Vehicle energy system active defense: A health assessment of lithium-ion batteries

With the wide application of lithium-ion battery in various fields, the security and reliability of lithium-ion battery have attracted great attention. Under the mode of continuous development of Internet of vehicles technology, vehicles will be connected with each other in the future, and the hackers will attack the energy system of the vehicle. However, health assessment of lithium-ion battery can timely grasp the running state and health of the power battery system, so as to realize active defense against hacker security attacks. This paper proposes a health assessment method for lithium-ion batteries using incremental capacity analysis and weighted Kalman filter algorithm. In view of the problem that ordinary Kalman filtering algorithm produces poor filtering results when the actual measurement noise error is large, this paper proposes a weighted Kalman filtering algorithm based on ordinary Kalman filtering. Incremental capacity analysis was performed on the charge and discharge data of lithium-ion batteries, and health characteristics were extracted to construct a Gaussian nonlinear feature association mapping model for the health characteristics of lithium-ion batteries. Combined with the battery SOH double-exponential decay model, the weighted Kalman filter algorithm was used to evaluate the health of lithium-ion batteries. Four lithium-ion battery data sets provided by NASA were used to simulate and verify the health assessment method proposed in this paper. The verification results show that the health assessment method based on weighted Kalman filter proposed in this paper has better assessment accuracy than the common Kalman filter method with an average percentage error of 0.61%. The average percentage error of the assessment results for different types of batteries was less than 0.9%. The health assessment method has high accuracy and is suitable for different types of batteries.