Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination
Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination
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DOI：
10.1016/j.apenergy.2013.05.001
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发表时间：
2013-11
期刊：
影响因子：
11.2
通讯作者：
W. Waag;D. Sauer
中科院分区：
文献类型：
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作者：
W. Waag;D. Sauer
当电池作为纯电动或混合能源系统的一部分使用时，电池状态和参数的在线估计是具有挑战性的任务之一。为了确定存储在电池中的可用能量，需要了解电池的当前荷电状态(SOC)和容量。对于SOC和容量的确定，通常使用电池电动势(EMF)的估计。电动势可以用电池的开路电压(OCV)来测量，当自电流中断以来经过了相当长的时间后。对于锂离子电池来说，这段时间可能需要几个小时，并且需要消除扩散过电压的影响。本文提出了一种新的估计电动势的方法，该方法只在电流中断后的第一分钟内考虑OCV松弛过程。该方法基于OCV松弛模型与测量的OCV松弛曲线的在线拟合。该模型基于一个由电压源(代表电动势)和一个恒定相位元件(CPE)串联而成的等效电路。在此基础上，确定了模型参数，并估算了电动势。将该方法应用于电动汽车锂离子电池的荷电状态和容量估计。在所给出的例子中，使用在两种不同荷电状态下估计的电动势来确定电池容量，最大误差为2%。通过在低成本的16位微控制器(英飞凌XC2287)上的实现，验证了该算法的实时性。
The online estimation of battery states and parameters is one of the challenging tasks when battery is used as a part of the pure electric or hybrid energy system. For the determination of the available energy stored in the battery, the knowledge of the present state-of-charge (SOC) and capacity of the battery is required. For SOC and capacity determination often the estimation of the battery electromotive force (EMF) is employed. The electromotive force can be measured as an open circuit voltage (OCV) of the battery when a significant time has elapsed since the current interruption. This time may take up to some hours for lithium-ion batteries and is needed to eliminate the influence of the diffusion overvoltages. This paper proposes a new approach to estimate the EMF by considering the OCV relaxation process within only some first minutes after the current interruption. The approach is based on an online fitting of an OCV relaxation model to the measured OCV relaxation curve. This model is based on an equivalent circuit consisting of a voltage source (represents the EMF) in series with the parallel connection of the resistance and a constant phase element (CPE). Based on this fitting the model parameters are determined and the EMF is estimated. The application of this method is exemplarily demonstrated for the state-of-charge and capacity estimation of the lithium-ion battery in an electrical vehicle. In the presented example the battery capacity is determined with the maximal inaccuracy of 2% using the EMF estimated at two different levels of state-of-charge. The real-time capability of the proposed algorithm is proven by its implementation on a low-cost 16-bit microcontroller (Infineon XC2287).