Virtual prototyping and pre-sizing methodology for buck DC–DC converters using genetic algorithms

Lefranc, Pierre; Jannot, Xavier; Dessante, Philippe

doi:10.1049/iet-pel.2010.0284

Cited by 24 publications

(32 citation statements)

References 7 publications

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“…Resonant tank of DAB [15] DE-PSO HFT (SST) [16] GA Boost PFC converter [17] MOGA MFT in DAB and LLC converters [18] Buck converter [19] Resonant boost HB [20] Boost converter [21] Other GP Boost and synchronous buck [22] -Quickly produce globally optimum designs.…”

Section: Alpfmentioning

confidence: 99%

“…Conventionally, the double-pole frequency f p and double-zero frequency f z can be determined through the 'k-factor' method considering Equation 16-Equation (19) where φ boost(III) is the phase boost of the controller, f c is the cut-off frequency of controller, T(s) is the magnitude of open-loop gain determined at the cut-off frequency f c :…”

Section: Of 31mentioning

confidence: 99%

“…Nonlinear programming (NP) based on Lagrangian functions [11], augmented Lagrange penalty function (ALPF) [12] and sequential quadratic programming (SQP) [13] can be categorized as the gradient-based techniques. On the other hand, the derivative-free techniques are Through the literature, numerous optimization techniques for HW sizing of the PE converters can be classified into four main types: (i) Pareto-front (PF) analysis [6][7][8][9][10] based on brute-force (BF) search (known as exhaustive search), (ii) gradient-based algorithms [11][12][13], (iii) derivative-free based algorithms [14][15][16][17][18][19][20][21], and (iv) other types such as geometry programming [22,23]. Generally, the main design variables involve switching frequency, geometry parameters, inductance-or capacitance-values, and thermal resistance.…”

Section: Introductionmentioning

confidence: 99%

“…Nonlinear programming (NP) based on Lagrangian functions [11], augmented Lagrange penalty function (ALPF) [12] and sequential quadratic programming (SQP) [13] can be categorized as the gradient-based techniques. On the other hand, the derivative-free techniques Energies 2020, 13, 5167 3 of 31 are based on evolutionary nature-inspired algorithms such as particle swarm optimization (PSO) [14,15], differential evolution (DE) combined PSO [16], genetic algorithm (GA) [17] and multi-objective genetic algorithm (MOGA) [18][19][20][21]. A review of those metaheuristic-based optimizations in the PE application was comprehensively reported in [25].…”

Section: Introductionmentioning

confidence: 99%

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NSGA-II-Based Codesign Optimization for Power Conversion and Controller Stages of Interleaved Boost Converters in Electric Vehicle Drivetrains

et al. 2020

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This article proposes a holistic codesign optimization framework (COF) to simultaneously optimize a power conversion stage and a controller stage using a dual-loop control scheme for multiphase SiC-based DC/DC converters. In this study, the power conversion stage adopts a non-isolated interleaved boost converter (IBC). Besides, the dual-loop control scheme uses type-III controllers for both inner- and outer- loops to regulate the output voltage of the IBC and tackle its non-minimum phase issue. Based on the converter architecture, a multi-objective optimization (MOO) problem including four objective functions (OFs) is properly formulated for the COF. To this end, total input current ripple, total weight of inductors and total power losses are selected as three OFs for the power conversion stage whilst one OF called integral of time-weighted absolute error is considered for the controller stage. The OFs are expressed in analytical forms. To solve the MOO problem, the COF utilizes a non-dominated sorted genetic algorithm (NSGA-II) in combination with an automatic decision-making algorithm to obtain the optimal design solution including the number of phases, switching frequency, inductor size, and the control parameters of type-III controllers. Furthermore, compared to the conventional ‘k-factor’ based controller, the optimal controller exhibits better dynamic responses in terms of undershoot/overshoot and settling time for the output voltage under load disturbances. Moreover, a liquid-cooled SiC-based converter is prototyped and its optimal controller is implemented digitally in dSPACE MicroLabBox. Finally, the experimental results with static and dynamic tests are presented to validate the outcomes of the proposed COF.

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Section: Alpfmentioning

confidence: 99%

Section: Of 31mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Nonlinear programming (NP) based on Lagrangian functions [11], augmented Lagrange penalty function (ALPF) [12] and sequential quadratic programming (SQP) [13] can be categorized as the gradient-based techniques. On the other hand, the derivative-free techniques Energies 2020, 13, 5167 3 of 31 are based on evolutionary nature-inspired algorithms such as particle swarm optimization (PSO) [14,15], differential evolution (DE) combined PSO [16], genetic algorithm (GA) [17] and multi-objective genetic algorithm (MOGA) [18][19][20][21]. A review of those metaheuristic-based optimizations in the PE application was comprehensively reported in [25].…”

Section: Introductionmentioning

confidence: 99%

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NSGA-II-Based Codesign Optimization for Power Conversion and Controller Stages of Interleaved Boost Converters in Electric Vehicle Drivetrains

et al. 2020

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“…The most common objective functions to minimize are weight [98,99,[102][103][104], cost [32,99,102,[104][105][106][107][108], volume [102,[108][109][110], emitted radiations and thermal resistance of a heatsink [111], area [104,105] and power losses. Alternatively, the optimization can be achieved through efficiency maximization [57, 69, 98, 99, 101, 103-105, 108-110, 112-115].…”

Section: B Control Tuningmentioning

confidence: 99%

Metaheuristic Optimization Methods Applied to Power Converters: A Review

León-Aldaco

Calleja

Aguayo

2015

IEEE Trans. Power Electron.

152

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Power converters systems aimed at renewable energy applications have become a common option for sustainable electricity and distributed generation, since their performance has improved, and prices have steadily been reduced in the last years. However, there are still several drawbacks that hinder their widespread installation, such as the simultaneous minimization of cost and volume, efficiency maximization, size reduction, etc. Quite often, accomplishing these goals requires dealing with complicated optimization problems, which are difficult to solve by classical methods. Metaheuristic techniques provide a viable alternative to solve complex, intricate optimization problems, such as those encountered in the development of power electronics converters. This paper presents a comprehensive coverage of metaheuristic methodologies applied in the area of power converters. The review includes a classification of the methodologies and main objective functions in each paper surveyed. An aim for this paper is to highlight the importance of the optimization tools, and the many benefits they provide to tackle the challenges encountered in the design, operation and control of power converters. O 0885-8993 (c)

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Generalized game‐theoretic approach for the design of energy storage elements of higher‐order DC‐DC converters

Kulshreshtha

Saxena

2021

Int Trans Electr Energ Syst

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Virtual prototyping and pre-sizing methodology for buck DC–DC converters using genetic algorithms

Cited by 24 publications

References 7 publications

NSGA-II-Based Codesign Optimization for Power Conversion and Controller Stages of Interleaved Boost Converters in Electric Vehicle Drivetrains

NSGA-II-Based Codesign Optimization for Power Conversion and Controller Stages of Interleaved Boost Converters in Electric Vehicle Drivetrains

Metaheuristic Optimization Methods Applied to Power Converters: A Review

Generalized game‐theoretic approach for the design of energy storage elements of higher‐order DC‐DC converters

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