Implementation of Battery Digital Twin: Approach, Functionalities and Benefits

Abstract

The concept of Digital Twin (DT) is widely explored in literature for different application fields because it promises to reduce design time, enable design and operation optimization, improve after-sales services and reduce overall expenses. While the perceived benefits strongly encourage the use of DT, in the battery industry a consistent implementation approach and quantitative assessment of adapting a battery DT is missing. This paper is a part of an ongoing study that investigates the DT functionalities and quantifies the DT-attributes across the life cycles phases of a battery system. The critical question is whether battery DT is a practical and realistic solution to meeting the growing challenges of the battery industry, such as degradation evaluation, usage optimization, manufacturing inconsistencies or second-life application possibility. Within the scope of this paper, a consistent approach of DT implementation for battery cells is presented, and the main functions of the approach are tested on a Doyle-Fuller-Newman model. In essence, a battery DT can offer improved representation, performance estimation, and behavioral predictions based on real-world data along with the integration of battery life cycle attributes. Hence, this paper identifies the efforts for implementing a battery DT and provides the quantification attribute for future academic or industrial research.

Thermal Behavior of Lithium- and Sodium-Ion Batteries: A Review on Heat Generation, Battery Degradation, Thermal Runway – Perspective and Future Directions

“…Technologies such as cloud computing, artificial intelligence, big data, and others are employed for real-time data collection. Works by Wang et al 72 and Singh et al 73 throw more light on the application of digital twins and their implementation approach in battery systems, respectively. These methods are computationally less intense but require large experimental data for accurate modeling.…”

Overview of Digital Twin Platforms for EV Applications

“…The battery management system (BMS) is defined as the core element of a battery that monitors, protects, and ensures reliability, safety, and efficiency [ 39 ]. It is fundamental to point out that some indicators play a fundamental role in the successful BESS implementation, such as the state of charge (SOC), state of health (SOH), depth of charge (DOC), and depth of discharge (DOD).…”

“…Future research that identifies the efforts to implement a BESS for DT was also proposed by Singh et al in 2021, highlighting software packages in Python and MATLAB. The most important benefits of the DT and the integrated BMS in the scientific study conducted by Singh et al were the following [ 39 ]: (1) evaluation of the battery performance, (2) aging indicators to predict the remaining useful lifetime (RUL), (3) optimal assessment of the SOC, (4) thermal management, and (5) fault diagnostics.…”

Digital Twin Enables Rational Design of Ultrahigh‐Power Lithium‐Ion Batteries

“…Compared with normal theoretical calculation and simulation methods, digital twin technique usually includes interaction between physical and virtual models, and uses the virtual model to replicate, analyze, predict and optimize the physical system. [30,31] In the field of battery research, digital twin technique has widely been applied in the estimation of battery state of charge, capacity, and resistance based on the interaction with real-time data. [31][32][33][34] However, very few publications have adopted digital twin technique for design and optimization of LIBs, [35,36] much less in high power LIBs.…”

“…[30,31] In the field of battery research, digital twin technique has widely been applied in the estimation of battery state of charge, capacity, and resistance based on the interaction with real-time data. [31][32][33][34] However, very few publications have adopted digital twin technique for design and optimization of LIBs, [35,36] much less in high power LIBs.…”