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首页> 外文期刊>Journal of power sources >A fractional-order model-based state estimation approach for lithium-ior battery and ultra-capacitor hybrid power source system considering load trajectory
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A fractional-order model-based state estimation approach for lithium-ior battery and ultra-capacitor hybrid power source system considering load trajectory

机译：基于分数阶模型的锂离子电池与超级电容器混合电源系统状态估计方法

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摘要

In recent years, hybrid energy storage systems have been widely used in electric vehicle and smart grid applications. Real-time and robust modeling and state estimation are essential to the reliable and safe operation of the hybrid energy storage system. Although there exists a considerable mass of research on the modeling and state estimation of the lithium-ion batteries, a survey focusing on the remaining discharge time prognostic for the hybrid power source system has not been conducted. To fill this gap, this paper handles the problem of fractional-order modeling and the remaining discharge time prognostic of the lithium-ion battery and ultra-capacitor hybrid energy storage system. First, the fractional-order models for the lithium-ion batteries and ultra-capacitors are presented, where the particle swarm optimization Algorithm with the Chaos theory is employed for parameter identification in the time domain. Second, a Markov load trajectory prediction is proposed for enhancing the reliability and robustness of the remaining discharge time prognostic. Third, the prognostic framework of the hybrid energy storage system is presented based on the Bayesian method. The results with urban dynamometer driving schedule are analyzed and discussed, which indicate that the proposed method has high accuracy and robustness.
机译：近年来，混合储能系统已广泛用于电动汽车和智能电网应用中。实时，鲁棒的建模和状态估计对于混合储能系统的可靠和安全运行至关重要。尽管在锂离子电池的建模和状态估计方面进行了大量研究，但尚未进行针对混合动力系统预后的剩余放电时间的调查。为了填补这一空白，本文解决了分数阶建模问题以及锂离子电池和超级电容器混合储能系统的剩余放电时间预测问题。首先，提出了锂离子电池和超级电容器的分数阶模型，其中采用混沌理论的粒子群优化算法进行时域参数识别。其次，提出了马尔可夫负荷轨迹预测，以提高预测剩余放电时间的可靠性和鲁棒性。第三，基于贝叶斯方法，提出了混合储能系统的预测框架。分析和讨论了城市测功机行驶时间表的结果，表明该方法具有较高的准确性和鲁棒性。

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