Design and Implementation of Hybrid Energy Storage System Integrating Lithium-ion Battery and Wind Turbine

Abstract

A hybrid energy storage system (HESS) by integrating Lithium-Ion Battery and Wind Turbine System for Electric Vehicle is designed and implemented. An advanced model of lithium ion/wind turbine HESS model is developed to improve the battery’s charging capacity. The behavior of this model is tested by using Machine Learning Algorithms to identify remaining battery capacity of the energy storage components. The Machine learning Algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) Decision Tree (DT) and Linear Regression (LR) are implemented which helps in predicting the State of Charge (SoC) in the electric vehicle to estimate the battery’s lifetime. The SoC of the EV without HESS is calculated and compared with the SoC of HESS implemented EV model. Sensors are used to measure the current and voltage parameters of the Lithium-Ion Battery setup (Internal Battery) and Storage Battery (External Battery). The measured data is sent to the LPC2148 ARM microcontroller Board. The data that is obtained from LPC2148 is given as input to the machine learning algorithms which are executed in the Computer. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square (RMSE), R-Square Error (R2E) and Mean Square Error (MSE) are calculated and the SoC values are predicted. The Random Forest algorithm produces better results for the HESS model as the Error values produced are less than 2.5%. The battery capacity of the electric vehicle has been improved by using external battery that is charged by wind turbine. Thus, the driving range of the electric vehicle implemented with HESS is increased twice when compared to conventional electric vehicle [1].