An Improved PSO Algorithm for Battery Parameters Identification Optimization Based on Thevenin Battery Model

Peng, Wei; Yang, Zhengqiu; Liu, Chen; Xiu, Jiapeng; Zhang, Zheng

doi:10.1109/ccis.2018.8691341

Cited by 16 publications

(6 citation statements)

References 9 publications

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“…Therefore, several White Box-based heuristic algorithms, including GA [22], SA [8] and ACO [31] are compared and discussed. An improved PSO [24] that combines the advantages of both PSO and ACO is also compared and discussed, the detailed algorithm refers to Appendix B. The experimental results in static environment are shown in Table VII.…”

Section: ) Choquet Integral-based Interdependency and Significance Analysis Algorithmmentioning

confidence: 99%

DRESIA: Deep Reinforcement Learning-Enabled Gray Box Approach for Large-Scale Dynamic Cyber-Twin System Simulation

Lin

Yang

et al. 2021

IEEE Open J. Comput. Soc.

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The massive data generated by large-scale dynamic systems makes its optimization facing a tough challenge. Traditional White Box-based methods directly model the internal operating mechanism of the system, so massive amounts of measured data need to be handled, which is costly and time-consuming. The poor interpretability of the Black Box-based methods makes it difficult to adapt to the dynamic environment. Thus we propose a novel Gray Box-based approach namely Deep Reinforcement Learning-enabled Constraint Set Inversion Algorithm (DRESIA), which establishes a quantitative model of the nonlinear interoperability effects of system internal states which simplifies the White Box's complex mechanism of reconstruction and prediction and retains the interpretability of the model, therefore improves the prediction efficiency of feasible region while also improving the generalization ability. It further improves the dynamic adaptability of the modeling environment, which provides a new performance balancing scheme for system modeling. Under the premise that the large-scale 5G Cyber-Twin system satisfies the given Quality of Service(QoS) requirements, we perform DRESIA to realize the efficient and dynamic optimal search of feasible region, the results show that the DRESIA reduces the computational cost, and balances the accuracy and robustness of the feasible region, which validate the effectiveness and superiority of Gray Box-based approach.

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Section: ) Choquet Integral-based Interdependency and Significance Analysis Algorithmmentioning

confidence: 99%

DRESIA: Deep Reinforcement Learning-Enabled Gray Box Approach for Large-Scale Dynamic Cyber-Twin System Simulation

Lin

Yang

et al. 2021

IEEE Open J. Comput. Soc.

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show abstract

“…When identifying the model's parameters, the theoretical model with the experimental data are usually combined to identify the parameter value [19][20][21][22][23][24][25][26]. As an essential method of parameter identification, the optimization algorithm has a strong search performance for a specific parameter range, and it has high adaptability for practical problems [19,21,23,24].…”

Section: Parameter Identification Based On Optimization Algorithmmentioning

confidence: 99%

“…Second, because the PSO algorithm has few adjustable hyperparameters, the algorithm structure is relatively simple compared to other intelligent algorithms (simulated annealing algorithm, genetic algorithm, immune algorithm, etc.). It is also easy to implement in engineering and has a faster convergence speed [20,22,23]. Third, the algorithm structure of PSO is simple and it has high universality.…”

Section: Parameter Identification Based On Optimization Algorithmmentioning

confidence: 99%

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Synchronous Machine Winding Modeling Method Based on Broadband Characteristics

2021

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The accurate establishment of the equivalent circuit model of the synchronous machine windings’ broadband characteristics is the basis for the study of high-frequency machine problems, such as winding fault diagnosis and electromagnetic interference prediction. Therefore, this paper proposes a modeling method for synchronous machine winding based on broadband characteristics. Firstly, the single-phase high-frequency lumped parameter circuit model of synchronous machine winding is introduced, then the broadband characteristics of the port are analyzed by using the state space model, and then the equivalent circuit parameters are identified by using an optimization algorithm combined with the measured broadband impedance characteristics of port. Finally, experimental verification and comparison experiments are carried out on a 5-kW synchronous machine. The experimental results show that the proposed modeling method identifies the impedance curve of the circuit parameters with a high degree of agreement with the measured impedance curve, which indicates that the modeling method is feasible. In addition, the comparative experimental results show that, compared with the engineering exploratory calculation method, the proposed parameter identification method has stronger adaptability to the measured data and a certain robustness. Compared with the black box model, the parameters of the proposed model have a certain physical meaning, and the agreement with the actual impedance characteristic curve is higher than that of the black box model.

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“…In addition, among all kinds of parameters optimization methods, Particle Swarm Optimization (PSO) algorithm [41]- [43], Genetic Algorithm (GA) [44]- [46], and in recent years, Lu Cheng et al [47], [48] newly proposed methods such as Global/local Linked Driven Optimization Strategy (GLDOS) and Improved Decomposed-Coordinated Kriging Modeling Strategy (IDCKMS) are representative. The above optimization methods can efficiently and accurately optimize and solve specific objective function models; However, for problems that need to first construct an optimized objective function model through experiments, and then optimize its parameters (Such as this paper will optimize the control parameters of sine rotating puncture, which belongs to this kind of optimization problem), the experimental statistical analysis method is generally used for optimization analysis.…”

Section: Parameter Optimization Experimentsmentioning

confidence: 99%

Study on the Control Method and Optimization Experiment of Prostate Soft Tissue Puncture

Zhang

Liu

2020

IEEE Access

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In the treatment of prostate cancer patients, surgery of radioactive seed implantation with the puncture robots is an effective treatment method. However, when the puncture needle enters the lesion tissue, there are complicated forces between the needle body and the soft tissue, which leads to the drift of the prostate and the deflection of the puncture needle, thereby affecting the puncture accuracy. In order to improve the positioning accuracy and ensure the best treatment effect, a sine rotation continuous puncture control method was proposed in this paper. First, by analyzing the interaction between the puncture needle and the soft tissue, the force balance equation and the deflection equation were established, according to the equations, it could be known that puncture friction is the main factor affecting the deflection of the puncture needle. Second, combined with the influence of puncture friction and the research on the theory of rotation puncture, the sine rotation puncture method was compared with the direct puncture method and the existing rotation puncture method; Observed with a 4D Color Doppler Ultrasound to verify the feasibility of the rotating puncture theory and the effectiveness of the sine rotation puncture method. Third, through the establishment of Euler Beam model, rotation angle analysis and puncture velocity test analysis, the influence of dynamic parameters of this method on the puncture accuracy was discussed, the optimization range of each factor was obtained; The experiment was designed by the quadratic regression orthogonal combination theory, the experiment process was observed and recorded by a 3D X-ray scanner, the regression equations were established between experiment factors and experiment indicators with the Design-Expert, and the best parameter combinations were acquired through optimization. Last, comparison tests and verification test were carried out to verify the rationality and reliability of the optimized sine rotation puncture control method.

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An Improved PSO Algorithm for Battery Parameters Identification Optimization Based on Thevenin Battery Model

Cited by 16 publications

References 9 publications

DRESIA: Deep Reinforcement Learning-Enabled Gray Box Approach for Large-Scale Dynamic Cyber-Twin System Simulation

DRESIA: Deep Reinforcement Learning-Enabled Gray Box Approach for Large-Scale Dynamic Cyber-Twin System Simulation

Synchronous Machine Winding Modeling Method Based on Broadband Characteristics

Study on the Control Method and Optimization Experiment of Prostate Soft Tissue Puncture

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