A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms
Qingdao University · Xi'an Railway Survey and Design Institute · +3 more institutions
Abstract
As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and…
Citation impact
- FWCI
- 24.06
- Percentile
- 100%
- References
- 54
Authors
7Topics & keywords
- Dependability
- Computer science
- Process (computing)
- Data processing
- Data mining
- Algorithm
- Data-driven
- Machine learning
- Affordable and clean energy