Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression

• Published in: IEEE access Vol. 13; pp. 12581 - 12595
• Main Authors: Li, Xinyue, Chu, Jiangwei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Electric vehicles; Electrodes; Estimation; Life assessment; Life prediction; Lithium; Lithium batteries; Lithium battery; Lithium-ion batteries; Neural networks; Prediction models; Predictive models; Protocols; quantile regression; Quantiles; remaining life; State of charge; state of health; support vector network; time convolution network; Vectors

The need for lithium battery life forecast algorithms has increased due to the global acceptance of electric vehicles. To further enhance the forecast's precision and resilience about lithium batteries' remaining life, this study implements quantile regression with in support vector networ...