Machine learning-based lithium-ion battery life prediction for electric vehicle applications - Ha, Vo Thanh; Vinh, Vo Quang; Truc, Le Ngoc (International Journal of Power Electronics and Drive Systems (IJPEDS), 2024)
The actual and anticipated battlefield creates a model capable of accurately estimating the lifetime of lithium-ion batteries used in electric cars. This inquiry uses a technique known as supervised machine learning, more particularly linear regression. In lithium-ion batteries, modeling temperature-dependent per-cells is the basis for capacity calculation. When a sufficient quantity of test data is accessible, a linear regression learning method will be utilized to train this model, ensuring a positive outcome in forecasting battery capacity. The conclusions drawn in the article are derived from the attributes of the initial one hundred charging and discharging cycles of the battery, enabling the determination of its remaining power. This determination facilitates the swift identification of battery manufacturing procedures and empowers consumers to detect flawed batteries when signs of performance degradation and reduced longevity are observed. MATLAB simulations have demonstrated the accuracy of the projected results, exhibiting a margin of error of approximately 9.98%. With its capacity to provide a reliable and precise means of estimating battery lifespan, the developed model holds the potential to revolutionize the electric vehicle industry.