Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions. (September 2019)

• Record Type: Journal Article
• Authors: Liu, Datong; Li, Lyu; Song, Yuchen; Wu, Lifeng; Peng, Yu
• Is Part Of: International journal of electrical power & energy systems. Volume 110(2019)
• Page Start: 48
• Page End: 61
• Publication Date: 2019-09
• Subjects: Lithium-ion battery -- Dynamic conditions -- State of charge estimation -- Deep belief network -- Hybrid method; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2019.02.046
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Lithium-ion battery SOC estimation under dynamic conditions is focused. Deep belief network is fused with the Kalman filter algorithm to accurately estimate SOC. The method has good estimation accuracy under randomly working conditions. Abstract: Lithium-ion battery is widely used in various industrial applications including electric vehicles (EVs) and distributed grids due to its high energy density and long service life. As an essential performance indicator, state of charge (SOC) reflects the residual capacity of a battery. To ensure the safe operation of systems, it is vital to obtain battery SOC accurately. However, as a parameter which cannot be directly measured, the battery SOC are influenced not only by the measurement noise but also the cell temperature. Focusing on these challenging issues, this paper proposes a hybrid model to estimate the lithium-ion battery SOC under dynamic conditions. This method consists of deep belief network (DBN) and the Kalman filter (KF). The battery electric current, terminal voltage and temperature are used as the input of the proposed model of which output is the SOC. With the powerful nonlinear fitting capability of the DBN, the model can extract relationship between the measurable parameters and battery SOC. The KF algorithm is utilized to eliminate the effects from measurement noise and improve the estimation accuracy. Experiments under different operation conditions are carried out with commercial lithium-ion batteries. The biggest average estimation error is less than 2.2% which indicates that the proposed method is promising for battery SOC estimation especially for the complex operation conditions.