Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements

Abstract This paper presents a non-intrusive approach for modeling a bidirectional DC-DC converter used in mild hybrid electric vehicles. A black-box identification methodology is proposed to find a model based on the data acquired from the input/output terminals. Measured data include the steady state and transient response, and different operating conditions of the DC-DC converter, including the buck and boost modes. A deep learning architecture based on a long-short-term memory neural network (LSTM-NN) is applied. The trained network is tested under a set of operating points different from those used during the training stage. The proposed method is compared with three black-box modeling techniques commonly used in power converters, proving its superior performance. Results presented in this paper indicate that the proposed model is able to replicate the behavior of the bidirectional converter without a priori knowledge of the converter circuitry. This approach can also be applied to other power devices.

[1]  Dan Wang,et al.  Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting , 2019, International Journal of Electrical Power & Energy Systems.

[2]  Sangshin Kwak,et al.  A Flexible Voltage Bus Converter for the 48-/12-V Dual Supply System in Electrified Vehicles , 2017, IEEE Transactions on Vehicular Technology.

[3]  K. S. Xiahou,et al.  Diagnosis of Multiple Open-Circuit Switch Faults Based on Long Short-Term Memory Network for DFIG-Based Wind Turbine Systems , 2020, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[4]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[5]  F. Alonge,et al.  Identification and Robust Control of DC/DC Converter Hammerstein Model , 2008, IEEE Transactions on Power Electronics.

[6]  Zifan Liu,et al.  Synthesis and Experimental Validation of Battery Aging Test Profiles Based on Real-World Duty Cycles for 48-V Mild Hybrid Vehicles , 2017, IEEE Transactions on Vehicular Technology.

[7]  Sergio Saponara,et al.  Design and Measurement of Integrated Converters for Belt-Driven Starter-Generator in 48 V Micro/Mild Hybrid Vehicles , 2017, IEEE Transactions on Industry Applications.

[8]  Sergio Saponara,et al.  Electric Drives and Power Chargers: Recent Solutions to Improve Performance and Energy Efficiency for Hybrid and Fully Electric Vehicles , 2020, IEEE Vehicular Technology Magazine.

[9]  Rafael Asensi,et al.  Blackbox Polytopic Model With Dynamic Weighting Functions for DC-DC Converters , 2019, IEEE Access.

[10]  Pascal Venet,et al.  Practical Online Estimation of Lithium-Ion Battery Apparent Series Resistance for Mild Hybrid Vehicles , 2016, IEEE Transactions on Vehicular Technology.

[11]  Haihua Xu,et al.  Electromagnetic Transient Modeling and Simulation of Power Converters Based on a Piecewise Generalized State Space Averaging Method , 2019, IEEE Access.

[12]  Damian Giaouris,et al.  Advances on System Identification Techniques for DC–DC Switch Mode Power Converter Applications , 2019, IEEE Transactions on Power Electronics.

[13]  Jun Bi,et al.  Residual range estimation for battery electric vehicle based on radial basis function neural network , 2018, Measurement.

[14]  Hongwen He,et al.  Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles , 2018, IEEE Access.

[15]  Nando de Freitas,et al.  Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.

[16]  Wuneng Zhou,et al.  A hybrid electricity price forecasting model with Bayesian optimization for German energy exchange , 2019, International Journal of Electrical Power & Energy Systems.

[17]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[18]  Manuel Moreno-Eguilaz,et al.  Nonlinear Least Squares Optimization for Parametric Identification of DC–DC Converters , 2021, IEEE Transactions on Power Electronics.

[19]  Shima Nazari,et al.  Optimal Energy Management for a Mild Hybrid Vehicle With Electric and Hybrid Engine Boosting Systems , 2019, IEEE Transactions on Vehicular Technology.

[20]  Manuel Moreno-Eguilaz,et al.  Black-Box Modelling of a DC-DC Buck Converter Based on a Recurrent Neural Network , 2020, 2020 IEEE International Conference on Industrial Technology (ICIT).

[21]  Paul D. Walker,et al.  Comparative fuel economy, cost and emissions analysis of a novel mild hybrid and conventional vehicles , 2018 .

[22]  Rafael Asensi,et al.  Modeling Electronic Power Converters in Smart DC Microgrids—An Overview , 2018, IEEE Transactions on Smart Grid.

[23]  Yong Yu,et al.  A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures , 2019, Neural Computation.

[24]  Azzeddine Bakdi,et al.  Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems , 2021, Measurement.

[25]  Houshang Darabi,et al.  LSTM Fully Convolutional Networks for Time Series Classification , 2017, IEEE Access.

[26]  Hans-Georg Herzog,et al.  System Identification and Modeling of an Automotive Bidirectional DC/DC Converter , 2019, 2019 IEEE Vehicle Power and Propulsion Conference (VPPC).

[27]  Marina Sanz,et al.  Black-Box Behavioral Modeling and Identification of DC–DC Converters With Input Current Control for Fuel Cell Power Conditioning , 2014, IEEE Transactions on Industrial Electronics.