Predictive Battery Thermal Management of Electric Vehicles using Deep Learning
Overview & Synopsis
Improving the energy efficiency of battery electric vehicles increases their range and reduces well-to-wheel emissions. An efficient battery thermal management reduces the energy consumption while taking temperature- dependent battery ageing and power availability into account. This work presents a method for a predictive cooling strategy to reduce the energy consumption, using information about the route ahead and Quantile Neural Networks (Q*NN) for accurate predictions.
Indexed from repository: Directory of Open Access Books
(
www.doabooks.org)
Keyboard Shortcuts:
← / → for pages • + / - for zoom • 0 to fit width
Powered by Mozilla PDF.js v3.11
Document Information
| Document Title | Predictive Battery Thermal Management of Electric Vehicles using Deep Learning |
|---|---|
| Author / Organization | Billert, Andreas M. |
| Subject & Discipline | Batteriethermomanagement |
| Categories | Open Access Books |
| Publication Year | 2025 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
Directory of Open Access Books
(www.doabooks.org)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-306834 |
| Original Source Link | Visit Source Page on www.doabooks.org » |