Abstract
State-of-charge estimation and on-line model modification of lithium-ion batteries are more urgently required because of the great impact of the model accuracy on the algorithm performance. This study aims to propose an improved DUKF based on the state-parameter separation. Its characteristics include: (1) State-Of-Charge (SoC) is treated as the only state variable to eliminate the strong correlation between state and parameters. (2) Two filters are ranked to run the parameter modification only when the state estimation has converged. First, the double polarization (DP) model of battery is established, and the parameters of the model are identified at both the pulse discharge and long discharge recovery under Hybrid Pulse Power Characterization (HPPC) test. Second, the implementation of the proposed algorithm is described. Third, combined with the identification results, the study elaborates that it is unreliable to use the predicted voltage error of closed-loop algorithm as the criterion to measure the accuracy of the model, while the output voltage obtained by the open-loop model with dynamic parameters can reflect the real situation. Finally, comparative experiments are designed under HPPC and DST conditions. Results show that the proposed state-parameter separated IAUKF-UKF has higher SoC estimation accuracy and better stability than traditional DUKF.
Highlights
In recent years, the automobile industry has gradually developed, and the contradiction between traditional fuel vehicles and environmental carrying capacity has intensified
The results show that the uL prediction error of state-parameter separated Adaptive UKF (AUKF)-unscented Kalman filter (UKF) is larger than that of traditional
An improved AUKF-UKF based on state-parameters separation is proposed
Summary
The automobile industry has gradually developed, and the contradiction between traditional fuel vehicles and environmental carrying capacity has intensified. The demand of replacing traditional fuel vehicles with new energy ones is becoming more and more urgent. Lithium-ion batteries are widely used in pure electric vehicles as core power sources due to their high energy density and long cycle life. State-Of-Charge (SoC) is defined as the percentage of the current residual capacity of batteries. As an artificially defined variable, SoC can only be calculated by voltage, current and temperature of batteries [1]. The methods for SoC estimation in recent years can be divided into those based on characterization parameters and definitions, data-driven and battery modeling theory [2].
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