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

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 t...

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Detalles Bibliográficos
Autores: Rojas Dueñas, Gabriel, Riba Ruiz, Jordi-Roger|||0000-0001-8774-2389, Moreno Eguilaz, Juan Manuel|||0000-0001-6086-7034
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/350170
Acceso en línea:https://hdl.handle.net/2117/350170
https://dx.doi.org/10.1016/j.measurement.2021.109838
Access Level:acceso abierto
Palabra clave:DC-to-DC converters
DC-DC bidirectional converter
Mild hybrid electric vehicle
Deep learning
Modeling
Neural network
Convertidors continu-continu
Vehicles elèctrics híbrids
Àrees temàtiques de la UPC::Enginyeria elèctrica
Descripción
Sumario: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.