A Novel Adaptive Particle Swarm Optimization Algorithm Based High Precision Parameter Identification and State Estimation of Lithium-Ion Battery

He, Mingfang

doi:10.20964/2021.05.55

Cited by 18 publications

(5 citation statements)

References 39 publications

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“…The parameter identification method proposed in this study is competitive with methods in other studies. For example, He et al (32) used PSO incorporating an adaptive optimization strategy for parameter identification of LIBs, and MAPE of the identification results was 1.5%. The recursive LS method used by Yang et al (33) was applied to the experimental data in this study, and the RMSE value was 0.0473.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Equivalent Circuit Modeling and Parameter Identification for Lithium-ion Batteries Based on Improved Barnacle Mating Optimizer

Li¹,

Lin²,

Li³

et al. 2022

Sensors and Materials

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To address the current problems of accuracy and reliability of the equivalent circuit models for lithium-ion batteries and sensors, in this study we propose an improved barnacle mating optimizer (IBMO) to accurately identify parameters. First, an improved second-order RC model (ISO RC) is derived by segmenting the battery hysteresis voltage and introducing it into the second-order RC model (SO RC model) as a controlled source, which better reflects the dynamic effects of the battery than the SO RC model. Second, the tent chaos mapping, cosine control factor, and Levy flight strategy are introduced to enhance the convergence performance of the IBMO. Its convergence performance is verified by comparison with other existing intelligent algorithms using five single-peak and four multipeak test functions. Finally, the IBMO-ISO RC model is established by using the IBMO as the identification strategy of the ISO RC model in the simulation of two real operating conditions of lithium-ion batteries. The root mean square error of the model under the two operating conditions is only 0.0431 and 0.0483, and the mean absolute percentage error is only 0.38 and 0.56%, outperforming other existing models. The IBMO proposed in this study enables higher model recognition accuracy, making it highly convenient for lithium-ion battery and sensor-related research.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Equivalent Circuit Modeling and Parameter Identification for Lithium-ion Batteries Based on Improved Barnacle Mating Optimizer

Li¹,

Lin²,

Li³

et al. 2022

Sensors and Materials

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“…Adaptive PSO [269], exponential decay PSO (EDPSO) [270], PSO with chaos theory [271], hybrid PSO and gravitational search algorithm (PSO-CGSA) [243] Power System controllers Hybrid PSO-pattern search [244], PSO with Pade approximation [245] Capacitor Placement Constriction factor PSO [272], hybrid PSO [247] Generation expansion problem Comprehensive learning PSO [273], fuzzy adaptive chaotic binary PSO [274] Power system reliability and security…”

Section: State Estimationmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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“…With the identified results of θk, the parameters can be further calculated according to Equation (5). The calculation equation is shown in Equation (20).…”

Section: ()mentioning

confidence: 99%

“…Among them, the choice of batteries is extremely important, and lithium-ion batteries are the most widely used and promising batteries because of their long cycle life, good safety performance and high energy density [ [2], [3], [4]]. State of Charge (SOC) estimation is one of the most important issues for Lithium-ion battery, and SOC represents the predicted value of the remaining energy of the battery [5],…”

Section: Introductionmentioning

confidence: 99%

Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter

Xiao

Wang

et al. 2022

J. Electrochem. Soc.

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State of Charge (SOC) estimation is the focus of battery management systems, and it is critical to accurately estimate battery SOC in complex operating environments. To weaken the impact of unreasonable forgetting factor values on parameter estimation accuracy, an artificial fish swarm (AFS) strategy is introduced to optimize the forgetting factor of forgetting factor least squares (FFRLS) and to model the lithium-ion battery using a first-order RC model. A new method AFS-FFRLS is proposed for online parameter identification of the first-order RC model. In SOC estimation, it is not reasonable to fix the process noise covariance, and the differential evolution (DE) algorithm is combined with the extended Kalman filter (EKF) algorithm to achieve dynamic adjustment of the process noise covariance. A joint algorithm named AFS-FFRLS-DEEKF is proposed to estimate the SOC. to verify the reasonableness of the proposed algorithm, experiments are conducted under HPPC, BBDST and DST conditions, and the average errors of the joint algorithm under the three conditions are 1.9%, 2.7% and 2.4%, respectively. The validation results show that the joint algorithm improves the accuracy of SOC estimation.

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A Novel Adaptive Particle Swarm Optimization Algorithm Based High Precision Parameter Identification and State Estimation of Lithium-Ion Battery

Cited by 18 publications

References 39 publications

Equivalent Circuit Modeling and Parameter Identification for Lithium-ion Batteries Based on Improved Barnacle Mating Optimizer

Equivalent Circuit Modeling and Parameter Identification for Lithium-ion Batteries Based on Improved Barnacle Mating Optimizer

Particle Swarm Optimization: A Comprehensive Survey

Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter

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