Comparison of State-of-Charge (SOC) estimation performance based on three popular methods: Coulomb counting, open circuit voltage, and Kalman filter

This paper introduces an energy management strategy for an off-grid hybrid energy system. The hybrid system consists of a photovoltaic (PV) module, a LiFePO4 battery pack coupled with a Battery Management System (BMS), a hybrid solar inverter, and a load management control unit. A Long Short-Term Memory network (LSTM)-based forecasting strategy is implemented to predict the available PV and battery power. The learning data are extracted from an African country with a tropical climate, which is very suitable for PV power applications. Using LSTM as a prediction method significantly increases the efficiency of the forecasting. The main objective of the proposed strategy is to control the different loads according to the forecasted energy availability of the system and the forecasted battery state of charge (SOC). The proposed management algorithm and the system are tested using Matlab/Simulink software. A comparative study demonstrates that the reduction in the energy deficit of the system is approximately 53% compared to the system without load management. In addition to this, the reliability of the system is improved as the loss of power supply probability (LPSP) decreases from 5% to 3%.

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“…The instant power of the battery can be expressed as [ 10 , 22 ]:

where

is the instant load power. The battery state of charge (SOC) can be expressed as [ 23 , 24 ]:

where

is the nominal power capacity (Wh) of the battery.…”

Section: Methodsmentioning

confidence: 99%

Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System

Alnejaili

Labdai

Chrifi‐Alaoui

2021

Sensors

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“…The MGCC sets the value of SOC re f at its maximum possible value (SOC ref = 100%) at the time interval t final − t initial with a constant power P BESS ref = Equation (36) to charge the batteries. In this case, the BESS can fulfill the DIN41773 charge procedure.…”

Section: Pv = Onmentioning

confidence: 99%

Practical Analysis and Design of a Battery Management System for a Grid-Connected DC Microgrid for the Reduction of the Tariff Cost and Battery Life Maximization

et al. 2018

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This study is focused on two areas: the design of a Battery Energy Storage System (BESS) for a grid-connected DC Microgrid and the power management of that microgrid. The power management is performed by a Microgrid Central Controller (MGCC). A Microgrid operator provides daily information to the MGCC about the photovoltaic generation profile, the load demand profile, and the real-time prices of the electricity in order to plan the power interchange between the BESS and the main grid, establishing the desired state of charge (SOC) of the batteries at any time. The main goals of the power management strategy under study are to minimize the cost of the electricity that is imported from the grid and to maximize battery life by means of an adequate charging procedure, which sets the charging rate as a function of the MG state. Experimental and simulation results in many realistic scenarios demonstrate that the proposed methodology achieves a proper power management of the DC microgrid.

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“…Various methods, models and algorithms for SOC estimation have been proposed and developed over the years, with each having its own advantages and disadvantages [3]. Among all, the coulomb counting method is the most used method for SOC estimation [14]. This is because it is relatively easy to implement and requires relatively low computational resources [8].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Coulomb Counting Method for Estimating the State of Charge of Lithium-Ion Battery

Ndeche

Ezeonu

2021

PSIJ

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An accurate estimate of the state of charge, which describes the remaining percentage of a battery’s capacity, has been an important and ever existing problem since the invention of the electrochemical cell. State of charge estimation is one of the important function of a battery management system which ensures the safe, efficient and reliable operation of a battery. In this paper, the coulomb counting method is implemented for the estimation of the state of charge of lithium-ion battery. The hardware comprises an Arduino based platform for control and data processing, and a 16-bit analog to digital converter for current and voltage measurement. The embedded algorithm initializes with a self-calibration phase, during which the battery capacity, coulombic efficiency and initial state of charge are evaluated. The initial state of charge is determined at the fully charged state (100% state of charge) or the fully discharged state (0% state of charge). The cumulative error of this method was addressed by routine recalibration of the capacity, coulombic efficiency and state of charge at the fully-charged and fully-discharged states. The algorithm was validated by charging/discharging a lithium-ion battery through fifty complete cycles and evaluating the error in the estimated state of charge. The result shows a mean absolute error of 0.35% in the estimated state of charge during the test. Further analysis, considering prolonged battery operation without parameters recalibration, suggests that error in the coulombic efficiency term contributes the most to the increasing error in the estimated state of charge with each cycle.

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Comparison of State-of-Charge (SOC) estimation performance based on three popular methods: Coulomb counting, open circuit voltage, and Kalman filter

Cited by 37 publications

References 13 publications

Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System

Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System

Practical Analysis and Design of a Battery Management System for a Grid-Connected DC Microgrid for the Reduction of the Tariff Cost and Battery Life Maximization

Implementation of Coulomb Counting Method for Estimating the State of Charge of Lithium-Ion Battery

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