Cooperative co-evolutionary differential evolution algorithm applied for parameters identification of lithium-ion batteries

Wang, Chuan; Xu, Minyi; Zhang, Qinjin; Jiang, Ruizheng; Feng, Jinhong; Wei, Yi; Liu, Yancheng

doi:10.1016/j.eswa.2022.117192

Cited by 15 publications

(4 citation statements)

References 60 publications

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“…As shown in (b), the developed cloud-assisted method accurately follows the change of battery terminal voltage in the whole tested cycle, which validates its effectiveness. The performance of the developed method is further compared with offline [42], EKF [43], adaptive EKF (AEKF) [44], and cooperative co-evolutionary differential evolution (CCDE) [45] methods. The bounds of absolute percent error (BAPE) reaches 4.34% in the conventional offline method.…”

Section: Experimental Platform and Resultsmentioning

confidence: 99%

Edge computing for vehicle battery management: Cloud-based online state estimation

Li¹,

He²,

Wei³

et al. 2022

Journal of Energy Storage

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Section: Experimental Platform and Resultsmentioning

confidence: 99%

Edge computing for vehicle battery management: Cloud-based online state estimation

Li¹,

He²,

Wei³

et al. 2022

Journal of Energy Storage

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“…The differential evolution algorithm is one of the population iterative evolution algorithms. It is easy to realize and performs well in finding the best solutions to complex optimization problems [ 28 , 29 ]. There are three main steps of the differential evolution algorithm, including the mutation process, the crossover process, and the selection process [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

Zhu

Raghavan

2023

Plants

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As an agricultural plant, the cantaloupe contains rich nutrition and high moisture content. In this paper, the estimation problem of the moisture ratio model during a cantaloupe microwave drying process was considered. First of all, an image processing-based cantaloupe drying system was designed and the expression of the moisture ratio with regard to the shrinkage was built. Secondly, a maximum likelihood principle-based iterative evolution (MLP-IE) algorithm was put forward to estimate the moisture ratio model. After that, aiming at enhancing the model fitting ability of the MLP-IE algorithm, a maximum likelihood principle-based improved iterative evolution (MLP-I-IE) algorithm was proposed by designing the improved mutation strategy, the improved scaling factor, and the improved crossover rate. Finally, the MLP-IE algorithm and MLP-I-IE algorithm were applied for estimating the moisture ratio model of cantaloupe slices. The results showed that both the MLP-IE algorithm and MLP-I-IE algorithm were effective and that the MLP-I-IE algorithm performed better than the MLP-IE algorithm in model estimation and validation.

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“…Punyakum et al (2022) proposed a hybrid algorithm combining particle swarm and DE to solve multi-access multi-cycle labor scheduling and routing problems. Wang et al (2022) put forward a co-evolutionary DE algorithm to address the problem of identifying lithium-ion battery parameters. Zhou et al (2022) introduced a DE algorithm based on self-adaptive and dynamic reverse learning strategy and applied it to the parameter identification of photovoltaic models; In order to accurately estimate parameters of the fuel cell model, Hachana and El-Fergany (2022) combined the artificial bee colony algorithm with the DE algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…(2022) proposed a hybrid algorithm combining particle swarm and DE to solve multi-access multi-cycle labor scheduling and routing problems. Wang et al . (2022) put forward a co-evolutionary DE algorithm to address the problem of identifying lithium-ion battery parameters.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of Wiener-Hammerstein system based on multi-population self-adaptive differential evolution algorithm

Chu,

Li,

Jiang

et al. 2023

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PurposeThe Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system.Design/methodology/approachThe idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively.FindingsFrom the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system.Originality/valueCompared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system.

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Cooperative co-evolutionary differential evolution algorithm applied for parameters identification of lithium-ion batteries

Cited by 15 publications

References 60 publications

Edge computing for vehicle battery management: Cloud-based online state estimation

Edge computing for vehicle battery management: Cloud-based online state estimation

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

Parameter estimation of Wiener-Hammerstein system based on multi-population self-adaptive differential evolution algorithm

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