Electric Vehicle Battery Management using Digital Twin

Naga Durga Krishna Mohan Eaty, P. Bagade
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Abstract

In the transportation business, battery-powered electric vehicles (EVs) are regarded the immediate solution to internal combustion engines in light of the growth in environmental pollution. While expanding the use of electric vehicles, battery-related difficulties such as range anxiety, safety concerns, cost, and the availability of charging stations are important concerns. A precise online estimate of the battery’s State of Health (SoH) has the ability to resolve some of these issues. However, computing the SoH on EVs is computationally intensive, necessitating expensive onboard integrated electronics and rapidly draining the EV battery. In addition, the SoH estimating algorithms currently available do not utilise incremental battery usage data. This research presents a digital twin of the EV battery as a solution to the difficulty of onboard computation for incremental SoH prediction. It enables intensive computing and analytics to be performed in the cloud instead of a vehicle’s battery management system (BMS). It calculates the SoH of the battery using an incremental learning method with a mean square error of 0.023.
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基于数字孪生的电动汽车电池管理
在交通运输行业,电池驱动的电动汽车(ev)被认为是解决日益严重的环境污染的内燃机的直接解决方案。在扩大电动汽车的使用范围的同时,电池相关的问题,如里程焦虑、安全问题、成本和充电站的可用性是重要的问题。对电池健康状态(SoH)的精确在线估计能够解决其中的一些问题。然而,在电动汽车上计算SoH是计算密集型的,需要昂贵的车载集成电子设备,并且会迅速耗尽电动汽车电池。此外,目前可用的SoH估计算法没有利用增量电池使用数据。本研究提出了一种电动汽车电池的数字孪生体,以解决车载计算增量SoH预测的困难。它可以在云端执行密集的计算和分析,而不是在汽车电池管理系统(BMS)中执行。采用增量学习法计算电池的SoH,均方误差为0.023。
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