Abstract
This study introduces a balancing control strategy that employs an Artificial Neural Network (ANN) to ensure State of Charge (SOC) balance across lithium-ion (Li-ion) battery packs, consistent with the framework of smart battery packs. The model targets a battery pack consisting of cells with diverse characteristics, reflecting real-world heterogeneous conditions. A fundamental aspect of this approach is the ability to bypass individual cells optimally. This key feature stops current flow to and from the cell, allowing it to rest and cool off while avoiding charging or discharging cycles. The implementation of ANN enables adaptive and dynamic management of SOC, which is essential for optimizing performance and extending the lifespan of battery packs. The results demonstrate the effectiveness of the proposed ANN-based balancing strategy in SOC balancing, demonstrating its potential as a critical solution in enhancing battery management systems for electric vehicles.
Original language | English |
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Title of host publication | 2024 IEEE 10th International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia |
Number of pages | 5 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Publication date | 2024 |
Pages | 3997-4001 |
ISBN (Print) | 979-8-3503-5134-7 |
ISBN (Electronic) | 979-8-3503-5133-0 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia) - Chengdu, China Duration: 17 May 2024 → 20 May 2024 https://ieeexplore.ieee.org/xpl/conhome/10567049/proceeding |
Conference
Conference | 2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia) |
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Country/Territory | China |
City | Chengdu |
Period | 17/05/2024 → 20/05/2024 |
Internet address |
Series | International Power Electronics and Motion Control Conference (PEMC) |
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ISSN | 2473-0165 |
Keywords
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