A Digital Twin Model for the Battery Management Systems of Electric Vehicles

Li, Heng; Kaleem, Muaaz Bin; Chiu, I-Ju; Gao, Dianzhu; Huang, Zhiwu

doi:10.1109/hpcc-dss-smartcity-dependsys53884.2021.00171

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…Through this meta-model, an actual system can be created. The research work [63] proposes a digital twin paradigm for the BMS, shown in Figure 7, to estimate and anticipate battery conditions with only a voltage sensor. The battery's health needs to be monitored because it degrades over time.…”

Section: Electric Vehiclementioning

confidence: 99%

A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced

et al. 2023

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The concept of the digital twin has been adopted as an important aspect in digital transformation of power systems. Although the notion of the digital twin is not new, its adoption into the energy sector has been recent and has targeted increased operational efficiency. This paper is focused on addressing an important gap in the research literature reviewing the state of the art in utilization of digital twin technology in microgrids, an important component of power systems. A microgrid is a local power network that acts as a dependable island within bigger regional and national electricity networks, providing power without interruption even when the main grid is down. Microgrids are essential components of smart cities that are both resilient and sustainable, providing smart cities the opportunity to develop sustainable energy delivery systems. Due to the complexity of design, development and maintenance of a microgrid, an efficient simulation model with ability to handle the complexity and spatio-temporal nature is important. The digital twin technologies have the potential to address the above-mentioned requirements, providing an exact virtual model of the physical entity of the power system. The paper reviews the application of digital twins in a microgrid at electrical points where the microgrid connects or disconnects from the main distribution grid, that is, points of common coupling. Furthermore, potential applications of the digital twin in microgrids for better control, security and resilient operation and challenges faced are also discussed.

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Section: Electric Vehiclementioning

confidence: 99%

A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced

et al. 2023

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“…Using PyBaMM to develop a digital twin of a battery can be effective, especially for accurately updating parameters during battery cycles. In [34], the authors suggested a digital twin model for BMSs that can forecast battery characteristics such as the temperature, current, and state of charge by solely measuring voltage. They employed linear and multi-linear regression models to make predictions.…”

Section: Integration Of Digital Twins Into Bmssmentioning

confidence: 99%

Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach

Pooyandeh,

Sohn

2023

Mathematics

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This paper presents a transformative methodology that harnesses the power of digital twin (DT) technology for the advanced condition monitoring of lithium-ion batteries (LIBs) in electric vehicles (EVs). In contrast to conventional solutions, our approach eliminates the need to calibrate sensors or add additional hardware circuits. The digital replica works seamlessly alongside the embedded battery management system (BMS) in an EV, delivering real-time signals for monitoring. Our system is a significant step forward in ensuring the efficiency and sustainability of EVs, which play an essential role in reducing carbon emissions. A core innovation lies in the integration of the digital twin into the battery monitoring process, reshaping the landscape of energy storage and alternative power sources such as lithium-ion batteries. Our comprehensive system leverages a cloud-based IoT network and combines both physical and digital components to provide a holistic solution. The physical side encompasses offline modeling, where a long short-term memory (LSTM) algorithm trained with various learning rates (LRs) and optimized by three types of optimizers ensures precise state-of-charge (SOC) predictions. On the digital side, the digital twin takes center stage, enabling the real-time monitoring and prediction of battery activity. A particularly innovative aspect of our approach is the utilization of a time-series generative adversarial network (TS-GAN) to generate synthetic data that seamlessly complement the monitoring process. This pioneering use of a TS-GAN offers an effective solution to the challenge of limited real-time data availability, thus enhancing the system’s predictive capabilities. By seamlessly integrating these physical and digital elements, our system enables the precise analysis and prediction of battery behavior. This innovation—particularly the application of a TS-GAN for data generation—significantly contributes to optimizing battery performance, enhancing safety, and extending the longevity of lithium-ion batteries in EVs. Furthermore, the model developed in this research serves as a benchmark for future digital energy storage in lithium-ion batteries and comprehensive energy utilization. According to statistical tests, the model has a high level of precision. Its exceptional safety performance and reduced energy consumption offer promising prospects for sustainable and efficient energy solutions. This paper signifies a pivotal step towards realizing a cleaner and more sustainable future through advanced EV battery management.

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“…Recently, many studies have been conducted to apply digital twin to optimize manufacturing processes, including smart manufacturing, 29 and equipment control, such as CNC machine tools, 30,31 transformer equipment, 32 and cutting tools 33 . In addition, studies using digital twin have been performed on residual‐life prediction, 34,35 fatigue‐life prediction, 36,37,38 battery quality prediction, 39,40 and aircraft part quality control 41 …”

Section: Existing Studies On Digital Twinmentioning

confidence: 99%

Virtual qualification using digital twin for product development: A case study on liquid crystal display

Choi,

Ahn,

Yoo

et al. 2023

Quality & Reliability Eng

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A virtual qualification (VQ) model is proposed using a digital twin not only for verifying electrical properties of a liquid crystal display (LCD) but also for assessing the effect of environmental stresses. The digital twin is developed to reflect product specifications and distributions of manufacturing process data. The VQ model utilizes the digital twin to infer and verify the changes in electrical characteristic values in the internal circuit under a high‐temperature operating test. Additionally, a procedure is proposed for developing digital twins, and its effectiveness is demonstrated based on a case study of an LCD. The developed digital twin may also be used for similar LCD models for design verification.

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A Digital Twin Model for the Battery Management Systems of Electric Vehicles

Cited by 13 publications

References 15 publications

A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced

A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced

Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach

Virtual qualification using digital twin for product development: A case study on liquid crystal display

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