Abstract
State of health (SOH) measures the remaining service life of lithium-ion batteries, and it is defined as the percentage of lithium-ion batteries’ current maximum available energy and rate capacity, and the age characterization factors such as internal resistance, capacity, and voltage are analyzed as a function of the aging process. For the state co-estimation problem of lithium-ion batteries, considering the SOH and state of charge (SOC) interact with each other and vary significantly in time intervals. An improved adaptive unscented Kalman-unscented particle filter algorithm based on different update frequencies is proposed, the algorithm performs collaborative online estimation through building state estimation model and adaptive iterative algorithm for nonlinear systems of SOC and SOH of lithium-ion batteries.
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