An equivalent circuit model analysis for the lithium-ion battery pack in pure electric vehicles

Su, Jie; Lin, Maosong; Wang, Shunli; Li, Jin; Coffie-Ken, James; Xie, Fei

doi:10.1177/0020294019827338

Cited by 43 publications

(23 citation statements)

References 31 publications

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“…The battery equivalent circuit model is composed of networks of electrical components, such as the voltage sources, capacitors and resistors, which can simulate the electrical performance of a battery. 35 Considering the computing complexity and estimation accuracy of battery states, the Randles equivalent circuit model in Figure 5 is used for the lead–acid battery in this study. 27 In the circuit model, R t is the polarization resistance in the battery.…”

Section: Identification Of Circuit Model Parametersmentioning

confidence: 99%

Augmented system model-based online collaborative determination of lead–acid battery states for energy management of vehicles

Wang

Huang

Pan

et al. 2021

Measurement and Control

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State of charge (SOC) and state of health (SOH) of batteries are the indispensable control decision variables for online energy management system (EMS) in modern internal combustion engine vehicles. The real-time and accurate determination of SOC and SOH is essential to the reliability and safety of EMS operation. Obtaining good accuracy for the SOC estimation is difficult without considering SOH because of their coupling relationship. Although several works on the joint estimation of SOC and SOH of lithium–ion batteries are available, these studies cannot be applied to lead–acid batteries because of the differences in physical structure and characteristics. This study handles the problem of modeling the relationship between SOC and SOH of lead–acid battery and their online collaborative estimation. First, the structure and control strategy of a bus-based EMS is discussed, and the improper energy control actions of EMS due to the inaccurate SOC estimation are analyzed. Second, an instantaneous correlation factor β for SOC and SOH is defined as a new state estimating variable, and the simplified linear relationship model between β and open circuit voltage is established through the battery experiments. Third, a discretized augmented system equation of β is deduced according to the relationship model and the Randles circuit model. The least square circuit parameter identification (LSCPI) algorithm is presented to identify the time-varying circuit model parameters, while the adaptive Kalman filter for augmented system (AKFAS) algorithm is employed to estimate β online. A collaborative estimation algorithm is proposed on the basis of the LSCPI and AKFAS to determine SOC and SOH of lead–acid battery in real time, and a demo intelligent battery sensor is developed for its implementation. The results of battery charging and discharging experiments indicate that the proposed method has high accuracy. The estimation accuracy of SOC of this method reaches 3.13%, which is 7% higher than that of the existing method.

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Section: Identification Of Circuit Model Parametersmentioning

confidence: 99%

Augmented system model-based online collaborative determination of lead–acid battery states for energy management of vehicles

Wang

Huang

Pan

et al. 2021

Measurement and Control

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“…Generally, two models characterize an EV, which is the hybrid, and the pure EV, 28–30 as shown in Figure 2. The only difference between the two models is the internal combustion engine (ICE), which is used as a main power motor in the hybrid model.…”

Section: Electric and Hybrid Vehicle Architecturesmentioning

confidence: 99%

The efficiency of PSO-based MPPT technique of an electric vehicle within the city

Kariem

Touti

Fetouh

2020

Measurement and Control

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Electrical vehicle fed by photovoltaic energy represents a complex system, which needs a high-performance control algorithm. Regarding the real situations, mostly the electric vehicle will be moving inside the city. If this system is covered by photovoltaic cells, the efficiency of this renewable energy source will depend on various factors. The shade areas or sunlight zones which exist in the city make the solar system unstable. Resolving this problem can increase the battery autonomy and allow addition of some running kilometers to the vehicle. Based on this objective, this study deals with the problem of solar variation and its influence on vehicle efficiency within the city. The problem is how to extract the maximum energy in this case. In order to maximize the global energy performance and increase vehicle autonomy, the optimal control method will be applied to this photovoltaic system taking into account some performance indicators such as the obtained power, the tracking speed, and the chattering level. Therefore, this study explores two control techniques in order to extract the maximum power from the solar energy system, which are the incremental method and the particle swarm optimization method. Simulink/MATLAB tool is used for simulation and comparison study based on the offered performance indicators. The obtained results show that the particle swarm optimization method has high global performance and an energy gain is obtained.

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“…The estimation of SOC generally requires modeling of the battery cell and packs, the structure of the battery model, and parameter identification should be based on characteristics of the chargedischarge test under different temperatures, which need sufficient test data of batteries. Equivalent circuit models commonly used for lithium-ion batteries are the Thevenin and RC models [39,40].…”

Section: Introductionmentioning

confidence: 99%

A State of Charge Estimation of The Lithium-Ion Batteries Based on Reduced-Order Unscented Kalman Filtering Method

2021

JETP

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State of Charge (SOC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of lithium-ion batteries. SOC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and distinct nonlinear behavior. This paper compares two of the basic methods and algorithms for SOC estimation of lithium-ion batteries (LIBs) focusing on the description of the two techniques in a test experiment and the elaboration of their differences for use in battery management systems (BMS) applications. A Reduced-order unscented Kalman filter method is used for estimation and tracking to realizer real-time high-precision estimation of lithium-ion battery state of charge. Experimental tests are carried out with a lithium-ion battery cell for model and state estimation validations. Many researchers have proposed different methods of estimating SOC that raised the challenge of establishing a relationship between the accuracy and robustness of the method. The experimental results of the OCV-SOC estimation method, Hybrid Pulse Power Characterization (HPPC) test, and Beijing Bus Dynamic Stress Test (BBDST) working condition method are analyzed. The error of SOC estimation based on the established Thevenin RC modeling using the Reduced-order-unscented Kalman filter is less than 0.3%. The result from the use of the reduced-order unscented Kalman filtering algorithm proves that it has high accuracy in the state of charge estimation of the lithium-ion batteries.

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An equivalent circuit model analysis for the lithium-ion battery pack in pure electric vehicles

Cited by 43 publications

References 31 publications

Augmented system model-based online collaborative determination of lead–acid battery states for energy management of vehicles

Augmented system model-based online collaborative determination of lead–acid battery states for energy management of vehicles

The efficiency of PSO-based MPPT technique of an electric vehicle within the city

A State of Charge Estimation of The Lithium-Ion Batteries Based on Reduced-Order Unscented Kalman Filtering Method

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