scholarly journals Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2138 ◽  
Author(s):  
Jinhyeong Park ◽  
Munsu Lee ◽  
Gunwoo Kim ◽  
Seongyun Park ◽  
Jonghoon Kim
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Storage System ◽  
Integrated Approach ◽  
Energy Storage System ◽  
Measured Data ◽  
Ar Model ◽  
Health Indicator ◽  
Practical Applications ◽  
Battery Capacity

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.

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

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 33
Author(s):  
Yuanyuan Chen ◽  
Zilong Yang ◽  
Yibo Wang
Keyword(s):  
Wavelet Transform ◽  
Energy Storage ◽  
Storage System ◽  
Estimation Method ◽  
Energy Storage System ◽  
Operating Conditions ◽  
Estimation Methods ◽  
Extend Kalman Filter ◽  
Practical Applications ◽  
Soc Estimation

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.

Addressing Technology Uncertainties in Battery Energy Storage Sizing Procedures

2017 ◽  
Vol 18 (2) ◽  
Author(s):  
Guido Carpinelli ◽  
Fabio Mottola ◽  
Daniela Proto
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Life Time ◽  
Energy Storage System ◽  
Point Of View ◽  
Time Duration ◽  
Battery Energy Storage ◽  
Unit Costs ◽  
Battery Capacity ◽  
Battery Energy

Abstract This paper analyzes the influence of technology uncertainties on the sizing of battery systems. The sizing is based on the minimization of the costs incurred by the end customer and is performed considering demand response applications in the frame of time of use tariffs. The randomness of i) battery round trip efficiency, ii) life time duration, iii) unit costs related to battery capacity, power conversion system, operation and maintenance and replacement is taken into account in order to identify the most convenient solution from an economic and technical point of view. Based on the load requests of actual industrial and residential loads, numerical applications have been performed. The results provided useful information regarding the influence uncertainties have in the choice of a battery energy storage system.

Design, Control and Monitoring of an Offline Mobile Battery Energy Storage System for a Typical Malaysian Household Load Using PLC

2018 ◽  
Vol 9 (1) ◽  
pp. 180
Author(s):  
Nabil Mohammed ◽  
Kumeresan A. Danapalasingam ◽  
Ahmed Majed
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Low Noise ◽  
Portable Devices ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Practical Applications ◽  
Uninterruptible Power ◽  
Battery Energy

Battery energy storage system (BESS) is used in many practical applications including uninterruptible power supplies (UPS), portable devices, electrical vehicles and renewable energy systems. To utilize BESS effectively, an efficient control operation is required. Various controllers have been introduced in the open literature. However, they are not considered the best fit due to their limitations. This includes their incapability of handling high-power rating BESS, low noise immunity, short life cycle and limited number of input and output interfaces. In this paper, the programmable logic controller (PLC) is used to control and monitor a 158.8 kWh offline BESS for a typical Malaysian household. TIA Portal V13 software by siemens is used to program the proposed PLC control. Human machine interface(HMI) system is used to monitor and simulate the control performance. The results show that the PLC approach provides an efficient and reliable control of the BESS in which a compact protection against the battery overcharging, under-discharging and overheating is achieved.

scholarly journals SOC Estimation of Lithium-Ion Battery Based on Kalman Filter Algorithm for Energy Storage System in Microgrids

2018 ◽  
Vol 57 ◽  
pp. 02006 ◽  
Author(s):  
Dae-Won Chung ◽  
Seung-Hak Yang
Keyword(s):  
Kalman Filter ◽  
Energy Storage ◽  
Power Systems ◽  
Lithium Ion Battery ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Internal States

State-of-charge (SOC) is one of the vital factors for the energy storage system (ESS) in the microgrid power systems to guarantee that a battery system is operating in a safe and reliable manner for the system. Many uncertainties and noises, such as nonlinearities in the internal states of a battery, sensor measurement accuracy and bias, temperature effects, calibration errors, and sensor failures, pose a challenge to the accurate estimation of SOC in most applications. This study makes two contributions to the existing literatures. First, a more accurate extended Kalman filter (EKF) algorithm is proposed to estimate the battery nonlinear dynamics. Due to its discrete form and ease of implementation, this straightforward approach could be more suitable for real applications on the ESS. Second, its order selection principle and parameter identification method are illustrated in detail. It can accurately demonstrate the characteristics of the lithium-ion battery to show the feasibility and effectiveness of the algorithm for the ESS.

Sensorless control of wind power generator with flywheel energy storage system

2019 ◽  
pp. 0309524X1988470
Author(s):  
L Hocine ◽  
M Menna ◽  
K Yazid
Keyword(s):  
Kalman Filter ◽  
Wind Power ◽  
Sensorless Control ◽  
Storage System ◽  
Induction Machine ◽  
Energy Storage System ◽  
Power Generator ◽  
Flywheel Energy Storage System ◽  
Wind Power Generator ◽  
Extended Complex

For the purpose of sensorless low-speed control of direct-drive permanent magnet synchronous generator–based wind power systems, a technique based on the rotating high-frequency voltage-signal injection is discussed in this article, which improves the efficiency of the method and eliminates the phase shift introduced by traditional filters; band pass and low-pass filters are replaced by an extended complex Kalman filter algorithm. In addition, a flywheel energy storage system based on a squirrel cage induction machine is connected with the wind power generator by a DC bus through two power converters. For sensorless control of the induction machine, a rotor speed estimation method based on the extended complex Kalman filter is established. Finally, network connection interface of the complete system is presented and the efficiency of the proposed sensorless control of the whole chain of wind power generation is shown by numerical simulations using MATLAB/Simulink environment.

Discussion of the Performance of VRB Based on Modeling by Using EKF to Estimate SOC

2012 ◽  
Vol 512-515 ◽  
pp. 1364-1370 ◽  
Author(s):  
Jin Ding Lu ◽  
Gang Sun ◽  
Yue Jin Tang ◽  
Li Ren ◽  
Jing Shi
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Output Voltage ◽  
Redox Flow Batteries ◽  
Battery Capacity ◽  
Flow Batteries ◽  
Operating Current ◽  
Vanadium Redox ◽  
Capacity Output

There are several ongoing researchers on searching for an appropriate model to describe the characteristic of Vanadium redox flow batteries(VRB) .Based on one of these models, a SOC estimator of VRB- Extended Kalman Filter (EKF) is advanced. And then, update the VRB model by using EKF to estimate SOC when simulation in SIMULINK. At last, the effects of the temperature and operating current on performance of VRB, including battery capacity, output voltage, efficiencies are discussed.

scholarly journals Optimization of Microgrid Battery Capacity using PSO with Considering Islanding Operation

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Ayu Sintianingrum, Khairudin, Lukmanul Hakim
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Pso Algorithm ◽  
Energy Storage System ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Battery Capacity ◽  
Effective System ◽  
Islanding Operation ◽  
Storage Technologies

Electrical is used for various activities in all sectors. Rapid increase of electricity demand recently, makes it necessary to have an even more efficient method for generating electricity. Renewable energy and the microgrid provides an integrated and alternative solution for electricity generation. In microgrid systems, energy storage devices are one of important aspects. Batteries are one kind of the energy storage technologies widely used in power system and hence, their suitable capacity must be determined in order to develop an effective system installation. In this research, sizing optimization of battery capacity is modeled as a minimization of microgrid battery capacity using the Particle Swarm Optimization/PSO algorithm with considering islanding operation of the system for effective battery installation. Results show that optimal battery capacity can be obtained and the developed computational model gives satisfactory results for the system under study.   Keywords: Battery, microgrid, energy storage system, PSO algorithm

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