Load identification of domestic induction heating based on Particle Swarm Optimization

Domínguez, A.; Otín, A.; Urriza, I.; Barragán, L.A.; Navarro, D.; Artigas, J.I.

doi:10.1109/compel.2014.6877162

Cited by 13 publications

(9 citation statements)

References 9 publications

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“…Most of the previously proposed techniques are aimed at obtaining the equivalent electrical parameters of the load. In [10], a high-performance hardware is proposed for on-line identification, whereas in [14] and [15] particle swarm optimization and the pseudo energy method are employed, respectively, to achieve off-line identifications. However, all these methods are only valid for off-line calculation.…”

Section: B Review Of Identification and Detection Systemsmentioning

confidence: 99%

Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances

et al. 2019

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Induction heating has become the reference technology for domestic heating applications due to its benefits in terms of performance, efficiency and safety, among others. In this context, recent design trends aim at providing highly flexible cooking surfaces composed of multi-coil structures. As in many other wireless power transfer systems, one of the main challenges to face is the proper detection of the magnetic coupling with the induction heating load in order to provide improved thermal performance and safe power electronic converter operation. This is specially challenging due to the high variability in the materials used in cookware as well as the random pot placement in flexible induction heating appliances. This paper proposes the use of deep learning techniques in order to provide accurate area overlap estimation regardless of the used pot and its position. An experimental test-bench composed of a complete power converter, multi-coil system and real-time measurement system has been implemented and used in this study to characterize the parameter variation with overlapped area. Convolutional neural networks are then proposed as an effective method to estimate the covered area, and several implementations are studied and compared according to their computational cost and accuracy. As a conclusion, the presented deep learning-based technique is proposed as an effective tool to estimate the magnetic coupling between the coil and the induction heating load in advanced induction heating appliances.INDEX TERMS Inductive heating, resonant power conversion, wireless power transfer, deep learning, neural network, electromagnetic design, induction heating, home appliances.

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Section: B Review Of Identification and Detection Systemsmentioning

confidence: 99%

Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances

et al. 2019

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“…Similarly, this method is focused only on the S/S-type IPT system. In [21] the authors treat a similar problem by applying the Particle Swarm Optimization (PSO) algorithm to identify the load parameters in an induction heating application. In [22], multiple coils mounted on the primary and secondary couplers are used to detect the position of the EV.…”

Section: Introductionmentioning

confidence: 99%

Load Segment Tracking Control of IPT System Based on Power Efficiency Optimization

et al. 2020

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In the induction electric energy transmission system applied to fix point charging of vehicles, the mutual inductance change caused by position deviation or air gap change, and the equivalent load change caused by power demand change will make the output power and efficiency fluctuate, thus affecting the output stability of the system. In order to ensure the reliability of the charging process of the system and at the same time have a strong power output capacity, this paper proposes a load segmental tracking control strategy based on the optimal power efficiency. This control strategy adjusts the equivalent load to track different power efficiency indicator curves according to different mutual inductance states of the system and power demand when the vehicle is static charging, so that it can always maintain the optimal power efficiency state according to different charging conditions and improve the system performance. Finally, simulation and experimental verification are carried out under different states of mutual inductance. The results show that this method can achieve the equivalent load segmental tracking under different mutual inductance states, and always maintain the optimal power efficiency output. INDEX TERMS Efficiency, inductive power transmission, load tracking, optimization, power.

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“…Several works have dealt with these aspects [5]- [9]. In [5] the authors treat a similar problem by applying the Particle Swarm Optimization (PSO) algorithm to identify the load parameters in an induction heating application.…”

Section: Introductionmentioning

confidence: 99%

“…In [5] the authors treat a similar problem by applying the Particle Swarm Optimization (PSO) algorithm to identify the load parameters in an induction heating application. In [6], multiple coils mounted on the primary and secondary couplers are used to detect the position of the EV.…”

Section: Introductionmentioning

confidence: 99%

Load identification in dynamic wireless power transfer system utilizing current injection in the transmitting coil

Khalilian

Rosu

Cirimele

et al. 2016

2016 IEEE Wireless Power Transfer Conference (WPTC)

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In this paper, a load identification approach for dynamic wireless power transfer for electric vehicles is introduced. In the proposed method, determination of the load parameters and position are achieved without requiring any communication link between the transmitter and receiver or using any extra coil. The load detection procedure is achievable using two modes: the energy injection mode and the free resonant mode. The operating principles and theoretical analysis of this method are presented in this paper, together with experimental results.

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Load identification of domestic induction heating based on Particle Swarm Optimization

Cited by 13 publications

References 9 publications

Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances

Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances

Load Segment Tracking Control of IPT System Based on Power Efficiency Optimization

Load identification in dynamic wireless power transfer system utilizing current injection in the transmitting coil

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