Digital twin for electric vehicle battery management with incremental learning

Eaty, Naga Durga Krishna Mohan; Bagade, Priyanka

doi:10.1016/j.eswa.2023.120444

Cited by 13 publications

(2 citation statements)

References 20 publications

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“…Event-driven ADCs [27] Primary, secondary architecture, FPGA centralized and decentralized architecture [53,54,67,69] Cell balancing, overvoltage protection, and thermal protection, liquid cooling, Charging/discharging control, fault diagnosis and detection, battery state estimation, thermal isolation, and battery pressure release [8,9,20,29,31,34,[54][55][56][57][58][59][60][61][62]77] Mitigating cyber attacks [66] Unmanned Aerial Vehicles (UAVs) [68] Blockchain, cloud computing, artificial intelligence, digital twins, vehicle-to-grid (V2G), big data [20,21,64,65,[71][72][73][74][75][76] Reconfiguration, self-reconfigurable multicell batteries [70,80] Demand response, demand-side management, grid management [3,78,81,83] Economic operation and security, energy arbitrage, battery operation cost minimization, minimizing power loss, battery scheduling, life cycle operating and storage cost optimization [79,82,[84][85]<...…”

Section: Bms Trends Workmentioning

confidence: 99%

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Battery Energy Storage Systems: A Review of Energy Management Systems and Health Metrics

Nazaralizadeh,

Banerjee,

Srivastava

et al. 2024

Energies

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With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics.

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Section: Bms Trends Workmentioning

confidence: 99%

“…Blockchain, cloud computing, artificial intelligence, and digital twins have been used to efficiently estimate battery chemical behavior using a BMS in [73][74][75]. In [76], the author presents a digital twin framework for EV batteries. The physical system has an on-vehicle BMS that gathers real-time data and transmits them to the Azure cloud.…”

mentioning

confidence: 99%

Battery Energy Storage Systems: A Review of Energy Management Systems and Health Metrics

Nazaralizadeh,

Banerjee,

Srivastava

et al. 2024

Energies

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Multiscale modeling for enhanced battery health analysis: Pathways to longevity

Yang,

Zhang,

Wang

et al. 2024

Carbon Neutralization

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The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.

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