Hybrid optimization strategy for lithium-ion battery's State of Charge/Health using joint of dual Kalman filter and Modified Sine-cosine Algorithm

In order to improve the accuracy of State of Charge / Health (SOC/SOH) estimation, Modified-Sine-cosine Algorithm-based dual Extend Kalman filter (MSCA-DEKF) is proposed. Second-order RC model is applied, the model parameters are obtained by Pulse discharge test and Open circuit voltage test (OCV). DEKF is divided into state filter and parameter filter. Nonlinear decrement of transformation parameter r(1) in Sine-cosine Algorithm (SCA) is proposed. Modified SCA (MSCA) is applied to optimizing the covariance noise matrix in the state filter. Parameter filter is applied to estimating the Ohm internal resistance and capacity online, Meanwhile, the time scale of parameters' online update is adjusted to 60 time steps for reducing computing costs. SOH is also obtained by Ohm internal resistance and capacity. Simulation results show that the proposed method improves the accuracy of SOC estimation, and the initial errors of SOC and Ro can be corrected in the first parameter estimation. Ro-based SOH has a better robustness and precision than capacity-based SOH.

Automated summary

A Modified-Sine-cosine Algorithm-based dual Extend Kalman filter (MSCA-DEKF) is proposed to improve the accuracy of State of Charge / Health (SOC/SOH) estimation, which involves optimizing the covariance noise matrix and adjusting the online update time scale of parameters for reducing computing costs. The method also includes estimating the Ohm internal resistance and capacity online.