Abstract
To enhance the efficiency of an energy storage system, it is important to predict and estimate the battery state, including the state of charge (SOC) and state of health (SOH). In general, the statistical approaches for predicting the battery state depend on historical data measured via experiments. The statistical methods based on experimental data may not be suitable for practical applications. After reviewing the various methodologies for predicting the battery capacity without measured data, it is found that a joint estimator that estimates the SOC and SOH is needed to compensate for the data shortage. Therefore, this study proposes an integrated model in which the dual extended Kalman filter (DEKF) and autoregressive (AR) model are combined for predicting the SOH via a statistical model in cases where the amount of measured data is insufficient. The DEKF is advantageous for estimating the battery state in real-time and the AR model performs better for predicting the battery state using previous data. Because the DEKF has limited performance for capacity estimation, the multivariate AR model is employed and a health indicator is used to enhance the performance of the prediction model. The results of the multivariate AR model are significantly better than those obtained using a single variable. The mean absolute percentage errors are 1.45% and 0.5183%, respectively.
Highlights
Due to the Paris Agreement for preventing catastrophic climate change, the current energy system requires a rapid global shift toward decarbonization in all sectors, such as industry, transportation, and residential and commercial buildings, through the use of renewable energy [1]
Since the open-circuit voltage (OCV) is highly dependent on the state of charge (SOC) than the number of cycles, it is not suitable to define the OCV for health indicator (HI)
The capacity prediction result was compared with the measured capacity and the estimated capacity from the SOC and state of health (SOH) joint estimator (DEKF)
Summary
Due to the Paris Agreement for preventing catastrophic climate change, the current energy system requires a rapid global shift toward decarbonization in all sectors, such as industry, transportation, and residential and commercial buildings, through the use of renewable energy [1]. It is important to consider both the battery cost and the performance of the ESS over its lifespan [6]. Because improvements in battery performance can reduce operation and management costs, the importance of the battery management system (BMS) is gradually increasing to manage and monitor the battery state more efficiently. Accurate estimation and prediction of the state of charge (SOC) and the state of health (SOH) can ensure the BMS prolong the cycle life of energy management and reduce the utilization cost for replacing the battery [8]. Accurate capacity estimation is required for the efficient and safe operation of the battery [10]. Research on estimating the SOC and SOH, according to the variability of the battery characteristics, is crucial for developing an advanced BMS. The Kalman filter is advantageous in the sense that it has a very flexible coordinator to handle battery characteristic changes [8,10]
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