Embedded Files
Scientific Paper in IEEE Conference Proceedings
Scientific Paper in IEEE Conference Proceedings
Wireless System-on-Cell for Smart Battery Management in EVs
Wireless System-on-Cell for Smart Battery Management in EVs
Ana C. Barros García1, Adrián Alonso Vilar1, Ana M. Cao1, Lucía Costas1, Uxía Fernández García2, Cintia M. Ramírez Navarro2, Mojtaba Ghodrati2,3, Sergi Zapater Villar2,3, Juan J. Rodríguez Andina1, Damián González Figueroa2, José Fariña Rodríguez1, Jorge Varela Barreras2,3,4
Ana C. Barros García1, Adrián Alonso Vilar1, Ana M. Cao1, Lucía Costas1, Uxía Fernández García2, Cintia M. Ramírez Navarro2, Mojtaba Ghodrati2,3, Sergi Zapater Villar2,3, Juan J. Rodríguez Andina1, Damián González Figueroa2, José Fariña Rodríguez1, Jorge Varela Barreras2,3,4
1 Dept. Electronics Technology, University of Vigo, Spain
2 Electric vehicle propulsion system and validation, CTAG, Spain
3 Dept. Electronic Engineering, UPC, Spain
4 Dept. Civil & Environmental Eng., Imperial College London, UK
Abstract: There is growing interest in developing compact and cost-effective battery management systems (BMSs) for next-generation electric vehicles. These systems aim to feature enhanced fault tolerance, active balancing capabilities, hardware-based diagnostic tools, and wireless communication to simplify wiring and improve cell traceability. To address these needs, a new application-specific integrated circuit (ASIC) has been designed and fabricated through the EUROPRACTICE program for integration into a system-on-cell BMS. The ASIC is developed following a staged approach, with parallel implementation and validation of key functionalities in proof-of-concept circuits. The target system is designed to include current, voltage, and temperature monitoring, LiFi-based wireless communication, NFC tag reading for cell identification, on-board impedance measurement, and reconfiguration capabilities for fault isolation and balancing.
Abstract: There is growing interest in developing compact and cost-effective battery management systems (BMSs) for next-generation electric vehicles. These systems aim to feature enhanced fault tolerance, active balancing capabilities, hardware-based diagnostic tools, and wireless communication to simplify wiring and improve cell traceability. To address these needs, a new application-specific integrated circuit (ASIC) has been designed and fabricated through the EUROPRACTICE program for integration into a system-on-cell BMS. The ASIC is developed following a staged approach, with parallel implementation and validation of key functionalities in proof-of-concept circuits. The target system is designed to include current, voltage, and temperature monitoring, LiFi-based wireless communication, NFC tag reading for cell identification, on-board impedance measurement, and reconfiguration capabilities for fault isolation and balancing.
Scientific Paper in IEEE Conference Proceedings
Scientific Paper in IEEE Conference Proceedings
A New Non-recursive Analytical Calibration Approach for
Li-ion ECMs based on Voltage Gradient and Sigmoid Function
A New Non-recursive Analytical Calibration Approach for
Li-ion ECMs based on Voltage Gradient and Sigmoid Function
Jorge Varela Barreras1,2,3, Sergi Zapater Villar1,2, Ramon Guzman2, Damián González Figueroa1
1 Dept. Electric Vehicle Propulsion System and Validation, CTAG, Spain
2 Dept. Electronic Engineering, UPC, Spain
3 Dept. Civil & Environmental Eng., Imperial College London, UK
Abstract: Electrical Equivalent Circuit Models (EECMs) are commonly used for Li-ion battery simulation and state estimation due to their balance of accuracy and simplicity. However, their parameters change with operating conditions such as temperature, state-of-charge, and state-of-health, making traditional offline parameterization methods, based on static look-up tables, prone to inaccuracies. Existing online parameter estimation methods, including recursive least squares and Kalman filters, are computationally demanding and therefore often applied only at the pack level, neglecting cell-to-cell variations that worsen with ageing. Additionally, current methods struggle with the battery's mixed dynamics, introducing numerical challenges and higher memory requirements. Multi-timescale approaches offer solutions but still face significant complexity. In this paper, a new method is proposed to calibrate multi-order EECM based, for the first time in the literature, on non-recursive analysis of the terminal voltage gradient and key insights provided by the sigmoidal nature of the variable first order time constant. This simpler analytical approach requires minimal data, computational demand, and knowledge of prior battery characteristics. Exemplary simulation results based on synthetic data proof the robustness of the method, showing an error in cell voltage estimation <1% over a 90 s forecast period under CC discharge conditions. The method also provides key insights into the system’s dynamic behavior in relation to the order of the system and the calculation of its time constants, with an error <1%, from a relatively short time series data.
Abstract: Electrical Equivalent Circuit Models (EECMs) are commonly used for Li-ion battery simulation and state estimation due to their balance of accuracy and simplicity. However, their parameters change with operating conditions such as temperature, state-of-charge, and state-of-health, making traditional offline parameterization methods, based on static look-up tables, prone to inaccuracies. Existing online parameter estimation methods, including recursive least squares and Kalman filters, are computationally demanding and therefore often applied only at the pack level, neglecting cell-to-cell variations that worsen with ageing. Additionally, current methods struggle with the battery's mixed dynamics, introducing numerical challenges and higher memory requirements. Multi-timescale approaches offer solutions but still face significant complexity. In this paper, a new method is proposed to calibrate multi-order EECM based, for the first time in the literature, on non-recursive analysis of the terminal voltage gradient and key insights provided by the sigmoidal nature of the variable first order time constant. This simpler analytical approach requires minimal data, computational demand, and knowledge of prior battery characteristics. Exemplary simulation results based on synthetic data proof the robustness of the method, showing an error in cell voltage estimation <1% over a 90 s forecast period under CC discharge conditions. The method also provides key insights into the system’s dynamic behavior in relation to the order of the system and the calculation of its time constants, with an error <1%, from a relatively short time series data.
Scientific Poster in International Congress
Scientific Poster in International Congress
Gestión Inteligente de Batería a Nivel de Celda en Vehículos Eléctricos de Baterías
Gestión Inteligente de Batería a Nivel de Celda en Vehículos Eléctricos de Baterías
Damián González Figueroa1, Ana C. Barros García2, Adrián Alonso Vilar2, Ana M. Cao2, Lucía Costas2, Uxía Fernández García1, Cintia M. Ramírez Navarro1, Mojtaba Ghodrati1,3, Sergi Zapater Villar1,3, Juan J. Rodríguez Andina2,, José Fariña Rodríguez2, Jorge Varela Barreras1,3,4
Damián González Figueroa1, Ana C. Barros García2, Adrián Alonso Vilar2, Ana M. Cao2, Lucía Costas2, Uxía Fernández García1, Cintia M. Ramírez Navarro1, Mojtaba Ghodrati1,3, Sergi Zapater Villar1,3, Juan J. Rodríguez Andina2,, José Fariña Rodríguez2, Jorge Varela Barreras1,3,4
1 Electric vehicle propulsion system and validation, CTAG, Spain
2 Dept. Electronics Technology, University of Vigo, Spain
3 Dept. Electronic Engineering, UPC, Spain
4 Dept. Civil & Environmental Eng., Imperial College London, UK
Presenter: Damián González, CTAG
Where: Batteries Event 2024, France
Título: SISTEMA INTEGRADO DE GESTIÓN DE BATERÍA PARA UN VEHÍCULO ELÉCTRICO
Título: SISTEMA INTEGRADO DE GESTIÓN DE BATERÍA PARA UN VEHÍCULO ELÉCTRICO
Assignee: CTAG
Assignee: CTAG
Inventors: FERRO SANTIAGO, Esteban y SEGOVIA ROMERO, Miguel
Inventors: FERRO SANTIAGO, Esteban y SEGOVIA ROMERO, Miguel
Filing Date: 07.05.2020
Filing Date: 07.05.2020
Grant Date: 30.12.2022
Grant Date: 30.12.2022
Publication Date: 09.01.2023
This work is part of the R&D projects TED2021-131718B-C21 and TED2021-131718B-C22, funded by MICIU/AEI/10.13039/501100011033/ and by the “European Union NextGenerationEU/PRTR”.
Page updated
Google Sites
Report abuse