Researchers Develop New Way to Determine Amount of Charge Remaining in Battery

来自北卡罗来纳州立大学的研究人员开发了一种新技术，允许用户更好地确定电池中剩余的充电量。这对电动车司机来说是个好消息，因为它让他们更好地想到他们的汽车可能用完果汁。

The research is also good news for battery developers. “This improved accuracy will also give us additional insight into the dynamics of the battery, which we can use to develop techniques that will lead to more efficient battery management,” says Dr. Mo-Yuen Chow, a professor of electrical and computer engineering at NC State and co-author of the paper. “This will not only extend the life of the charge in the battery, but extend the functional life of the battery itself.”

At present, it is difficult to determine how much charge a battery has left. Existing computer models for estimating the remaining charge are not very accurate. The inaccuracy stems, in part, from the number of variables that must be plugged in to the models. For example, the capacity of a battery to hold a charge declines with use, so a battery’s history is a factor. Other factors include temperature and the rate at which a battery is charged, among many others.

现有的模型只允许这些变量的数据to be plugged in to the model once. Because these variables – such as temperature – are constantly changing, the models can become increasingly inaccurate.

但是现在研究人员开发了识别和处理可用于实时更新计算机模型的数据的软件，允许模型更准确地估计电池中的剩余电荷。虽然该技术是专门用于电池中的电池的电池，但该方法也适用于任何其他应用中的电池。

Using the new technique, models are able to estimate remaining charge within 5 percent. In other words, if a model using the new technique estimates a battery’s state of charge at 48 percent, the real state of charge would be between 43 and 53 percent (5 percent above or below the estimate).

The paper, “适应性参数识别和锂离子电池的充电状态估计,” will be presented at the 38th Annual Conference of the IEEE Industrial Electronics Society in Montreal, Oct. 25-28. Lead author of the paper is Habiballah Rahimi-Eichi, a Ph.D. student at NC State. The research was supported by the National Science Foundation, in collaboration with the foundation’s Engineering Research Center for Future Renewable Electric Energy Delivery and Management, which is based at NC State.

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Note to Editors:The presentation abstract follows.

“Adaptive Parameter Identification and State-of-Charge Estimation of Lithium-Ion Batteries”

Authors：北卡罗来纳州立大学沼泽地哈希米 - Eichi，莫根省

Presented：10月25日至28日，IEEE工业电子社会第38届会议，加拿大蒙特利尔

Abstract:Estimation of the State of Charge (SOC) is a fundamental need for the battery, which is the most important energy storage in Electric Vehicles (EVs) and the Smart Grid. Regarding those applications, the SOC estimation algorithm is expected to be accurate and easy to implement. In this paper, after considering a resistor-capacitor (RC) circuit-equivalent model for the battery, the nonlinear relationship between the Open Circuit Voltage (VOC) and the SOC is described in a lookup table obtained from experimental tests. Assuming piecewise linearity for the VOC -SOC curve in small time steps, a parameter identification technique is applied to the real current and voltage data to estimate and update the parameters of the battery at each step. Subsequently, a reduced-order linear observer is designed for this continuously updating model to estimate the SOC as one of the states of the battery system. In designing the observer, a mixture of Coulomb counting and VOC algorithm is combined with the adaptive parameter-updating approach and increases the accuracy to less than 5% error. This paper also investigates the correlation between the SOC estimation error and the observability criterion for the battery model, which is directly related to the slope of the VOC- SOC curve.