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Indexed by:Journal Papers

Date of Publication:2019-11-01

Journal:INTERNATIONAL JOURNAL OF ENERGY RESEARCH

Included Journals:EI、SCIE

Volume:43

Issue:14

Page Number:7956-7969

ISSN No.:0363-907X

Key Words:electric vehicle; health indicator; Kalman filter; Li-ion battery; state space representation; state-of-health

Abstract:Lithium-ion battery state-of-health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model-based and data-driven estimator is developed to achieve accurate and reliable state-of-health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state-space representation is constructed based on the data-driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method.