SOC Estimation of Lead Carbon Batteries Based on the Operating Conditions of an Energy Storage System in a Microgrid System

Abstract

The environment for practical applications of an energy storage system (ESS) in a microgrid system is very harsh, and therefore actual operating conditions become complex and changeable. In addition, the signal of the ESS sampling process contains a great deal of system and measurement noise, the sampled current fluctuates significantly, and also has high frequency. In this case, under such conditions, it is difficult to accurately estimate the state of charge (SOC) of the batteries in the ESS by common estimation methods. Therefore, this study proposes a compound SOC estimation method based on wavelet transform. This algorithm is very suitable for microgrid systems with large current, frequent fluctuating conditions, and high noise interference. The experimental results and engineering data show that the relative error of the method is 0.5%, which is much lower than the extend Kalman filter (EKF) based on wavelet transform.

A novel streamlined particle‐unscented Kalman filtering method for the available energy prediction of lithium‐ion batteries considering the time‐varying temperature‐current influence

“…Consequently, the experiment is performed, in which the real-time parameters are recorded in real-time, including OCV, discharging current, discharging time, and so on. (5) Steps of ( 3) and ( 4) are repeated by and the experiments are performed at different SOC levels, respectively. (6) After performing the complete experiment, the battery is discharged with a 1 C current rate for 3 minutes to reduce the available energy value by 5.00%.…”

“…[1][2][3] The relaxation behavior of exotic lithium-ion batteries can be extracted to express its capacity decaying characteristics, including Dynamic Linear Modeling, Long Short-Term Memory (LSTM), Neural Network (NN), and Wide Operating Temperature Degradation methods. [4][5][6][7][8] Consequently, the robust adaptive Sliding Mode Observation algorithm is introduced into the effective and predictable correction stage as well as its diagnosis evaluation in advance.…”

“…The average circulating current has a great influence on the available energy status of lithium‐ion batteries, which can be predicted by using Dual Extended Kalman Filtering, Gaussian Markov Modeling, and Unscented Particle Filtering algorithms 1‐3 . The relaxation behavior of exotic lithium‐ion batteries can be extracted to express its capacity decaying characteristics, including Dynamic Linear Modeling, Long Short‐Term Memory (LSTM), Neural Network (NN), and Wide Operating Temperature Degradation methods 4‐8 . Consequently, the robust adaptive Sliding Mode Observation algorithm is introduced into the effective and predictable correction stage as well as its diagnosis evaluation in advance.…”

An improved smoothing factor-extended kalman filtering method for accurate online state-of-charge estimation of Lithium-ion battery

“…If the machine knows the state of charge, and thus changes its strategy to adjust the voltage, current, etc. [2,15], it can achieve the best-operating conditions and the longest life of Lithium-ion [12]. However, the Lithium-ion battery has a complicated internal structure, and the state of charge is affected by various factors such as temperature, self-aging degree, and external pressure [3], making it difficult to directly estimate the state of charge accurately .…”

“…The current estimation methods for Lithium-ion batteries are as follows: (1) Open circuit voltage method [8,11]: Although this method can measure the state of charge, the battery must be in a static state for more than 1 hour, and the battery is easily affected by the outside world, so the estimated state of charge will be different, so it is not suitable for real-time estimation. (2) Ampere-hour integration method [7,16]: The battery estimates the state of charge of the battery through accumulated charge and discharge. But the shortcomings are also obvious.…”