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Real-Time Model-Based Estimation of SOC and SOH for Energy Storage Systems

机译:基于实时模型的储能系统SOC和SOH估计

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To obtain a full exploitation of battery potential in energy storage applications, an accurate modeling of electrochemical batteries is needed. In real terms, an accurate knowledge of state of charge (SOC) and state of health (SOH) of the battery pack is needed to allow a precise design of the control algorithms for energy storage systems (ESSs). Initially, a review of effective methods for SOC and SOH assessment has been performed with the aim to analyze pros and cons of standard methods. Then, as the tradeoff between accuracy and complexity of the model is the major concern, a novel technique for SOC and SOH estimation has been proposed. It is based on the development of a battery circuit model and on a procedure for setting the model parameters. Such a procedure performs a real-time comparison between measured and calculated values of the battery voltage while a PI-based observer is used to provide the SOC and SOH actual values. This ensures a good accuracy in a wide range of operating conditions. Moreover, a simple start-up identification process is required based on battery data-sheet exploitation. Because of the low computational burden of the whole algorithm, it can be easily implemented in low-cost control units. An experimental comparison between SOC and SOH estimation performed by suggested and standard methods is able to confirm the consistency of the proposed approach.
机译:为了在能量存储应用中充分利用电池潜力,需要对电化学电池进行精确建模。实际上,需要精确了解电池组的充电状态(SOC)和健康状态(SOH),才能对能量存储系统(ESS)的控制算法进行精确设计。最初,对SOC和SOH评估的有效方法进行了综述,旨在分析标准方法的利弊。然后,由于模型精度和复杂度之间的折衷是主要关注的问题,因此提出了一种用于SOC和SOH估计的新技术。它基于电池电路模型的开发以及用于设置模型参数的过程。当使用基于PI的观察器提供SOC和SOH实际值时,此过程将在电池电压的测量值和计算值之间进行实时比较。这样可确保在广泛的工作条件下保持良好的精度。此外,基于电池数据表的开发,需要一个简单的启动识别过程。由于整个算法的计算量较小,因此可以在低成本控制单元中轻松实现。通过建议方法和标准方法进行的SOC和SOH估计之间的实验比较能够确认所提出方法的一致性。

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