SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

Abstract

In recent years, electric vehicles (EVs), hybrid electric vehicles (HEVs), and plug-in electric vehicles (PEVs) have become very popular. Therefore, the use of secondary batteries exponentially increased in EV systems. Battery fuel gauges determine the amount of charge inside the battery, and how much farther the vehicle can drive itself under specific operating conditions. It is very important to provide accurate state-of-charge (SOC) information of the battery module to the driver, since inaccurate fuel gauges will not be tolerated. In this paper, a model-based approach is proposed to estimate the SOCs of multiple lithium-ion (Li-ion) battery cells, connected in a module in series, by using a nonlinear state observer (NSO) and an online parameter identification algorithm. A simple method of estimating the impedance and SOC of each cell in a module is also presented in this paper, by employing a ratio vector with respect to the reference value. A battery model based on an autoregressive model with exogenous input (ARX) was used with recursive least squares (RLS) for parameter identification, in an effort to guarantee reliable estimation results under various operating conditions. The validity and feasibility of the proposed algorithm were verified by an experimental setup of six Li-ion battery cells connected in a module in series. It was found that, when compared with a simple linear state observer (LSO), an NSO can further reduce the SOC error by 1%.

Development of a Comprehensive Model for the Coulombic Efficiency and Capacity Fade of LiFePO4 Batteries under Different Aging Conditions

“…The equivalent circuit model is commonly used in predicting battery function and providing estimation to the battery management system [53,54].…”

“…The equivalent circuit model is commonly used in predicting battery function and providing estimation to the battery management system [53,54]. Figure 4a,b are quite accurate, since they can clearly describe the battery's nonlinear dynamic behavior, which can be used as a good solution for the SoC estimation of energy storage devices.…”

“…The equivalent circuit model can be represented by the following state equations [36,37,53,54], where V ps and V pf are the state variables, I b is the input, and V b the output:…”

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

“…29 A new parameter identification method was proposed by considering electrochemical properties. 35 The degradation state prediction was explored in the whole climate, 36 and the identification was conducted for different ECM parameters along with the global optimization. 32 A practical model was also constructed for the energy and voltage response state prediction including temperature and aging effects.…”

“…33 The parameter estimation and sensitivity analysis were carried out by using the electrochemical models for the LIBs, 34 which was coupled with the thermal model and used to describe the thermal performance during the cycling charge-discharge process. 35 The degradation state prediction was explored in the whole climate, 36 and the identification was conducted for different ECM parameters along with the global optimization. 37 As the complex series-parallel combination is used in the LIB packs to break for the limitations of cell voltage and capacity, there are differences in cell-to-cell consistency and interaction.…”