Black-Box Modelling of a DC-DC Buck Converter Based on a Recurrent Neural Network

Rojas-Dueñas, Gabriel; Riba, Jordi-Roger; Kahalerras, Khaled; Moreno-Eguilaz, Manuel; Kadechkar, Akash; Gómez-Pau, Álvaro

doi:10.1109/icit45562.2020.9067098

Cited by 31 publications

(22 citation statements)

References 11 publications

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“…In refs. [15][16][17][18][19][20][21][22], various adaptive-based controllers are designed as follows: adaptive neural network-based controllers, adaptive sliding mode controllers, adaptive Lyapunov-based control, and adaptive predictive controllers. The main benefits presented by these methods are effective to control in ill-defined systems, robustness toward load uncertainties, faster dynamics performance, and better external disturbance compensation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive backstepping controller design on Buck converter with a novel improved identification method

Saadat

Ghamari

Mollaee

2022

IET Control Theory & Appl

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An adaptive backstepping method is presented by this paper for a DC-DC Buck converter utilising a strategy for system identification with pulse width modulation in the presence of parametric uncertainties, load variations, and high variance noises. In this control structure, the system is assumed as a black-box block that can decrease the computational burden providing faster dynamics. An adaptive mechanism is adopted for the BSM using the Lyapunov definition, providing robust dynamics for the controller against various disturbances. In addition, a novel improved exponential recursive least-squares identification algorithm is proposed, which shows higher robustness in parametric estimations and can decrease the negative impact of disrupting factors on the estimator. Moreover, a particle swarm optimisation algorithm-based PID controller is designed to be compared with the proposed controller. Finally, the merits of the presented controller are validated for various working conditions through simulations and experiments. It can be seen that the adaptive backstepping method with the improved identification technique provides much better results with faster dynamics.

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Section: Introductionmentioning

confidence: 99%

Adaptive backstepping controller design on Buck converter with a novel improved identification method

Saadat

Ghamari

Mollaee

2022

IET Control Theory & Appl

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“…In addition to the introduction of AI in the EV market, DC-DC converter controllers based on NN supervised/unsupervised learning and reinforcement learning techniques are powerful tools concerning the noise and uncertainties [47][48][49][50]. AI networks were used to identify a black-box converter model in [51]. The neural network predictive controller (NNPC) that combined the advantages of both the NN and MPC was applied to the buck converter in [47], which investigated the accuracy during start-up and during the reference voltage variation.…”

Section: Introductionmentioning

confidence: 99%

Theoretical and Experimental Analysis of a New Intelligent Charging Controller for Off-Board Electric Vehicles Using PV Standalone System Represented by a Small-Scale Lithium-Ion Battery

2022

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Electric vehicles are rapidly infiltrating the power grid worldwide, initiating an immediate need for a smart charging technique to maintain the stability and robustness of the charging process despite the generation type. Renewable energy sources (RESs), especially photovoltaic (PV), are becoming the essential source for electric vehicle charging points. The stochastic behavior of the PV output power affects the power conversion for regulating the battery charger voltage levels, which influences the battery to overheat and degrade. This study presents a PV standalone smart charging process for off-board plug-in electric vehicles, represented by a small-scale lithium-ion battery based on the multistage charging currents (MSCC) protocol. The charger comprises a DC–DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory recurrent neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. Additionally, it was used as an alarm flag for any possible PV output shortage during the charging process in the long- and short-term prediction to be supported by any other electricity source. The NNPC–LSTM controller was compared with the fuzzy logic and the conventional PID controllers while varying the input voltage and implementing the MSCC protocol. The proposed charging controller perfectly ensured that the minimum battery terminal voltage ripple and charging current ripple reached 1 mV and 1 mA, respectively, with a very high-speed response of 1 ms in reaching the predetermined charging current stages. The present simulated and experimental results are in good agreement with the previous related work in the literature survey.

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“…In [17] a LSTM-NN was applied to model the transient behavior of a DC-DC power converter used in mild hybrid electric vehicles. In [18] a recursive artificial neural network, in this case a nonlinear autoregressive exogenous neural network (NARX-NN) was applied for reproducing the behavior of a DC-DC buck converter at the expense of the time required to train the network. Wavelet artificial neural networks (WA-NNs) have been also applied for this purpose.…”

Section: Introductionmentioning

confidence: 99%

CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine

Rojas-Dueñas

Riba

Moreno-Eguilaz

2021

Sensors

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This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and predict the future performance of the converter and specifically of the electrolytic capacitors, in order to avoid malfunctioning and failures, since it is known they have the highest failure rates among power converter components. To this end, accelerated aging tests of the electrolytic capacitors are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM model that is capable of predicting the future values of the capacitance and the equivalent series resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful life of the device, thus, increasing the reliability of the system and ensuring an adequate operating condition of the electric motor.

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Black-Box Modelling of a DC-DC Buck Converter Based on a Recurrent Neural Network

Cited by 31 publications

References 11 publications

Adaptive backstepping controller design on Buck converter with a novel improved identification method

Adaptive backstepping controller design on Buck converter with a novel improved identification method

Theoretical and Experimental Analysis of a New Intelligent Charging Controller for Off-Board Electric Vehicles Using PV Standalone System Represented by a Small-Scale Lithium-Ion Battery

CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine

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