Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook
- Publisher:
- ELSEVIER SCI LTD
- Publication Type:
- Journal Article
- Citation:
- Journal of Cleaner Production, 2021, 292
- Issue Date:
- 2021-04-10
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S095965262100264X-main.pdf | Published version | 3.27 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Globally, the research on battery technology in electric vehicle applications is advancing tremendously to address the carbon emissions and global warming issues. The effectiveness of electric vehicles depends on the accurate assessment of key parameters as well as proper functionality and diagnosis of the battery storage system. However, poor monitoring and safety strategies of the battery storage system can lead to critical issues such as battery overcharging, over-discharging, overheating, cell unbalancing, thermal runaway, and fire hazards. To address these concerns, an effective battery management system plays a crucial role in enhancing battery performance including precise monitoring, charging-discharging control, heat management, battery safety, and protection. The goal of this paper is to deliver a comprehensive review of different intelligent approaches and control schemes of the battery management system in electric vehicle applications. In line with that, the review evaluates the intelligent algorithms in battery state estimation concerning their features, structure, configuration, accuracy, advantages, and disadvantages. Moreover, the review explores the various controllers in battery heating, cooling, equalization, and protection highlighting categories, characteristics, targets, achievements, benefits, and shortcomings. The key issues and challenges in terms of computation complexity, execution problems along with various internal and external factors are identified. Finally, future opportunities and directions are delivered to design an efficient intelligent algorithm and controller toward the development of an advanced battery management system for future sustainable electric vehicle applications.
Please use this identifier to cite or link to this item: