From ROM of Electrochemistry to AI-Based Battery Digital and Hybrid Twin

Abstract

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. 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 realtime 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 on the employment of Dynamic Mode Decomposition techniques is developed to extract an on-line model that corrects the gap between prediction and measurement, thus constructing the first (to our knowledge) hybrid twin of a Li-ion battery able to self-correct from data. In addition, thanks to this model, the above gap is corrected during the driving process, taking into consideration real-time restrictions.

A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors

“…If needed, standard references concerning the Proper Generalized Decomposition (PGD) method for solving PDEs can be reviewed in [5,6,20]. In this section a brief exposition of the sPGD is presented based on a more detailed work [19]. After discussing this basis, we will present the novel approach in Sect.…”

“…different procedures can be thought. Here, we employ that defined in references [12,19], where the methodology of the sPGD is deeply described. Following that reference, the following norm is used for the PGD residual in the present work:…”

“…Concretely, the methodology proposed in this work is a novel formulation of the sparse proper generalized decomposition (sPGD) [12,19], combining it with a reduced basis (RB) approach, which is used to fit correctly the ROM with the numerical simulations and to perform a further data compression. This procedure is described in Sect.…”

“…This methodology will allow us to achieve excellent results when dealing with high-dimensional spaces and sparse data. This technique is specially convenient because only sparse data is available when dealing with high-dimensional problems [12,19]. This allows us to cope with the curse of dimensionality.…”

Fast Computation of Multi-Parametric Electromagnetic Fields in Synchronous Machines by Using PGD-Based Fully Separated Representations

“…PGD was previously applied in electromagnetics, such as, for instance, the computation of impedance in industrial busbar systems [6], the analysis of magnetoelectric devices [7] or batteries [8]. However, the complex non-separable geometry of electric motors limited its use for efficiently expressing the solution by separating the space coordinates.…”