State of Charge and State of Health Coestimation for Lithium-Ion Capacitor Based on Multi-innovation Filters
Algorithm 1
Recursive process of MI-LKF.
Regression model:
Step I: initialization
For , set ,,, and .
Step II: computation
State prior estimation
Error covariance prior estimation
Kalman gain calculation
Innovation value calculation
Innovation vector and gain vector calculation
State posterior estimation
Error covariance posterior estimation
where represents the identified vector, represents the input vector, is the Kalman gain vector of MILKF, is the covariance matrix, is the process noise covariance matrix, is measurement noise covariance matrix, and is the unit matrix.