Irsyad Nashirul Haq

In this research, we developed an active cell balancing system to improve battery module performance. The active cell balancing system is based on Switched-Capacitor Boost Converter (S-CBC) method which consists of capacitors and DC-DC boost converters. The developed active cell balancing system will select the highest battery cell energy then transferred it to the lowest cell energy. From the analytical result, the optimum energy transferred in the active cell balancing system was using 1.1 F capacitor capacity, 5 Ohm resistance, and 5 Volt of the capacitor upper-lower voltage difference which shows the 0.5 Hz frequency and 50% duty cycle. The active cell balancing system experiment was conducted in two stages using valve regulated lead acid (VLRA) batteries with the nominal voltage 12 Volt and the nominal capacity 7 Ah. The first experiment was conducted by comparing the performance of the active cell balancing system with a conventional switched capacitor circuit in 2 battery series connection which shows that the developed system was 4.6 times faster than the conventional circuit at 0.5 Hz frequency with 81% efficiency. The second experiment was conducted to investigate the battery module performance when discharging process occurred with 1 A applied to 4 battery series connection. The result shows that the voltage differences between battery voltage differences without using cell balancing shows above 0.2 V and continues to expand to 0.8 V and by using the developed system the voltage difference were able to achieved below 0.1 Volt. It also shows 7.5 % performance improvement of the average battery system capacity and energy.

In this work, we developed the state of energy (SOE) estimation method for battery module while taking into account the unbalance voltage between cells and energy efficiency by using energy counting and support vector machine (SVM). The energy counting is performed by accumulating power coming in and out of the battery module when discharging or charging process occurs. The power is accumulated over time and compared to its nominal energy to obtain the SOE of the battery module. The experiment was conducted in two stages by utilizing Lithium Nickel Cobalt Aluminium Oxide (LiNiCoAlO2) batteries with the nominal voltage 3.6 V and the nominal capacity 3350 mAh, a DC power supply model, a programmable DC electronic load, and sensors for battery monitoring and protection system. In the first experiment, the energy counting is performed on a single cell to obtain individual battery cell characteristics based on C5, C10, and C20 discharging methods. The analytical results show that the relationship energy efficiency of charging and discharging process to discharge rate was 0.8974 x C(exp(-0.033)). The dataset from one battery cell characteristics is arranged into a lookup table that expresses the relationship between voltage, current and SOE of the battery. The lookup table was used as training datasets for SVM to generate model for estimating battery cell and module SOE. The second experiment was conducted to estimate the SOE of battery module consisting of 10 battery cells in series connection with the same discharging method as the single cell experiment. The SOE estimation results by using radial basis function based support vector regression model, with cost function value of 30, and an epsilon value of 0.04, and with kernel parameter value of 1, give the average of root-mean-squared-error value was below 5% which show an acceptable result. Because of cell unbalance will occur continuously during the battery charging and discharging process, the lowest SOE value from battery cells is selected as the reference value for the next cycle's process estimation.

In this work, we develop battery thermal management system for high capacity lithium-ion module by using air cooling method to minimize the differences of battery cell's temperature by comparing the parasitic energy consumed by the cooling fan system. The battery thermal management system utilizes two brushless fans, an evaporator unit, an insulation box, a programmable logic control (PLC) and sensors to monitor the operational and the performance of the battery cells. The battery module consists of four high capacity lithium iron manganese phosphate (LiFeMnPO4) battery cells with the nominal voltage of 3.2V and the nominal capacity of 100Ah which are connected in series. To get the initial battery cells thermal characteristic in time series, C5, C10, and C20 discharging methods have been conducted to the battery module using programmable load controller. Based on these thermal characteristics, we demonstrate thermal management system using active control of air cooling system to reduce the temperature rises and temperature differences between battery cells. The results show that the implemented battery thermal management system affects the amount of energy that can be utilized from the battery module. In the C5 discharging process, by using 40 % of fan speed control, the amount of energy that can be drawn from battery module was 3044 kJ including 19.3 kJ as the parasitic energy, and the highest battery cell temperature differences was 3.9 Celsius. This amount of energy was 5.1% higher than that of without the thermal management system.

— Battery has an important role as energy storage in electricity system utilization such as in electric vehicle and in smart microgrid system. Battery Management System (BMS) is needed to treat the dynamics of energy storage process in the battery in order to improve the performance and extend the life time of battery. In this paper, BMS cell monitoring and protection has been designed and tested for Lithium Ferro Phosphate (LFP) battery cells. The BMS cell monitoring function has been able to measure the battery parameters such as the voltage and current dynamics of each cell. The data taken from the BMS cell monitoring experiment is used to estimate the state of charge (SOC) of battery which is based on coulomb counting with coulomb efficiency ratios. The BMS cell monitoring function has successfully demonstrated the presence of unbalanced cell voltages during both processes of charging and discharging as well. From the analysis, the existence of capacity and energy fades was also investigated for every discharging and charging cycles. Based on the BMS cell protection experiment results, overcharged and over discharged protections have successfully been demonstrated for the battery cells. The charging process is disabled when the voltage of the corresponding battery cell exceeds its high limit (HLIM) at 3.65V, and the battery will be available for charging when all of the cell voltages are below their boundary limits (CAVL) at 3.3V. The discharging process will be disabled when the battery cell voltage is lower than the corresponding low limit (LLIM) at 2.5 V. The battery will be available again when all battery cell voltages are above their discharge available (DAVL) voltage at 2.8V. The proposed BMS cell monitoring and protection has shown its function as a data acquisition system, safety protection, ability to determine and predict the state of charge of the battery, and ability to control the battery charging and discharging.

— In this research work, we demonstrate state-of-charge (SoC) estimation using support vector regression (SVR) approach for a high capacity Lithium Ferro Phosphate (LiFePO 4) battery module. The proposed SoC estimator in this work is extracted from open circuit voltage (OCV)-SoC lookup table which is obtained from the battery module discharging and charging testing cycles, using voltage and current as independent variables. The SoC estimation based on SVR gives a perfectly linear curve fitting with its reference within the range of 37.5% to 90% while the rest hysteresis due to the discharging and charging process is compensated using OCV-SoC curve as the training data set. The SVR estimates the battery module SoC with RMSE of 2.3% over the whole test and the maximum positive and negative error is 4%, which means that it shows good accuracy. Keywords— State of Charge, Support Vector Regression, Coulomb Counting, Open Circuit Voltage, Lithium Ferro Phosphate Battery Module.

Indonesia is an archipelago country. Country development tends to be uneven due to many remote areas. Remote areas are often the ideal place to exploit the use of solar energy sources. Source of solar energy for electrical energy has very important role for development because the cost of conventional electricity supply for remote area is very high, e.g. for transportation, distribution, operations and maintenance cost. The use of solar energy especially Solar Power Plant (SPP), offers long-term benefits such as lower operating costs and reducing environmental pollution.

SPP monitoring in remote areas is very important in order to guarantee the condition of reliable operation. Monitoring by visiting the area are impractical and expensive, therefore monitoring with a wireless data transmission and web-based applications is an appropriate and efficient way.

Operating conditions and performance of SPP depend on system configuration and weather conditions. The main characteristics of an SPP in addition to the operating current solar module (Ia), and the operating voltage of solar module (Va) are also short-circuit current (Isc),  open circuit voltage (Voc), maximum current (Imp), and maximum voltage (Vmp). These characteristics are influenced by the solar cell temperature (Tc) and solar irradiation (Gi). These characteristics are the initial parameter for assessing the efficiency (η), and the usage factor (UF). Efficiency is the ability of SPP to change the solar energy into an amount of electrical energy from the total solar energy received at the surface of the module, while the usage factor is the ratio of energy generated and the maximum energy that SPP can produce.

Efficiency degradation information and early failure detection system will improve the availability and reliability of SPP. One method to increase the availability and reliability is Condition Monitoring.

This study develops an easy, cost-effective, on-line, wireless condition monitoring system that is integrated in the SPP. The system is developed under web-based and cost-effective application by using Free and Open Source Software (FOSS). SPP characteristics data is acquired by using wireless sensors with IEEE 802.11b standard. The system can show the characteristics of SPP in real-time using trends and daily data report facility or monthly on the history facility.

Test results and characteristics analysis of SPP that is mounted on TP Rahmat building bridges for 100 hours or equals to 20 days (5 hours of solar irradiation), give operating conditions of UF = 0.24. This means that SPP electrical energy is only 24% utilized from the maximum electrical energy that can be generated. While the performance of SPP in terms of average efficiency obtained η = 9.59%. This means that the efficiency of solar energy conversion into electrical energy is 9.59%. These result indicate that SPP is in relatively good performance because the efficiency degradation is only 9.36% of normal conditions.

Functional and performance of trends and history facilities in web application are examined by using of active probing methods and client-side measurement. The average performance rate of web application that is obtained by active probing methods is 20.11 seconds with average transfer rate 21.42 kB/s. In the client-side measurement, average transfer rate is 38,33 kB/s while the average performance rate of web applications obtain is 6.83 seconds by using client with Windows 7 operating system and 7.16 seconds for the client with linux OpenSUSE 11.2 operating system. From the results of this test, it is also obtained that the average time difference between the recorded data from wireless sensor and data displayed on the client is 3.94 seconds.

This system can be developed further by adding other facilities related to the availability and reliability of an SPP such as for batteries or inverters. By applying condition monitoring for determining state-of-charge (SOC) and state-of-health (SOH) of battery or condition monitoring to obtain the efficiency of an inverter, the concept of measuring the availability and reliability of SPP can be enhanced and completed.

Keywords:
Solar Power Plant, Condition Monitoring, Wireless Sensor, Efficiency, Usage Factor