Lead-acid Charging Research Articles

An improved takagi - sugeno variable step size perturb and observe MPPT algorithm based lead acid battery charge controller for photovoltaic system

The main objective of photovoltaic system controller is to reduce the price of the kWh compared to traditional fuel-based electricity. Thus, the improvement of simple and low – cost controllers is a major challenge. Perturb and Observe (P&O) is the commonly used algorithm in order to ensure the power maximization of the photovoltaic system, due to its simplicity and effectiveness. However, on the main drawbacks of that algorithm is the fixed step size of perturbation, due to working environmental changes, which require dynamic tuning of the controller parameter. In order to overcome the cited disadvantage, a new Takagi - Sugeno variable step P&O is proposed. Based on the real-time measurements of the solar radiation and temperature, the proposed fuzzy system schedules the appropriate step size based on human expertise. A zero order Takagi – Sugeno with singleton output is proposed. The fuzzification were carried out using triangular and trapezoidal membership functions. The centroid defuzzification method based 25 If -Then rules are adopted. In order to verify the effectiveness of the improved P&O, three scenarios were used. The obtained results show the effectiveness of the proposed controller under shaded conditions and various temperatures.

Smart charging process development based on ant colony optimization machine learning for controlling lead-acid battery charging capacity

The Indonesian government has targeted 2.1 million two-wheeled electric vehicles and 2,200 four-wheeled electric vehicles (EV) by 2025. This is hampered by limited electricity supply and EV charging, which takes long time. Multi device interleaved DC-DC bidirectional converter has been applied and assessed as the most suitable method for battery EV and plug-in hybrid EV because it produces high power >10 kW. For power below 10 kW, it is recommended to use a sinusoidal, Z-Source, and boost amplifier type converter. The smart charging (SC) system will be applied to electric vehicles, which only require a minimum charging power of around 169 W for four lead acid batteries. This paper focuses on an SC system that is capable of charging the battery quickly while still paying attention to the state of health (SoH) of the battery. The SC developed uses a DC-DC boost converter to increase the voltage produced by the switch mode power supply (SMPS). Estimated charging time is less than 30 minutes and still pay attention to the battery SoH. SC will also use pulse width modulation (PWM) as a duty power cycle regulator. This research applies a multi-layer perceptron (MLP) classifier to a neural network (NN). The results of the research show that smart charging can charge up to 600 W with an estimated charging time of around 11 minutes. The charging condition is above 60 % and the power duty cycle setting is 100 %. The power estimation results processed using the ant colony optimization (ACO) based neural network method show a root mean square deviation value of 0.010013430 for charging four lead acid batteries. These results are useful to help solve the problem of capacity requirements and battery charging speed for EVs, with good SoH

Prediction of State of Charge for Lead-acid Batteries Based on GRU Network and Isolated Forest

Accurate prediction of the state of charge (SOC) of lead-acid batteries is the key to ensuring battery life. In this paper, a new combined SOC prediction model IF-GRU (Isolation Forest, Gated Recurrent Unit) is proposed. The model combines the Isolation Forest anomaly detection algorithm and the Gated Recurrent Network. The Isolation Forest algorithm is used to detect anomalous and missing values in the raw data. Length dependence of the GRU network can be further utilized to perform high-accuracy SOC estimation by implementing a sliding window that takes into account the data's charging and discharging details. In addition, the conventional Adam optimizer is utilized to improve the convergence speed of model training. The experimental data demonstrate that the IF-GRU model proposed in this paper has higher prediction accuracy and convergence speed with a RMSE of 1.59% compared with traditional LSTM network, GRU network, and BP network.

Design Of Charge and Discharge Performance Inspection System for Lead-Acid Battery in Coal Mine

On the basis of the introduction of coal mine safety production situation and coal mine lead-acid battery, we designed the coal mine lead-acid battery charging and discharging performance inspection system, focusing on the design of the charging or discharging test voltage detection circuit, current detection circuit and IGBT isolation drive circuit, to realize the inspection of the charging and discharging performance , battery capacity and other test items of lead-acid battery in coal mine.

Estimating the estate of charge of lead-acid batteries.

A new system approach for battery discharge to estimate the state of charge of lead-acid batteries has been developed and tested. This new system is based on an industrial network that runs the Modbus Protocol with RTU format. Each unit includes an ID for communication in this network, its own power supply, a stable current supply for battery discharge and its own calibration curve for each channel at the analogical-digital converter. All parameters can be configured by the software supervisor. This system is able to discharge up to 16 batteries simultaneously and save voltage and current values for each unit. All information can be saved into a file that can be read simultaneously to the data acquisition. Thus, it is easier to have it integrated to a database and distributed on to a computing network system. This system will bring more information to the battery ageing estimator, based on the observed voltage distributions over one month. The Pierre Auger Observatory works with different set-ups for the 1600 PV-stand-alone systems. Therefore, a larger scale system capacity to test each battery unit a time has become necessary.

Fault Detection for Low Voltage System in Electric Vehicle

With the rapid development of the electric vehicle ecosystem, there are concerns about the electrical safety of electric vehicles. This concern is caused by the problems with electric vehicles, which cannot be used when not used for a long time. This problem is caused by a low voltage rating that is not available when the electric vehicle is in that condition. In this study, an algorithm is proposed to support safety for the availability of low-voltage electrical energy, when electric vehicles are in use or not in use. The results of this research indicate that the created algorithm can provide safety for the availability of low voltage level electrical energy, by adjusting the lead-acid battery charging procedure, monitoring the low voltage level while it not in use, and making existing electric vehicles unable to move as long as problems with the low voltage level charging system cannot be resolved.

A Soft Computing Intelligent Control Algorithm to Extract Maximum Energy from Solar Panel

Utilizing soft computing, a maximum power point tracking (maximum PPT) control algorithm is developed, and its performance is compared to that of more traditional Lead Acid battery charging methods such as incremental conductance technique-based maximum PPT. Since the power vs voltage graph of a photovoltaic (PV) cell is nonlinear, a suitable control method seeks to obtain the highest power under dynamic conditions. In order to construct a PV cell with the maximum PPT, a fuzzy logic control approach known as soft computing is used. The cell active energy is used to charge the lead acid battery. A fuzzy logic compares its performance with the incremental conductance technique under dynamic conditions. Moreover, dc to dc converter is required to maintain constant output voltage to charge the battery under low level voltage. A zeta converter is taken to maintain output voltage under various insolation. The significance of algorithm is demonstrated by MATLAB Simulation results and hardware results.

Design and Development of Electric Vehicle Charging Station

This project aims to develop a system for EV battery charging and monitoring with location tracking using NodeMCU microcontroller-based hardware and software solutions. The system will include a battery charger with built-in voltage and battery percentage monitoring, as well as a GPS tracking device to track the battery's location and usage. The hardware components will be integrated with software solutions, including mobile apps and web-based dashboards, to provide real-time monitoring and alerts on the battery's charging status, health, and location. The system will utilize predictive analytics and machine learning algorithms to provide insights and recommendations for optimizing the battery's performance and lifespan. Overall, this project seeks to provide a comprehensive solution for lead-acid battery charging and monitoring with location tracking, using cutting-edge technologies and techniques to enhance battery performance and longevity. This project involves the development of a NodeMCU-based system for charging and monitoring lead-acid batteries with location tracking capabilities. The system utilizes a NodeMCU microcontroller to control the charging voltage and current, as well as monitor the battery's voltage and battery percentage during charging. Additionally, a GPS module is used to track the location of the battery and monitor its usage. The system includes a mobile app and web-based dashboard for real-time monitoring and alerts, which can provide insights and recommendations for optimizing the battery's performance and lifespan. This project aims to provide a low-cost and easy-to-implement solution for charging and monitoring lead-acid batteries with location tracking capabilities

Impact of high constant charging current rates on the charge/discharge efficiency in lead acid batteries, for residential photovoltaic system applications

The charging and discharging of lead acid batteries using Traditional Charge Controllers (TCC) take place at constantly changing current rates. These techniques do not permit the accurate estimation of energy input and energy output during charge and discharge processes. In this work, the main objective is to investigate the effect of high constant charging current rates on energy efficiency in lead acid batteries, extending the current range to 8A from 5A already reported in literature. We also present the experimental proof of the variation of electrolyte temperature with magnitude of the constant electric charging current. Firstly, a Constant Current Circuit (CCC), capable of charging the battery at current rates ranging from 0.5A to 8A was built and used to run experiments on two sample lead acid batteries, battery sample 01, the Vanbo battery and battery sample 02, a Winbright battery. Charge and discharge processes were conducted on these batteries through the CCC and efficiencies computed per current rate tested. Secondly, to compare the results of the CCC built with those of a TCC, the very batteries (battery sample 01 and battery sample 02) were charged and discharged through a TCC and their corresponding efficiencies computed. Thirdly, three constant charging current regimes (0.5A, 5A and 8A) were chosen within the tested current rates for which further electrolyte temperature monitoring tests were carried out, using two other lead acid battery samples of different health states. This was carried out on battery sample 03 which was a SIGA lead acid battery type with a compromised state of health and on battery sample 04, another brand new Winbright battery with relatively good state of health. It was noticed that, for the CCC, efficiencies between 62 % and 82 %, were recorded for the Vanbo Battery (Sample 01) while efficiencies of 53 % to 91 % were recorded for the Win Bright Battery (Sample 02). On the other hand, the efficiency of the Vanbo Battery (Sample 01) through the TCC was found to be 66 % while that for the Winbright battery (Sample 02) was found to be 79 %. As for electrolyte temperature monitoring, battery sample 03 with compromised health state registered higher temperatures for higher charging rates while battery sample 04 of good state of health registered no noticeable increase in temperatures even for constant charging current rates up to 8A. Since existing literature had tackled lower current values from 0.5A to 5A, this work therefore comes in with an extension of the current rates, testing higher current magnitudes and obtaining the same results with conclusion that, if the same energy is stored in a lead acid battery at precise rates, the charge/discharge efficiency of the battery increases as the charging current rate increases. As regards electrolyte temperature monitoring, the work provides further proof that even at charging current rates as high as 8A, battery electrolyte temperatures still remain within acceptable limits(below 50 °C).

Design and Implementation of a Digital Control System for Lead Acid Battery Charging

Ensuring a long battery life and satisfactory performance requires accurate charging cycles. There are three phases to the charge cycle - Constant Current Charge, Constant Voltage Charge, and Float Charge. It is usual that lead acid battery users complain about fast degrading performance because most the low cost commercially available lead Acid Battery chargers provides only single-stage charging phase which is that of constant-voltage charging phase. To ensure long service life and good performance, it is of paramount importance that all charging modes are respected. This said it is clear that the battery charger should have a certain degree of controllability over voltage and current quantities through-out the charging process. In this paper, we designed and built a lead acid battery charger to use in conjunction with a synchronous buck converter topology. After implementing and testing the system, we obtained good results in both the quantitative and qualitative analysis of the implemented system tested, a 12 V- 7000mAh battery. With the help of a MCU-based digital control system containing two different control transfer functions - constant current Feedback Control and Constant Voltage Feedback Control monitoring the charging process proved possible without any overshoot. The prototype showed us an efficiency rating of 86.60%, the maximum error level was recorded at 0.05V, and there were no problems related to overshoot or transient response when testing our prototype which worked flawlessly.

Solar tracker design on solar panel for stm32 microcontroller based on battery charging system

This paper describes about solar energy is one of the energy sources that won’t terminated. Solar energy can be easily converted into electrical energy by using solar panels. Solar panels that are placed horizontally on the ground, the solar panel cannot absorb the light perfectly. Therefore, solar panels require an automatic solar tracking system to increase the efficiency of the solar panels. In this study, a solar tracker has been designed using a light dependent resistor (ldr) sensor based on the stm32 microcontroller. From the results of the study, the increase in power obtained from the use of a solar tracker was 27.97%. In static solar panels, the time required to fully charge the 12v, 7ah lead acid battery is 5 hours 10 minutes, while using a solar tracker the lead acid battery charging time is 3 hours 30 minutes. The use of a solar tracker saves battery charging time for 1 hour 40 minutes compared to static solar panels.

A 3-states mode lead-acid battery charger simulation for medical applications

The design and simulation of a battery charger is presented, for medical applications using three-state mode charge, containing a Buck converter and a lead-acid battery. Implementing Proportional Integral (PI) controllers, to modify the duty cycle of the Buck converter, in such a way that the battery is subjected to the 3 states during the charging process, the PI controller reference changes for each of the 3 states. In this way the battery is subjected to the 3 states with a single PI controller. In order to predict the current and voltage values ​​to which it would be subjected in the real system in a charging process with this algorithm, it was possible to obtain the battery charge graphs, which describe all the voltage and current values. that the actual battery will undergo in a 3-state charge. Concluding that the charging current is maintained at adequate values, however there is a possibility that in state 2, the voltage exceeds the tolerated overvoltage value, so it is recommended to reduce the voltage reference in state 2.

Novel Micro flow Sensor for Air Purge Method to Monitor the State of Charge of Lead Acid Battery

The foremost aim of the present research study is to measure specific gravity of lead-acid batteries and further know batterys state-of-charge (SoC). In this article, air purge method is used to monitor the status of a battery; back differential pressure is measured using MPX4006 DP MEMS pressure sensor. In air purge method, it is essential to adjust the rate of bubbles in acid. This can be achieved by controlling the compressors speed or controlling the air flow by controllers. When air purge tubes are inserted through a batterys cap, it is difficult to observe bubbles produced in the acid as the battery is nontransparent. If the bubble rate is too slow, the system lowers the output signal and hence stops working. During the calibration of the instrument, air flow measurement through the pipe is of prime importance. In this method, FS1012 MEMS gas flow sensor is used to monitor the discharged air flow. FS1012 is based on the thermo-transfer principle. Compared to the resistive technique, this flow sensor consists of a thermopile for air flow sensing whose signal-noise ratio is exceptional. The output of the sensor is exponential; signal conditioning circuit has been designed to get linear output 0 to 3.3 V standard range. The amplified output of signal conditioning circuit is monitored using TM4C123GXL arm controller. FS1020 is first time proposed to monitor the air flow of a small air purge probe used to monitor the state of charge of a lead-acid battery. FS1020 ensures an ideal flow of the air bubbler system, which improves the performance of the battery charge monitoring system. Conventional flow sensors for example, rotameter cannot be used for very low flow measurement; they are costlier as well as bulky. The minimum flow required to produce sufficient bubbles in acid is 0.50 liter per minute (LPM) which is measured by FS 1012.

An hybrid control strategy design for Photovoltaic battery charger

This work presents the design and the modelling of an improved lead acid Battery charger for solar photovoltaic applications. In this context, the control unit of the battery charger is composed of two intelligent controllers. In the first state, an MPPT controller based on an Adaptive neuro-fuzzy inference system (ANFIS) is used to extract the full maximum power provided by the PV array, in the second stage, the control unit switches to the regulator mode on the basis of a fuzzy logic control block that offers the three charging stages according to DIN 41773 standard for lead-acid battery. In order to demonstrate the performance of the ANFIS controller, this paper presents also a comparison of several MPPT techniques for solar PV applications.

A real-time estimator for model parameters and state of charge of lead acid batteries in photovoltaic applications

The intermittent nature of photovoltaic energy source has revealed concerns about the stability of the power electric system. For that, a massive use of storage elements becomes needed. Batteries are considered as one of the most important technologies for energy storage. In order to achieve the needs of safety, durability and reliability for the battery management system (BMS), data monitoring and diagnosis system has been discussed in this work. In fact, several methods have been presented with the intention of estimating the internal parameters of an AGM lead acid battery model such as the Recursive least square algorithm (RLS) with variable forgetting factor, a novel Adaptive Joint Extended Kalman Filter (AJEKF), and the Unscented Kalman Filter (UKF). The state of charge (SOC) of electrochemical storage device has been considered as one of the most important state which needs to be monitored in order to optimize the performance and to extend the lifetime of the system. SOC estimation is a challenging task hindered by considerable changes of the battery characteristic throughout its lifetime due to its nonlinear behavior and aging factor. In this work, online state of the charge estimation has been evaluated through the Extended Kalman Filter, Unscented Kalman Filter (UKF), and Cubature Kalman Filter (CKF). An Open circuit voltage experimental test (OCV) has been established in order to validate the operation of the proposed battery model under real work conditions.This paper proposes an exhaustive review for the online identification of battery parameters and its state of charge, where the tendency of estimation techniques has been oriented toward a mixture of different techniques that clearly raised the challenge of establishing a relationship between the accuracy and the robustness of these methods, without forgetting their low complexity of implementation.

An intelligent lead-acid battery closed-loop charger using a combined fuzzy controller for PV applications

This paper presents the modeling of an intelligent combined MPPT and Lead-Acid battery charger controller for standalone solar photovoltaic systems. It involves the control of a DC/DC buck converter through a control unit, which contains two cascaded fuzzy logic controllers (FLC), that adjusts the required duty cycle of the converter according to the state of charge and the three stage lead acid battery charging system. The first fuzzy logic controller (FLC1) consists of an MPPT controller to extract the maximum power produced by the PV array, while the second fuzzy controller (FLC2) is aimed to control the voltage across the battery to ensure the three stage charging approach. This solution of employing two distinct cascaded fuzzy controllers surmounts the drawbacks of the classical chargers in which the voltage provided to the lead acid battery is not constant owing to the effects of the MPPT control which can automatically damage the battery. Thus, the suggested control strategy has the benefit of extracting the full power against the PV array, avoiding battery damage incurred by variable MPPT voltage and increasing the battery’s lifespan.

Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems

Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is a complex multi-objective challenge involving economical maintenance, environmental metrics, and energy demand requirements. In this paper, data from instrumented vessels on the River Thames in London, which includes environmental emissions, power demands, journey patterns, and variance in operational patterns from the captain(s) and loading (passenger numbers), is integrated and analyzed through automatic, multi-objective global optimization to create an optimal hybrid propulsion configuration for a hybrid vessel. We propose and analyze a number of computational techniques, both for monitoring and remaining useful lifetime (RUL) estimation of individual energy assets, as well as modeling and optimization of energy use scenarios of a hybrid-powered vessel. Our multi-objective optimization relates to emissions, asset health, and power performance. We show that, irrespective of the battery packs used, our Relevance Vector Machine (RVM) algorithm is able to achieve over 92% accuracy in remaining useful life (RUL) predictions. A k-nearest neighbors algorithm (KNN) is proposed for prognostics of state of charge (SOC) of back-up lead-acid batteries. The classifier achieved an average of 95.5% accuracy in a three-fold cross validation. Utilizing operational data from the vessel, optimal autonomous propulsion strategies are modeled combining the use of battery and diesel engines. The experiment results show that 70% to 80% of fuel saving can be achieved when the diesel engine is operated up to 350 kW. Our methodology has demonstrated the feasibility of combination of artificial intelligence (AI) methods and real world data in decarbonization and optimization of green technologies for maritime propulsion.

Design, Construction and Evaluation of 5A AC-DC Portable Lead Acid Battery Charger with an Inbuilt Automatic Shut OFF

Battery charger is a system that restores battery after being discharged. It is well known that battery can be destroyed for being discharged below its manufacturers specified discharge voltage limit, when left for some hours. A single switch fly back buck converter that enables battery charging from the alternating current (AC) supply is designed, and constructed. To prevent the battery from overcharging an automatic charge controller was added to the design. Size of the charger was minimised by designing it to operate at higher frequency. It has an input potential difference (PD) from 80 to 250 volt. The circuit components and other design materials were chosen based on their simplicity and availability in most of the electronics shops. Red light emitting diode (LED) is ON when the charger is connected to the mains supply while the green LED is ON when charging is cut OFF or completed. The design was first conducted on MULTISIM software to ascertain the working conditions of the circuits before the construction procedure. Performance analysis was carried out and found working appropriately.

Modeling of Photovoltaic MPPT Lead Acid Battery Charge Controller for Standalone System Applications

This paper presents the circuitry modeling of the solar photovoltaic MPPT lead-acid battery charge controller for the standalone system in MATLAB/Simulink environment. A buck topology is utilized as a DC-DC converter for the charge controller implementation. The maximum power of the photovoltaic panel is tracked by the Perturb and Observe MPPT algorithm. The battery charge controller charges the lead-acid battery using a three-stage charging strategy. The three charging stages include the MPPT bulk charge, constant voltage absorption charge, and float charge stage. The performance analysis of the model is carried out in the following aspects, there are MPPT tracking performance, battery charging performance and overall charge controller efficiency performance are benchmarked with commercial MPPT charge controller for validation. The performance result shows that the MPPT is capable to track to the PV panel maximum point at any solar irradiance variation within 0.5 seconds with maximum power tracking efficiency up to 99.9 %. The three-stage charging strategy also successfully demonstrated. The overall charge controller average efficiency achieved up to 98.3 % which matches many high end commercial solar PV MPPT charge controller product specifications. This validated model contributes to a better sizing of PV panel and battery energy storage for the small and medium standalone PV system.

Modeling the Charge and Discharge Behaviors of an AGM Lead-Acid Battery

An absorbent glass mat (AGM) lead-acid battery is a type of valve-regulated lead-acid (VRLA) battery. An AGM lead-acid battery differs from a flooded lead-acid battery in that the sulfuric acid is absorbed in a very fine fiberglass mat, making the battery spill-proof. The leading advantages of AGM type are a higher specific power, lower internal resistance, five times faster charge speed than the flooded version, and the ability to deep cycle. AGM lead-acid batteries are found in high-end vehicles equipped with more electronic devices and making inroads into the start-stop function of cars. A validated mathematical model for the accurate prediction of the charge and discharge behaviors of an AGM lead-acid battery in real-world driving conditions is necessary to ensure that the battery can provide enough power for the rising number of electrical components. In this work, a method of modeling the charge and discharge behaviors of an AGM lead-acid battery under various operating conditions is presented. This work adopts a simple modeling approach by considering Ohm’s law and charge conservation on the electrodes based on the simplified polarization characteristics of the electrodes. By not computing the potential distribution of electrolyte phase and the transport phenomena of ionic species, the modeling method in this work cuts down considerable computation time in comparison with rigorous electrochemical models. The model is validated by comparing the modeling results with the experimental measurements.