FPGA Implementation of an Ant Colony Optimization Based SVM Algorithm for State of Charge Estimation in Li-Ion Batteries

Stighezza, Mattia; Bianchi, Valentina; Munari, Ilaria De

doi:10.3390/en14217064

Cited by 17 publications

(7 citation statements)

References 48 publications

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“…The particle swarm optimization algorithm (PSO) is proposed by Kennedy et al (Kennedy and Eberhart 1995) an algorithm for population-based searches that simulates the social behavior of flocks of birds. To order to avoid errors caused by empirical and random choices, the particle swarm optimization algorithm is chosen to optimize the parameters of the kernel function in this paper (Stighezza, Bianchi, and De Munari 2021). PSO is an algorithm that continuously searches for the best solution while considering various possible results.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

An improved particle swarm optimization-least squares support vector machine-unscented Kalman filtering algorithm on SOC estimation of lithium-ion battery

Zhou

Wang

Xie

et al. 2023

International Journal of Green Energy

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For real-time monitoring and safe control of electrical vehicles, it is important to accurately estimate the state of charge of lithium-ion batteries. A combined data-driven modeling approach based on Least squares support vector machine based on particle swarm optimization and unscented Kalman filter is proposed to obtain a better state of charge estimation accuracy. In this article, least squares support vector machine is used to establish the nonlinear connection between current, voltage, and SOC, and the parameters of least squares support vector machine are optimized by particle swarm optimization to improve the accuracy of voltage estimation, and the state and measurement equations are established by Least squares support vector machine in unscented Kalman filter for SOC estimation. The experimental results show that the maximum voltage error for the voltage prediction made with the PSO optimized model is 0.5 V. The maximum SOC error under various working situations is similarly kept to 0.5%, which is a significant improvement compared to the traditional algorithm. The above data show that the PSO considerably increases the precision of the Least squares support vector machine, as well as the estimation accuracy of the voltage and SOC, demonstrating the effectiveness of the model.

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

An improved particle swarm optimization-least squares support vector machine-unscented Kalman filtering algorithm on SOC estimation of lithium-ion battery

Zhou

Wang

Xie

et al. 2023

International Journal of Green Energy

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“…The use of ant colony optimization in resistor value estimation offers several advantages. It is a flexible [24] and adaptive approach [25] that can handle different resistor network configurations and accommodate variations in resistor characteristics. The algorithm's ability to leverage collective intelligence and distributed decision-making enables robust estimation even in the presence of noise or uncertainties in the circuit [26].…”

Section: Article Infomentioning

confidence: 99%

Ant Colony Optimization for Resistor Color Code Detection

Wibawanto,

Kirana,

Ramadhan

2023

Knowl. Eng. Data Sci.

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In the early stages of learning resistors, introducing color-based values is needed. Moreover, some combinations require a resistor trip analysis to identify. Unfortunately, a resistor body color is considered a local solution, which often confuses resistor coloration. Ant Colony Optimization (ACO) is a heuristic algorithm that can recognize problems with traveling a group of ants. ACO is proposed to select commercial matrix values to be computed without preventing local solutions. In this study, each explores the matrix based on pheromones and heuristic information to generate local solutions. Global solutions are selected based on their high degree of similarity with other local solutions. The first stage of testing focuses on exploring variations of parameter values. Applying the best parameters resulted in 85% accuracy and 43 seconds for 20 resistor images. This method is expected to prevent local solutions without wasteful computation of the matrix.

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“…RMSE is a goo measure of accuracy, but is applicable to comparing model predictions with data and no between variables. R squared is a statistical indicator that describes the amount of un certainty explained by an independent variable [16,17]. The empirical formula to calculate the predicted output is given as…”

Section: Machine Leaning Technique For Parameter Estimationmentioning

confidence: 99%

“…R squared is a statistical indicator that describes the amount of uncertainty explained by an independent variable [16,17].…”

Section: Machine Leaning Technique For Parameter Estimationmentioning

confidence: 99%

Novel Technique for Estimation of Cell Parameters Using MATLAB/Simulink

Surya

Saldanha²,

Williamson

2021

Electronics

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The main source of power in Electric Vehicles (EVs) is derived from batteries. An efficient cell model is extremely important for the development of complex algorithms like core temperature estimation, State of Health (SOH) estimation and State of Charge (SOC) estimation. In this paper, a new methodology for improving the SOC estimation using Equivalent Cell Model (ECM) approach is proposed. The modeling and simulations were performed using MATLAB/Simulink software. In this regard, a Li polymer cell was modeled as a single Resistor-Capacitor (RC) pair (R0, R1 and C1) model using PowerTrain blockset in MATLAB/Simulink software. To validate the developed model, a NASA dataset was used as the reference dataset. The cell model was tuned against the NASA dataset for different currents in such a way that the error in the terminal voltages (difference in terminal voltage between the dataset and the ECM) is <±0.2 V. The mean error and the standard deviation of the error were 0.0529 and 0.0310 respectively. This process was performed by tuning the cell parameters. It was found that the cell parameters were independent of the nominal capacity of the cell. The cell parameters of Li polymer and the Li ion cells (NASA dataset) were found be almost identical. These parameters showed dependence on SOC and temperature. The major challenge in a battery management system is the parameter estimation and prediction of SOC, this is because the degradation of battery is highly nonlinear in nature. This paper presents the parameter estimation and prediction of state of charge of Li ion batteries by implementing different machine learning techniques. The selection of the best suited algorithm is finalized through the performance indices mainly by evaluating the values of R- Squared. The parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on Support Vector Machine (SVM) technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out. Later, these parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on SVM technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out.

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FPGA Implementation of an Ant Colony Optimization Based SVM Algorithm for State of Charge Estimation in Li-Ion Batteries

Cited by 17 publications

References 48 publications

An improved particle swarm optimization-least squares support vector machine-unscented Kalman filtering algorithm on SOC estimation of lithium-ion battery

An improved particle swarm optimization-least squares support vector machine-unscented Kalman filtering algorithm on SOC estimation of lithium-ion battery

Ant Colony Optimization for Resistor Color Code Detection

Novel Technique for Estimation of Cell Parameters Using MATLAB/Simulink

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