A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects

Abstract

The state of energy (SOE) is a key indicator for the energy optimization and management of lithium-ion (Li-ion) battery-based energy storage systems in smart grid applications. To improve the SOE estimation accuracy, a Li-ion battery model is presented in this study against dynamic loads and battery ageing effects. First, an electrical battery model is combined with an analytical model in order to take advantages of both models for accurate prediction of battery terminal voltage characteristics, SOE, and remnant runtime. Second, a novel method to separate the fast and slow dynamics of the electrical battery model is developed, and its superior performance is presented. Third, the effects of the battery initial SOC, load current rate and direction, operating temperature, and ageing level are systematically scrutinized and involved in the proposed model for robust SOE and terminal voltage prediction. Commercial Li-ion batteries are then tested under dynamic loads and at an arbitrary battery ageing level to validate the effectiveness and robustness of the proposed model. The laboratory-scale experimental test results show superb accuracy and reliability of the proposed battery model for estimating battery SOE and terminal voltage under dynamic loads and battery ageing conditions.