PNGV Equivalent Circuit Model and SOC Estimation Algorithm for Lithium Battery Pack Adopted in AGV Vehicle

Liu, Xiangyong; Li, Wanli; Zhou, Aiguo

doi:10.1109/access.2018.2812421

Cited by 74 publications

(33 citation statements)

References 36 publications

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“…PNGV Model. The PNGV model was proposed for the 2001 US New Generation Vehicle Cooperation Program to model power batteries in the automotive field [23].…”

Section: Power Battery Equivalent Model and Modelmentioning

confidence: 99%

“…(3) We analyze the accuracy, complexity, dynamic characteristics, and other factors of many power battery models, such as the Rint model, the Thevenin model, the Partnership for a New Generation of Vehicles (PNGV) model [23,25], the General nonlinear (GNL) model, and so on. Then we choose the PNGV model, which is more able to characterize the internal changes in the battery charging and discharging process and has practical physical significance.…”

Section: Introductionmentioning

confidence: 99%

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Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification

Liu

2019

Mathematical Problems in Engineering

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In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper.

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“…PNGV Model. The PNGV model was proposed for the 2001 US New Generation Vehicle Cooperation Program to model power batteries in the automotive field [23].…”

Section: Power Battery Equivalent Model and Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification

Liu

2019

Mathematical Problems in Engineering

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“…Thus, the lithium-ion battery model can usually be established by referring to that of the lead-acid batteries. At present, the battery equivalent circuit model mainly includes R int model [7], PNGV (Partnership for a New Generation of Vehicles) model [8], Thevenin model [9], and n-order RC equivalent circuit model [10]. The R int model is an internal resistance model consisting of a DC source and an internal resistance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

Sun

Ren

et al. 2019

Energies

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With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured terminal voltage, it is shown that the proposed AFFRLS algorithm is superior to the existing forgetting factor recursive least square (FFRLS) and variable forgetting factor recursive least square (VFFRLS) algorithms in accuracy and rapidity, which proves the feasibility and correctness of the proposed parameter identification algorithm.

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“…18,19 The ECM belongs to the semimechanism semiempirical model, in which the mathematical circuit expression is used to simulate the battery behavior by using the circuit elements such as voltage source, resistor, capacitor, and inductors. 23 The LIBs often exhibit some resistance-capacitance (RC) characteristics during the charge-discharge process 24 and further consider the hysteresis of open-circuit voltage (OCV) after the charge-discharge time period. 21,22 The electrical modeling method is used to simulate the working process regardless of its internal chemical reaction and therefore has good applicability.…”

mentioning

confidence: 99%

“…21,22 The electrical modeling method is used to simulate the working process regardless of its internal chemical reaction and therefore has good applicability. 23 The LIBs often exhibit some resistance-capacitance (RC) characteristics during the charge-discharge process 24 and further consider the hysteresis of open-circuit voltage (OCV) after the charge-discharge time period. 25 The problem with this equivalent modeling method is that a simple model cannot reflect the dynamic changes of the battery and may lead to incorrect recognition results.…”

mentioning

confidence: 99%

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

Cao

Wang

Fernandez

et al. 2019

Energy Science & Engineering

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The state of charge (SoC) estimation is the safety management basis of the packing lithium‐ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state‐space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.

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PNGV Equivalent Circuit Model and SOC Estimation Algorithm for Lithium Battery Pack Adopted in AGV Vehicle

Cited by 74 publications

References 36 publications

Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification

Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification

Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

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

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