Design of power lithium battery management system based on digital twin

Tang, Hao; Wu, Yih‐Min; Cai, Yuanfeng; Fanyu, Wang; Lin, Zequn; Pei, Yanxi

doi:10.1016/j.est.2021.103679

Cited by 35 publications

(8 citation statements)

References 25 publications

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“…In the domain of vehicle components, we find that digital twins of batteries have very well defined (and often abbreviated) common purposes. Particularly, those are estimation of state of charge [24], [26], [30], [31], [34], [35], state of health [26], [30], [35], state of power [35] remaining useful life [23], [28], voltage [31], or parameter and state estimation in general [32]. Battery digital twins are also used to evaluate [27] and decelerate [28] performance degradation as well as "fulfill[ing] services for different stakeholders" [25].…”

Section: Diagnostic Applications Of Digital Twinsmentioning

confidence: 99%

A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation

Prikler,

Wotawa

2023

2023 10th International Conference on Dependable Systems and Their Applications (DSA)

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In recent years, digital twins have been proposed and implemented in various fields with potential applications ranging from prototyping to maintenance. Going forward, they are to enable numerous efficient and sustainable technologies, among them autonomous cars. However, despite a large body of research in many fields, academics have yet to agree on what exactly a digital twin is -and as a result, what its capabilities and limitations might be. To further our understanding, we explore the capabilities of digital twins concerning diagnosis in the field of transportation. We conduct a systematic mapping study including digital twins of vehicles and their components, as well as transportation infrastructure. We discovered that few papers on digital twins describe any diagnostic process. Furthermore, most existing approaches appear limited to system monitoring or fault detection. These findings suggest that we need more research for diagnostic reasoning utilizing digital twins.

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Section: Diagnostic Applications Of Digital Twinsmentioning

confidence: 99%

A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation

Prikler,

Wotawa

2023

2023 10th International Conference on Dependable Systems and Their Applications (DSA)

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“…A hybrid HIF-PF online technique is suggested for predicting SOC under the Beijing Bus Dynamic Stress Test (BBDST) experimental setting. That design is based on power lithium BMS to monitor and visualize real-time voltages and currents to estimate SOC in [23]. An EKF has been suggested for estimating the battery pack's parameters and SOC that has been utilized state-space model of an EV battery.…”

Section: Related Workmentioning

confidence: 99%

Prediction of the Battery State Using the Digital Twin Framework Based on the Battery Management System

Jafari

Byun

2022

IEEE Access

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Electric Vehicles (EVs) reliance on batteries, which currently have lower energy and power densities than liquid fuels and are prone to aging and performance degradation over time, restricts their mainstream adoption. With applications like electric vehicles and grid-scale energy storage, effective management of lithium-ion batteries is a vital enabler for a low-carbon future. Monitoring the battery's condition of health and charge over the lifetime of an EV is, therefore, a highly pertinent issue. Battery Management Systems (BMS) are used during the operation of EVs to monitor, estimate and control battery states to ensure that batteries can function effectively and safely. Additionally, the materials composition, system design, and operating circumstances substantially impact a battery's usable life, making it more challenging to govern and maintain battery systems. This work proposes the structure of a battery digital twin-based battery for the electronic vehicle, which has the potential to enhance BMS situational awareness greatly and enable the optimal functioning of battery storage units. Digitalization and Artificial Intelligence (AI) present an opportunity and offer a Digital Twin (DT) of the EV battery as a solution to the difficulty of onboard computation for the incremental State Of Health (SOH) and State Of Charge (SOC) based on Extreme Gradient Boost (XGBoost) and Extended Kalman Filter (EKF) to predict the state estimate. The battery's condition has been determined by using the EKF, which can provide vital information for maintenance. The battery's usable life can be extended with an accurate estimate of the SOC to continue then a learning-based prediction approach to gauge the battery's health state is suggested in order to increase battery life. A SOC model is frequently retrained to depict the effects of aging, and a SOH model is often performed to foretell the reduction in the highest battery capacity. According to a result, DT models are useful for managing batteries, and full life cycle statistics are important for planning the battery's upgrade path.

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“…Relying on the development of the existing IoT cloud technology, the background management system of the battery management system is built in the cloud, and using the mighty computing power of the cloud server for the prediction of relevant parameters is a future trend of the intellectual development of lithium battery management system [3] . For the audience of electric bicycles, it is also an idea to provide users with an APP-based management platform.…”

Section: Management Systemmentioning

confidence: 99%

The study of edge-end collaborative management system for lithium batteries

Yue

Dai

2022

Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022)

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For the management of an electric bicycle power battery pack, a lithium battery management system with a mobile Application and collection terminal is designed to realize real-time monitoring of single battery voltage, current, and temperature. A recursive least squares algorithm with a forgetting factor (FFRLS) is used on the Android app for online parameter identification to solve the balance between accuracy and real-time SOC estimation. An extended Kalman filter algorithm (EKF) based on the equivalent circuit model with real-time parameter update is used on the acquisition terminal for real-time SOC estimation. The above functions and SOC estimation methods were tested and validated using battery test equipment. The results show that the accuracy of the selected SOC estimation algorithm is significantly higher than that of the ampere-time integration method.

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Design of power lithium battery management system based on digital twin

Cited by 35 publications

References 25 publications

A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation

A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation

Prediction of the Battery State Using the Digital Twin Framework Based on the Battery Management System

The study of edge-end collaborative management system for lithium batteries

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