Differential evolution based regression algorithm for mathematical representation of electrical parameters in lithium-ion battery model

Vyas, Ujjval B.; Shah, Varsha A.

doi:10.1016/j.est.2021.103673

Cited by 12 publications

(5 citation statements)

References 36 publications

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“…Similarly, the optimization techniques presented in (Mehedi et al, 2021; Prasad & Vinod Kumar, 2017) can help improve the system's accuracy by estimating power system parameters. Other optimization technologies in (Houssein, 2021; Li et al, 2022; Vyas & Shah, 2022) help determine the state of different types of electrical equipment under consideration. The increasing demand for artificial intelligence and neural networks is a saviour of nonlinear system calibration.…”

Section: Detailed Analysis Of Available Literaturementioning

confidence: 99%

“…The updated fitness in each iteration indicates a unique solution for the objective function in the optimization environment. An example of a differential evolution algorithm based on regression methods is discussed in (Vyas & Shah, 2022) to apply state estimation of a battery management system. The nature‐inspired algorithm influenced by the social behaviour of a flock of birds is regarded as particle swarm optimization.…”

Section: Estimation Of Frequency Componentsmentioning

confidence: 99%

“…The mathematical expression for the polynomial regression model of degree ‘ d ’ with ‘ n ’ number of variables is expressed in Equation . The polynomial regression model proposed in impedance analysis for the automation and control of electrochemical sources is discussed (Vyas & Shah, 2022).…”

Section: Calibration To Estimate Rms Values and Fundamental Frequency...mentioning

confidence: 99%

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Calibration and frequency estimation in sensors for electrical parameter measurement using regression and metaheuristic based models

2022

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Calibration is the backbone of any sensor and measurement philosophy. The conventional calibration techniques for electrical parameter measurement using nonlinear sensors are affected by the repeating analogue signals and lead to errors in measurement. This research paper investigates different regression-based mathematical models to calibrate the Hall sensor for measuring RMS and the fundamental frequency. The novelty of this research work lies in the feature-based input modelling to measure RMS current and frequency with an error of 3.37e-12% and 7.61e-9%, respectively. The conventional Fourier transform method is compared with six different bio-inspired metaheuristic algorithms to estimate the frequency components of the analogue signal received from the measurement setup. Apart from the conventional sine waveforms, this paper investigates sawtooth and square waveforms as the periodic signals for determining the frequency components. The results from the comparison study show that the Whale Optimization Algorithm exhibits 1.25% lesser error whilst predicting the features in measured frequency components. Apart from this, the paper identifies a unique combination of features that effectively measures the instantaneous electrical parameter with RMSE, NRMSE, and R 2 value of 158.8, 0.51, and 0.44, respectively. Experimentally it is found that the decision tree and random forest regression models calibrate the Hall sensor with 93% less error than their linear and polynomial counterparts.

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Section: Detailed Analysis Of Available Literaturementioning

confidence: 99%

Section: Estimation Of Frequency Componentsmentioning

confidence: 99%

Section: Calibration To Estimate Rms Values and Fundamental Frequency...mentioning

confidence: 99%

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Calibration and frequency estimation in sensors for electrical parameter measurement using regression and metaheuristic based models

2022

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“…In this work, literature 3 [32] considers the effect of hysteresis on SoC estimation and applies a differential evolution based SoC estimation technique to accurately estimate SoC while minimizing the hysteresis effect. The literature [33] proposes a regression algorithm with differential evolution to represent polynomials of SOC-related parameters, Gaussians, and parameters of sine and exponential equations as a function of SOC, reducing the memory footprint. In summary, EKF remedies the defects of Kalman filtering and can obtain optimal solutions to local problems, but it is affected by process noise, and the differential evolution algorithm is flexible enough to be used for optimization in different fields.…”

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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“…It is essential to organize and analyze data regarding the mental health of students in order to realize improvements in the efficacy of mental health education provided in schools. e psychological data of college students can be mined in-depth using a variety of methods, such as classification algorithms [4][5][6], regression algorithms [7][8][9], the cluster analysis method [10][11][12], big data technology [13][14][15], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Mental Health Management and Cognitive Behavior Analysis Model of College Students Using Multi-View Clustering Analysis Algorithm

Dong

Shen

2022

Computational Intelligence and Neuroscience

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In this new era that is full of social changes, ongoing economic transformation, an abundance of information resources, and a fast pace of life, the pressure that people feel to compete with one another is also increasing day by day. Because of the vast differences in people’s states of consciousness and worldviews, interpersonal relationships have become increasingly difficult to navigate. Students in higher education institutions will eventually emerge as the dominant demographic in society. Their mental health has a significant bearing on all aspects of life, including learning and future growth. An objective condition that must be met in order to guarantee that the next generation of talent will have a high level of overall quality is the improvement of the mental health of college students (CSMH) in the new era. One component of public health is the emotional well-being of students in higher education. The state of the public’s health is consistently ranked among the most urgent problems facing modern society. However, there is not much hope for the Chinese CSMH. In order to effectively manage their mental health, a variety of educational institutions, including colleges and universities, have proposed a large number of management strategies for CSMH. The vast majority of these strategies are not targeted, and they do not offer a variety of management strategies that are based on the many different psychological states. It is necessary to first be able to accurately predict the mental health status of each individual college student in order to achieve the goal of improving the mental health management of students attending colleges and universities. This study proposes using a multi-view K-means algorithm, abbreviated as MvK-means, to analyze the CSMH’s data on mental health. This is possible because the data can be obtained from multiple perspectives. This paper presents a multi-view strategy as well as a weight strategy in light of the fact that each point of view contributes in its own unique way. Different weight values should be assigned to each view’s data, which will ultimately result in an improved evaluation effect of the model. The findings of the experiments indicate that the model that was proposed has a beneficial impact on the analysis of the data pertaining to the mental health of college students.

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Differential evolution based regression algorithm for mathematical representation of electrical parameters in lithium-ion battery model

Cited by 12 publications

References 36 publications

Calibration and frequency estimation in sensors for electrical parameter measurement using regression and metaheuristic based models

Calibration and frequency estimation in sensors for electrical parameter measurement using regression and metaheuristic based models

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

A Mental Health Management and Cognitive Behavior Analysis Model of College Students Using Multi-View Clustering Analysis Algorithm

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