A Multiple Time-Scales Based Multi-state Co-estimation Method for Lithium-ion Battery

Abstract

As the core power source of electric vehicles (EVs), the lithium-ion batteries are supposed to work in safe and efficient condition, and an effective and stable battery management system (BMS) is the premise. In order to assure the batteries are working in an appropriate condition, the BMS need to be designed with the capability of monitoring the working information and internal states of battery. In this paper, considering the variation characteristics of the state of charge (SOC), the state of health (SOH) and the state of power (SOP) at different time scales and the coupling relationship between them, a multiple time scales based multi-state estimation method for lithium-ion batteries is proposed. First, an integrated model-based SOC estimation method is used for real time battery parameters identification and SOC estimation. Second, the capacity is estimated in a large time scale based on the combination of coulomb counting method and the SOC estimation method. Then, on the premise of knowing the internal parameters and states of the battery, the instantaneous peak charge/discharge power and continuous charge/discharge power are estimated under a variety of constrains. Finally, the urban dynamometer driving schedule (UDDS) test is carried out for verification. Results show that the proposed method can estimate battery states in high accuracy.