Abstract
Accurate state of charge (SoC) estimation of batteries plays an important role in promoting the commercialization of electric vehicles. The main work to be done in accurately determining battery SoC can be summarized in three parts. (1) In view of the model-based SoC estimation flow diagram, the n-order resistance-capacitance (RC) battery model is proposed and expected to accurately simulate the battery’s major time-variable, nonlinear characteristics. Then, the mathematical equations for model parameter identification and SoC estimation of this model are constructed. (2) The Akaike information criterion is used to determine an optimal tradeoff between battery model complexity and prediction precision for the n-order RC battery model. Results from a comparative analysis show that the first-order RC battery model is thought to be the best based on the Akaike information criterion (AIC) values. (3) The real-time joint estimator for the model parameter and SoC is constructed, and the application based on two battery types indicates that the proposed SoC estimator is a closed-loop identification system where the model parameter identification and SoC estimation are corrected mutually, adaptively and simultaneously according to the observer values. The maximum SoC estimation error is less than 1% for both battery types, even against the inaccurate initial SoC.
Highlights
The battery is a bottleneck technology for electric vehicles (EVs)
Reference [13] takes the multi-swarm particle swarm optimization (MPSO) method to select the optimal model out of the twelve equivalent circuit models for the LiNMC cell and LiFePO4 cell, and the results indicate that the first-order RC model is preferred for LiNMC cells, while the first-order RC model with one-state hysteresis seems to be the best choice for LiFePO4 cells
To select a reasonable n, the Akaike information criterion (AIC) criterion is applied to establish the optimal tradeoff between the model complexity and prediction precision, and the results show that the first-order RC model is the best
Summary
The battery is a bottleneck technology for electric vehicles (EVs). It is valuable both in theory and practical application to carry out research on the state estimation of batteries, which is very crucial to optimize the energy management, extend the cycling life, reduce the cost and safeguard the application of batteries in EVs. Reference [14] takes six battery models into consideration and applies the least square method and extended Kalman filter (EKF) to identify the LiPB cell model parameters and estimate its voltage; the results indicate that the battery model accuracy could improve greatly with additional hysteresis and filter states at some cost in complexity, while it makes no sense to increase the filter state order beyond 4. Applies the moving window least square (LS) method to realize real-time model parameter identification, and a state observer is built to estimate the battery SoC simultaneously. Proposes a method combining the coulombic counting and the OCV-based method, where two different real-time model-based SoC estimation methods for Lithium-ion batteries are presented, one based on model parameter identification using the weighted RLS method and another based on state estimation using the EKF method. References [21,22] indicate that CDKF, as one sigma-points Kalman filter (SPKF) method, is able to avoid the linearization error of the battery model and improve the model’s precision for SoC estimation, which has the potential to solve the nonlinear estimation problems [23,24,25,26,27]
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