From ROM of Electrochemistry to AI-Based Battery Digital and Hybrid Twin
Article dans une revue avec comité de lecture
Author
Date
2020Journal
Archives of Computational Methods in EngineeringAbstract
Lithium-ion batteries are widely used in the automobile industry (electric vehicles and hybrid electric vehicles) due to their high energy and power density. However, this raises new safety and reliability challenges which require development of novel sophisticated Battery Management Systems (BMS). A BMS ensures the safe and reliable operation of a battery pack and to realize it a model must be solved. However, current BMSs are not adapted to the specifications of the automotive industry, as they are unable to give accurate results at real-time rates and during a wide operation range. For this reason, the main focus of this work is to develop a Hybrid Twin, as introduced in Chinesta et al. (Arch Comput Methods Eng (in press), 2018. https://doi.org/10.1007/s11831-018-9301-4), so as to meet the requirements of the new generation of BMS. To achieve this, three reduced order model techniques are applied to the most commonly used physics-based models, each one for a different range of application. First, a POD model is used to greatly reduce the simulation time and the computational effort for the pseudo-2D model, while maintaining its accuracy. In this way, cell design, optimization of parameters, and simulation of battery packs can be done while saving time and computational resources. In addition, its real-time performance has been studied. Next, a regression model is constructed from data by using the sparse-Proper Generalized Decomposition (s-PGD). It is shown that it achieves real-time performance for the whole electric vehicle (EV) system with a battery pack. In addition, this regression model can be used in a BMS without issues because of the simple algebraic expression obtained. A simulation of the EV with the proposed approach is demonstrated using the system simulation tool SimulationX (ESI ITI GmbH. Dresden, Germany). Furthermore, the Digital Twin created using the s-PGD does not only allow for real-time simulations, but it can also adapt its predictions taking into consideration the real driving conditions and the real driving cycle to change the planning in real-time. Finally, a data-driven model based
Files in this item
Related items
Showing items related by title, author, creator and subject.
-
Article dans une revue avec comité de lectureA novel model order reduction (MOR) technique is presented to achieve fast and real-time predictions as well as high-dimensional parametric solutions for the electromagnetic force which will help the design, analysis of ...
-
Article dans une revue avec comité de lectureSANCARLOS, Abel; DUVAL, Jean-Louis; ZERBIB, Nicolas; CUETO, Elias; GHNATIOS, Chady; CHINESTA SORIA, Francisco (MDPI AG, 2021)A novel Model Order Reduction (MOR) technique is developed to compute high-dimensional parametric solutions for electromagnetic fields in synchronous machines. Specifically, the intrusive version of the Proper Generalized ...
-
Article dans une revue avec comité de lectureGHNATIOS, Chady; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; CUETOS, Elias; DUVAL, Jean-Louis; CHINESTA SORIA, Francisco (Elsevier, 2019)This work aims at proposing a new procedure for parametric problems whose separated representation has been considered difficult, or whose SVD compression impacted the results in terms of performance and accuracy. The ...
-
Article dans une revue avec comité de lectureGHNATIOS, Chady; DELPLACE, Frank; BARASINSKI, Anais; DUVAL, Jean-Louis; CUETO, Elias; AMMAR, Amine; CHINESTA SORIA, Francisco (Wiley, 2020)Composite manufacturing processes usually proceed from preimpregnated preforms that are consolidated by simultaneously applying heat and pressure, so as to ensure a perfect contact compulsory for making molecular diffusion ...
-
Article dans une revue avec comité de lectureCUETO, Elías G.; DUVAL, Jean-Louis; IBAÑEZ, Ruben; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; CHINESTA SORIA, Francisco (Springer Verlag, 2019)Compressed sensing is a signal compression technique with very remarkable properties. Among them, maybe the most salient one is its ability of overcoming the Shannon–Nyquist sampling theorem. In other words, it is able to ...