13 research outputs found
Artificial Neural Network based Battery Management System on State of Charge Estimation for Optimal Operation of Photovoltaic-Battery Integrated System
Due to the reduction of fossil-fuel utilization PV-Battery integrated system is a preferable power supply in many areas of the world. Designing a supervisory controller that can harvest high energy density and prolong the battery lifetime is one of the major challenges in a battery energy storage system. A proper Battery Management System (BMS) monitors the battery charge status and takes decision to lengthen the battery lifetime. A regulatory State of Charge (SOC) estimation based on PV-Battery standalone system is presented in this research that significantly addresses the issues. The proposed control algorithm estimates SOC by Backpropagation Neural Network (BPNN) scheme and implements Maximum Power Point Tracking (MPPT) system of the solar panels to take decision for charging, discharging or islanding mode of the Lead-Acid battery bank. The proposed model is designed in MATLAB/SIMULINK software and the experimental prototype is assessed via dSPACE 1104 component. The proposed power control strategy is explored as robust as well as attained the effective objective of standalone PV-Battery Management System e.g. avoiding overcharging and deep-discharging manoeuvre under different solar radiations and temperatures. A case study is presented for several SOC estimation methodologies that demonstrate the effectiveness of the proposed strategy with 0.082% error
Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System
: Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an
imbalance issue. Such imbalance has severe consequences on the battery activation function and the
maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing
technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of
charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized
to check the charge condition of the storage system. The developed battery management system
(BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC
Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and
experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the
proposed approac
Recent Progress and Future Trends on State of Charge Estimation Methods to Improve Battery-Storage Efficiency : A Review
Battery storage systems are subject to frequent charging/discharging cycles which reduce the operational life of the battery and reduce system reliability on the long run. As such, several Battery Management Systems (BMS) have been developed to maintain system reliability and extend the battery's operative life. Accurate estimation of the battery State of Charge (SOC) is a key challenge in the BMS due to its non-linear characteristics. This paper presents a comprehensive review on the most recent classifications and mathematical models of the SOC estimation. Future trends of the SOC estimation methods are also presented
An intelligent controlling method for battery lifetime increment using state of charge estimation in PV-battery hybrid system
In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly
Cost-Effective Design of IoT-Based Smart Household Distribution System
The Internet of Things (IoT) plays an indispensable role in present-day household electricity
management. Nevertheless, practical development of cost-effective intelligent condition monitoring,
protection, and control techniques for household distribution systems is still a challenging task. This
paper is taking one step forward into a practical implementation of such techniques by developing an
IoT Smart Household Distribution Board (ISHDB) to monitor and control various household smart
appliances. The main function of the developed ISHDB is collecting and storing voltage, current,
and power data and presenting them in a user-friendly way. The performance of the developed
system is investigated under various residential electrical loads of different energy consumption
profiles. In this regard, an Arduino-based working prototype is employed to gather the collected data
into the ThingSpeak cloud through a Wi-Fi medium. Blynk mobile application is also implemented
to facilitate real-time monitoring by individual consumers. Microprocessor technology is adopted
to automate the process, and reduce hardware size and cost. Experimental results show that the
developed system can be used effectively for real-time home energy management. It can also be used
to detect any abnormal performance of the electrical appliances in real-time through monitoring
their individual current and voltage waveforms. A comparison of the developed system and other
existing techniques reveals the superiority of the proposed method in terms of the implementation
cost and execution time
An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System
In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging
and discharging cycles that reduces its operational life and affects its performance considerably.
As such, an intelligent power control approach for a PV-battery standalone system is proposed
in this paper to improve the reliability of the battery along its operational life. The proposed
control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and
battery management system (BMS) mode. The novel controller tracks and harvests the maximum
available power from the solar cells under different atmospheric conditions via MPPT scheme. On the
other hand, the state of charge (SOC) estimation technique is developed using backpropagation
neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and
deep-discharging disturbances significantl
A peer-to-peer blockchain based interconnected power system
Utilities produce and supply products following local requirements and with the synchronizations
which connect subscribers. Harmonics is a power efficiency/quality variable caused by electronic
devices that domestic and industrial consumers use. The famous IEEE Standard 519 is maintained to
calculate harmonic limits, which ensures power efficiency. In a standard power system, currents and
voltages generate pure sine wave signals during regular operations. As harmonics influence the power
system, they cause interference in the sine wave signals. So, the best practice method should be used to
resolve the harmonics issue. One of the problem-solving techniques of harmonics is the measurement
and reduction of harmonics detection, and it uses Fast Fourier Transform (FFT). Therefore, power output
should assess in a peer-to-peer Blockchain scheme by measuring and minimizing harmonics detection.
This paper uses a Shunt Active Power Filter (SHAPF). It describes the simulation analysis and reduction
of harmonics detection in a peer-to-peer interconnected 3-phase power system with the help of an
FFT algorithm. This research was carried out to assess the efficiency of the AC signal by collecting,
processing, and evaluating power data. Using the shunt active filter, the proposed design outperformed
the traditional methods for both six and twelve pulse rectifiers, achieving total harmonic distortion
(THD) of only 1.42% and 0.92%, respectively
Monitoring of renewable energy systems by IoT‐aided SCADA system
With the rapid increase of renewable energy generation worldwide, real‐time
information has become essential to manage such assets, especially for systems
installed offshore and in remote areas. To date, there is no cost‐effective
condition monitoring technique that can assess the state of renewable energy
sources in real‐time and provide suitable asset management decisions to
optimize the utilization of such valuable assets and avoid any full or partial
blackout due to unexpected faults. Based on the Internet of Things scheme,
this paper represents a new application for the Supervisory Control and Data
Acquisition (SCADA) system to monitor a hybrid system comprising
photovoltaic, wind, and battery energy storage systems. Electrical parameters
such as voltage, current, and power are monitored in real‐time via the
ThingSpeak website. Network operators can control components of the hybrid
power system remotely by the proposed SCADA system. The SCADA system is
interfaced with the Matlab/Simulink software tool through KEPServerEX
client. For cost‐effective design, low‐cost electronic components and Arduino
Integrated Development Environment ATMega2560 remote terminal unit are
employed to develop a hardware prototype for experimental analysis.
Simulation and experimental results attest to the feasibility of the proposed
system. Compared with other existing techniques, the developed system
features advantages in terms of reliability and cost‐effectivenes
An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System
In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly
Active cell balancing control strategy for parallelly connected LiFePO4 batteries
© 2015 CSEE. While several recent studies have focused on eliminating the imbalance of energy stored in series-connected battery cells, very little attention has been given to balancing the energy stored in parallel-connected battery cells. As such, this paper aims at presenting a new balancing approach for parallel LiFePO4 battery cells. In this regard, a Backpropagation Neural Network (BPNN) based technique is employed to develop a Battery Management System (BMS) that can assess the charging status of all cells and control its operations through a DC/DC Buck-Boost converter. Simulation results demonstrate the effectiveness of the proposed approach in balancing the energy stored in parallel-connected battery cells in which the state of charge (SoC) estimation error is found to be only 1.15%