grid systems. This transition is buoyed by global environmental regulations, growing consumer climate consciousness, and the expansion of EV charging infrastructures. Lithium-ion batteries are central to this evolution due to their high energy density and effective charge cycles. However, the thermal management of these high-energy-density systems emerges as a critical challenge, with the imperative to ensure thermal safety and optimise performance being paramount for the longevity and reliability of EV batteries.
Contemporary research in battery thermal modelling spans three principal methodologies: white box models based on heat transfer equations, grey box models utilising equivalent circuits, and black box models driven by data analytics. Each approach offers insights into the thermal dynamics of battery operation, yet they are hampered by the computational intensity and inaccuracies stemming from their reliance on specific assumptions and the complex task of determining precise closed-form equation coefficients.
Addressing these limitations, our paper introduces an innovative application of evolutionary algorithms (EAs) to refine the thermal modelling process. Evolutionary algorithms, inspired by natural selection and genetic variations, present a dynamic solution to the computational challenges of thermal management. Our work significantly contributes to the field by proposing a novel evolutionary algorithm framework capable of real-time, iterative computation of thermal management parameters derived from heat diffusion equations. |
| first_indexed | 2024-10-01T03:45:51Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/177205 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:45:51Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1772052024-05-31T15:43:53Z Thermal management in electrical vehicle batteries using evolutionary algorithms Shaurya, Baid Hung Dinh Nguyen Meng-Hiot Lim School of Electrical and Electronic Engineering EMHLIM@ntu.edu.sg, hunghtd@ntu.edu.sg Engineering Evolutionary algorithm Thermal management The escalating concerns over greenhouse gas emissions have catalysed the development and widespread adoption of electric vehicles (EVs) and smart grid systems. This transition is buoyed by global environmental regulations, growing consumer climate consciousness, and the expansion of EV charging infrastructures. Lithium-ion batteries are central to this evolution due to their high energy density and effective charge cycles. However, the thermal management of these high-energy-density systems emerges as a critical challenge, with the imperative to ensure thermal safety and optimise performance being paramount for the longevity and reliability of EV batteries. Contemporary research in battery thermal modelling spans three principal methodologies: white box models based on heat transfer equations, grey box models utilising equivalent circuits, and black box models driven by data analytics. Each approach offers insights into the thermal dynamics of battery operation, yet they are hampered by the computational intensity and inaccuracies stemming from their reliance on specific assumptions and the complex task of determining precise closed-form equation coefficients. Addressing these limitations, our paper introduces an innovative application of evolutionary algorithms (EAs) to refine the thermal modelling process. Evolutionary algorithms, inspired by natural selection and genetic variations, present a dynamic solution to the computational challenges of thermal management. Our work significantly contributes to the field by proposing a novel evolutionary algorithm framework capable of real-time, iterative computation of thermal management parameters derived from heat diffusion equations. Bachelor's degree 2024-05-27T02:20:05Z 2024-05-27T02:20:05Z 2024 Final Year Project (FYP) Shaurya, B. (2024). Thermal management in electrical vehicle batteries using evolutionary algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177205 https://hdl.handle.net/10356/177205 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Evolutionary algorithm Thermal management Shaurya, Baid Thermal management in electrical vehicle batteries using evolutionary algorithms |
| title | Thermal management in electrical vehicle batteries using evolutionary algorithms |
| title_full | Thermal management in electrical vehicle batteries using evolutionary algorithms |
| title_fullStr | Thermal management in electrical vehicle batteries using evolutionary algorithms |
| title_full_unstemmed | Thermal management in electrical vehicle batteries using evolutionary algorithms |
| title_short | Thermal management in electrical vehicle batteries using evolutionary algorithms |
| title_sort | thermal management in electrical vehicle batteries using evolutionary algorithms |
| topic | Engineering Evolutionary algorithm Thermal management |
| url | https://hdl.handle.net/10356/177205 |
| work_keys_str_mv | AT shauryabaid thermalmanagementinelectricalvehiclebatteriesusingevolutionaryalgorithms |