Abstract
Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation.
Highlights
Li-ion battery based energy storage technology has become a key enabler of power grids grid and electric transportation sector objectives due to their bene cial properties [1]
The RMSE mentioned in the rest of the paper is the result of 100 tests. e overall RMSE is lower than 1%, which is su cient in most online applications. e oscillation appears at around state of charge (SOC) = 1 is probably because of the error of SOC–open circuit voltage (OCV) curve mentioned in Section 2.2, which can be eliminated with precise SOC–OCV measurement
A hybrid trilaminar ltering based SOC estimation algorithm is proposed with the combination of standard KF, UKF, and SIR particle lter. e coupled Kalman ltering algorithm is designed to obtain the preliminary SOC estimation value based on the typical equivalent circuit models (ECMs)
Summary
Li-ion battery based energy storage technology has become a key enabler of power grids grid and electric transportation sector objectives due to their bene cial properties [1]. As a critical indicator of available energy in a Li-ion cell, SOC cannot be directly measured It can be obtained by various estimation approaches based on coulomb counting, open circuit voltage (OCV), electrochemical impedance spectroscopy (EIS), or battery modelling approaches in combination with machine learning or modern control theory. Model-based methods that use both the measured current and voltage can be used together with online parameter identi cation, such as Kalman lters, which is the most commonly used so far. To reduce the voltage error and provide satisfactory estimation accuracy in the low SOC area, an extended ECM based on the SPM using the knowledge of the surface SOC is proposed in [47] and presents a better tting result than the ECM at SOC lower than 20% by using the genetic algorithm for model parameter identi cation. Comprehensive tests including pulse charging test, Urban Dynamometer Driving Schedule (UDDS) test, and mixed charge and discharge test are conducted to validate the rationality and e ectiveness of the proposed algorithm
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