Multi-innovation and strong tracking based H filter for state of charge estimation of lithium-ion batteries

Accurate state of charge (SOC) is a crucial indicator of the battery management system, which ensures the safety performance of lithium-ion batteries (LiBs). However, traditional SOC estimation methods such as extended Kalman filter and H infinity filter (HIF) do not perform satisfactorily when dealing with sudden state changes, which pose a safety hazard to the normal operation of LiBs. This paper proposes two effective strategies to endow the HIF with the strong tracking capability to react to state mutations. Initially, the single innovation in the HIF is extended to the multi-innovation so that the estimation accuracy is ameliorated by incorporating the historical data. Additionally, a suboptimal fading factor is introduced into the HIF to adjust the gain matrix online by forcing the innovation sequence to keep orthogonal. The fading factor fully utilizes the rich information in the innovation sequence, enabling the filter to track changing states well. Finally, the discharge experiments and dynamic stress tests are conducted to verify that the proposed strategy can ameliorate the precision and robustness without bringing extra computational burden.