Cloud-Based Battery Digital Twin Middleware Using Model-Based Development

Merkle, Lukas

doi:10.1145/3386164.3387296

Cited by 1 publication

(1 citation statement)

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“…With the use of cloud-connected batteries, more and larger training datasets could be available in the future. Studies already show how the data is transmitted to the cloud and create a digital twin for battery health analysis [93,94]. Providing the datasets to the public will help in fitting data-driven aging models for system simulations.…”

Section: Data-driven Modelsmentioning

confidence: 99%

A Review on Aging-Aware System Simulation for Plug-In Hybrids

Frambach

Liedtke

Dechent

et al. 2022

IEEE Trans. Transp. Electrific.

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The lithium-ion battery is a vital powertrain component in plug-in hybrid electric vehicles (PHEVs). The fuel reduction potential and cost-effectiveness of these vehicles depend on the sizing of the powertrain components as well as on their utilization, which is defined by the energy management system (EMS). The battery is affected by power and capacity reduction over the lifetime of the vehicle, which needs to be considered during the design process to ensure the performance goals throughout the vehicle lifetime. Current literature regarding battery aging usually contains experimental results, which are not transformed into a useful aging model for system simulations. Consequently, battery aging is often neglected, which is why this paper intends to help researchers understand the degradation process of batteries in PHEVs and consider this in their simulation and dimensioning process. First, PHEV powertrain topologies and components are presented. Afterward, battery degradation mechanisms and recent findings are explained, followed by appropriate modeling approaches for different simulation targets. Finally, current aging-aware EMS literature is systematically reviewed and the integration of the aging models is analyzed, so researchers in system simulation areas can improve their powertrain models.

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Section: Data-driven Modelsmentioning

confidence: 99%