Battery Equivalent Circuit Model Research Articles

On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation

Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored. These include: battery state of charge (SoC), battery state of health (capacity fade determination, SoH), and state of function (power fade determination, SoF). The second paper concludes the series by presenting a multi-stage online parameter identification technique based on a weighted recursive least quadratic squares parameter estimator to determine the parameters of the proposed battery model from the first paper during operation. A novel mutation based algorithm is developed to determine the nonlinear current dependency of the charge-transfer resistance. The influence of diffusion is determined by an on-line identification technique and verified on several batteries at different operation conditions. This method guarantees a short response time and, together with its fully recursive structure, assures a long-term stable monitoring of the battery parameters. The relative dynamic voltage prediction error of the algorithm is reduced to 2%. The changes of parameters are used to determine the states of the battery. The algorithm is real-time capable and can be implemented on embedded systems.

On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models: Part 1. Requirements, critical review of methods and modeling

Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored, these include: battery state of charge (SoC), battery state of health (capcity fade determination, SoH), and state of function (power fade determination, SoF).In a series of two papers, we propose a system of algorithms based on a weighted recursive least quadratic squares parameter estimator, that is able to determine the battery impedance and diffusion parameters for accurate state estimation. The functionality was proven on different battery chemistries with different aging conditions.The first paper investigates the general requirements on BMS for HEV/EV applications. In parallel, the commonly used methods for battery monitoring are reviewed to elaborate their strength and weaknesses in terms of the identified requirements for on-line applications. Special emphasis will be placed on real-time capability and memory optimized code for cost-sensitive industrial or automotive applications in which low-cost microcontrollers must be used. Therefore, a battery model is presented which includes the influence of the Butler–Volmer kinetics on the charge-transfer process. Lastly, the mass transport process inside the battery is modeled in a novel state-space representation.

Adaptive gain sliding mode observer for state of charge estimation based on combined battery equivalent circuit model

An adaptive gain sliding mode observer (AGSMO) for battery state of charge (SOC) estimation based on a combined battery equivalent circuit model (CBECM) is presented. The error convergence of the AGSMO for the SOC estimation is proved by Lyapunov stability theory. Comparing with conventional sliding mode observers for the SOC estimation, the AGSMO can minimise chattering levels and improve the accuracy by adaptively adjusting switching gains to compensate modelling errors. To design the AGSMO for the SOC estimation, the state equations of the CBECM are derived to capture dynamics of a battery. A lithium-polymer battery (LiPB) is used to conduct experiments for extracting parameters of the CBECM and verifying the effectiveness of the proposed AGSMO for the SOC estimation.

A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles

In this paper, a novel approach for battery state of charge (SOC) estimation in electric vehicles (EVs) based on an adaptive switching gain sliding mode observer (ASGSMO) has been presented. To design the ASGSMO for the SOC estimation, the state equations based on a battery equivalent circuit model (BECM) are derived to represent dynamic behaviours of a battery. Comparing with a conventional sliding mode observer, the ASGSMO has a capability of minimising chattering levels in the SOC estimation by using the self-adjusted switching gain while maintaining the characteristics of being able to compensate modelling errors caused by the parameter variations of the BECM. Lyapunov stability theory is adopted to prove the error convergence of the ASGSMO for the SOC estimation. The lithium-polymer battery (LiPB) is utilised to conduct experiments for determining the parameters of the BECM and verifying the effectiveness of the proposed ASGSMO in various discharge current profiles including EV driving conditions in both city and suburban.

Application-specific parameterization of reduced order equivalent circuit battery models for improved accuracy at dynamic load

Reduced order equivalent circuit battery models are widely used for modeling batteries as a part of complex system or as a basis for battery monitoring algorithms. These models are often parameterized in frequency domain using impedance spectroscopy or in time domain by applying current profile and measuring respective voltage response. This paper shows how the frequency characteristic of a typical battery load in a given application can be considered during parameterization to improve the accuracy of the model when it is used in this application with dynamic load. The method uses impedance-based parameterization technique by adopting additional weighting coefficients to the complex nonlinear least squares (CNLS) fitting procedure without introducing any restrictions on its applicability. As an example, modeling of lithium-ion battery in electric vehicles is considered. The proposed techniques reduce the inaccuracy of modeled dynamic battery voltage response by 40% in the considered example.

Modeling and Parameter Estimation of Lithium Iron Phosphate Power Battery

Reasonable modeling and simulation of power battery, optimization of the estimated power battery parameters that can contribute to power balance control efficiently, prolong the service life of the battery power and reduce the cost of electric vehicles. Using the lithium iron phosphate (LiFePO4) power battery that adopted in current electric vehicles widely as the research object, choose PNGV equivalent circuit battery model based on the analysis of the static and dynamic characteristics of the battery. An improved least square method to identify the model parameters was proposed, the matlab/simulink simulation results show the estimation error of the proposed method is less than 3%, and validate the proposed method is effective.

SOC Estimation of Power Battery in Electric Vehicle Based on MC9S12DG128

This paper explored how to make use of extended Kalman filter (EKF) technique to estimate the state of charge (SOC) of battery. Based on the equivalent circuit model of battery, a mathematical model of EKF was established, and an EKF algorithm of SOC estimation was presented. By single chip microcomputer (MCU) of MC9S12DG128, a hardware platform for experimenting with this algorithm was built up. Then this algorithm was verified by two kinds of discharge tests on the hardware in the loop platform. The test results show that this algorithm is able to satisfy the requirement of SOC estimation. It has a high accuracy, and a low error accumulation.

On-line estimation of lithium-ion battery impedance parameters using a novel varied-parameters approach

In this paper a novel technique for on-line estimation of impedance parameters of a battery equivalent circuit model (ECM) is presented. The ECM is often used as a basis of battery management algorithms to calculate the state of charge or the maximal available power. Especially the latter requires that the parameters of the ECM describe precisely the battery impedance. Unfortunately, the battery impedance changes over the battery lifetime due to aging. Therefore, the parameters of the ECM must be updated continuously. In this paper such an update is achieved using a novel approach that in addition allows considering current dependency of the battery impedance. The basic idea is that the change of the battery voltage under load is predicted using the ECM with different parameter sets during a short time period. The predicted voltage is then compared to the sensor data and the best parameter set is selected. It is then used as a basis in the next adaption step. A necessary strategy for building parameter sets is implemented for a fast and efficient convergence which results in an accurate parameter estimation. The algorithm runs on a standard 16 bit 80 MHz microcontroller at only 16% processor load.

Comparison, Selection, and Parameterization of Electrical Battery Models for Automotive Applications

This paper describes the comparison and parameterization process of dynamic battery models for cell and system simulation. Three commonly used equivalent circuit battery models are parameterized using a numeric optimization method and basic electrical tests with a lithium-ion polymer battery cell. The maximum model performance is investigated, and the parameterized models are compared regarding the parameterization effort and the model accuracy. For the model with the best tradeoff between the parametrization effort and the model accuracy, a reasonable simplification of the parameterization process is presented. This model is parameterized with the simplified parameterization process and, finally, validated by using a current profile obtained from an electric vehicle simulation performing a real-life driving cycle.

The Method Research of Parameter Identification of Li-Ion Battery Base on Least Square Method

This paper described the theory of parameter identification of Li-ion battery base on least square method. The result of voltage response by HPPC test is discussed. The model parameter is identified through least square method. The battery model is built with Matlab/Simulink and the identified parameter is verified. The result indicated that the least square method is suitable for parameter identification of Li-ion battery equivalent circuit model.

Thermal analysis of a Li-ion battery module under realistic EV operating conditions

SUMMARY The thermal behavior of a Li-ion battery module that belongs to the battery system of an actual electric vehicle prototype was numerically investigated. Realistic driving loads and passive cooling conditions were considered. A combination of a vehicle dynamics model, an equivalent electric circuit battery model, and a 3D finite-element thermal model was used in the analysis. Temperature and electric potential measurements, performed at the cell and module levels, were first used for model calibration. Electric currents, associated with the ARTEMIS driving cycles, were then calculated and applied in the battery model to predict the heat sources for the thermal model. It was found that the temperature increase corresponding to urban transportation requirements in European countries is tolerable. Nevertheless, road and highway applications would result in a temperature increase that accelerates cell ageing, and an active cooling strategy is required. Copyright © 2012 John Wiley & Sons, Ltd.

H∞ Observer-Based Battery Fault Estimation for HEV Application

This paper presents an observer-based fault estimation method for the Li-ion battery used for hybrid electric vehicles (HEVs) propulsions, where battery ageing is treated as a fault. First, the nominal equivalent electric circuit (EEC) battery model and the extended one combining the effects of temperature and fault variations are given. Then the principle of fault estimation is stated, the fault observer with the general H∞ configuration is depicted, some weighting functions for performance description are given. Finally, the fault observer is derived and simulation results both in frequency domain and time domain are given. The simulation results illustrate the availability of the proposed observer which owns excellent robustness performance with respect to disturbances, e.g., electrical current and temperature variations.

Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach

To improve the use of lithium-ion batteries in electric vehicle (EV) applications, evaluations and comparisons of different equivalent circuit models are presented in this paper. Based on an analysis of the traditional lithium-ion battery equivalent circuit models such as the Rint, RC, Thevenin and PNGV models, an improved Thevenin model, named dual polarization (DP) model, is put forward by adding an extra RC to simulate the electrochemical polarization and concentration polarization separately. The model parameters are identified with a genetic algorithm, which is used to find the optimal time constant of the model, and the experimental data from a Hybrid Pulse Power Characterization (HPPC) test on a LiMn2O4 battery module. Evaluations on the five models are carried out from the point of view of the dynamic performance and the state of charge (SoC) estimation. The dynamic performances of the five models are obtained by conducting the Dynamic Stress Test (DST) and the accuracy of SoC estimation with the Robust Extended Kalman Filter (REKF) approach is determined by performing a Federal Urban Driving Schedules (FUDS) experiment. By comparison, the DP model has the best dynamic performance and provides the most accurate SoC estimation. Finally, sensitivity of the different SoC initial values is investigated based on the accuracy of SoC estimation with the REKF approach based on the DP model. It is clear that the errors resulting from the SoC initial value are significantly reduced and the true SoC is convergent within an acceptable error.

Research on State of Charge Estimation of Lithium Iron Phosphate Battery Used on Electric Vehicle

Lithium iron phosphate battery (LiFeO4 battery) is a very promising power battery. The research on LiFeO4 battery and the realization of its battery management system are very significant for the development of Electric Vehicle. Based on a large number of test data, the equivalent circuit model of battery is established, the parameters of the battery model are obtained using least-square method, and then the improved SOC estimation algorithm is studied in simulation conditions. All of the battery management algorithms are achieved in simulation model. The C code is generated from simulation model and applied in battery management system. Finally, the battery management algorithms are verified by simulation and hardware-in-loop test and bench test.

Impedance-based and circuit-parameter-based battery models for HEV power systems

The relationship between voltage and current inside a battery, or the impedance, plays an important role in the simulation and design of hybrid electric vehicle (HEV) power systems. This paper proposes a new approach employing the Bode plot for evaluation of equivalent circuit parameters for a lithium polymer battery (LiPB) for HEV application. The main concept of the proposed circuit-parameter-based model approach is the application of a transfer function used to process the frequency response of the battery for calculation of accurate circuit parameters. Additionally, the Bode plot is also applied to derive the impedance-based model directly from frequency response measurements for short time simulations and practical use in the HEV. Two methods for battery modeling are proposed and verified experimentally with the voltage-current profile of a conventional HEV using the battery measured in this paper. The results show that the proposed circuit-parameter-based technique provides a satisfactory battery equivalent circuit model.