Capacity Estimation of Lithium-ion Battery with Least Squares Methods

Lithium-ion battery is the most popular battery in the world of electric vehicles; it does have some disadvantages, such as capacity fading and power fading. Capacity fade in a lithium-ion battery is affected by a loss of active electrode material and active lithium, whereas power fade is caused by rise in the battery's internal resistance. In this paper, we use various least square approaches to estimate a lithium-ion batteries capacity and also minimize the sum squared error though Teacher Learning-Based Optimization technique. For this, 8 cells are selected, out of which the most and least degraded cells being trained to determine the parameters of the predicted capacity model, and then the remaining cells being tested against the trained cells to estimate the capacity with that predicted model using different least square methods. From the result, the maximum MAPE has been found to be approximately 1.5 % using all approaches.