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Review of adaptive systems for lithium batteries State-of-Charge and State-of-Health estimation

机译:思考锂电池自适应系统的抵制和健康状态估算

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High energy battery systems have recently appeared as an alternative Internal-Conbustion-Engine (ICE) based vehicle's powertrains. As a conquence, over the last few years, automotive manufacturers focused their research on electrochemical storage for electric (EV) and hybrid electric vehicles (HEV). In a lot of hybrid or electric applications, Lithium based batteries are used. To protect Lithium batteries and optimize their utilisation, a good State-of-Charge determiation is necessary. So three adaptive system used in the literature are presented in this article, the Kalman Filter, the Artificial Neural Network and the Fuzzy Logic systems.
机译:高能量电池系统最近出现为基于替代的内部混淆 - 引擎(ICE)的车辆的动力传动系统。作为征服,在过去几年中,汽车制造商的重点是电化学储存研究电化(EV)和混合动力电动车(HEV)。在许多混合或电应用中,使用锂基电池。为了保护锂电池并优化它们的利用,需要良好的充电质量确定。因此,在本文中介绍了文献中使用的三种自适应系统,卡尔曼滤波器,人工神经网络和模糊逻辑系统。

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