Charge Controller Research Articles

Fuzzy Logic Controller Based Charging and Discharging Control for Battery in EV Applications

The present research addresses the fuzzy charging and discharge control method for batteries made with lithium-ion utilized in EV applications. The proposed fuzzy-based solution takes into account available parameter to charge or discharge the store within the safe functioning area. To analyses and control battery performance, a variety of controlling methods have been used, but each has its own set of drawbacks, such as the inability to stop two charging conditions, the difficulty of the controller, the lengthy charge time. Due to the lack of mathematical calculations, a fuzzy controller is also simpler to construct, has less additional sensing components, and fewer deep discharging and overcharging protections, making it more efficient in terms of speed and complexity. The effectiveness of the suggested charging-discharging controller system is demonstrated through numerical simulations employing load demand and generation. Performance of the suggested controller is evaluated under simulated load conditions. The model's ability to regulate battery charging and discharging is confirmed by the trial's successful conclusion. The output shows that the battery's state of charge (SOC) never goes above the 20% to 80% safe range for that specific type. A new fuzzy model and an operational real-time system for regulating battery charging and discharging are the major results of this research.

A New Design and Embedded Implementation of a Low‐Cost Maximum Power Point Tracking Charge Controller for Stand‐Alone Photovoltaic Systems

This article presents a low‐cost maximum power point tracking (MPPT) charge controller designed to efficiently charge solar batteries using a simple and robust MPPT method, while providing protection against over‐currents and over‐voltages. The proposed MPPT approach utilizes a modified Incremental Conductance (INC) algorithm adapted to low‐cost embedded controllers, as it avoids all the mathematical division calculations typically used in the conventional INC algorithm. It also introduces a modified step size based only on photovoltaic (PV) power change to enhance tracking accuracy and convergence speed. Furthermore, to reduce the stress and switching losses of DC–DC converter, a feedback control with two proportional and integral (PI) compensators is also used to control both the PV voltage and to protect the battery from over‐currents using PV power limitation technique. Simulation results, carried out under MATLAB/Simulink, demonstrate the good performance of the proposed control scheme regarding any variations in atmospheric conditions, both for the MPPT control and for the battery charge control. Finally, a processor‐in‐the‐loop (PIL) test using a low‐cost Arduino MKR ZERO board is also performed to verify the hardware implementation of the proposed method. PIL results are in totally accordance with MATLAB/Simulink simulation results, which further proves the effectiveness of the proposed method.

Modelling of Charging Demand for Electric Vehicle based on Person-trip Survey Data

In electricity distribution networks, voltage might be dropped significantly by simultaneous charging of high penetration Electric Vehicle (EV). The impact of EV on voltage drop can be different among areas depending on various factors such as original electricity demand profile without EV, EV penetration level and usage pattern, etc. For the statistical assessment of impact of high penetration EV in various distribution networks, this study proposes a model to estimate time-series data of electricity demand including EV charging. The proposed model is based on Grid Square Statistics Data on geographical distribution of population, employee, etc. and Person-trip Survey Data. As an example using the proposed model, this study compares three EV charging strategies in suburban area of Nagoya, Japan. As a result, if the EV charging starts simultaneously at 23:00 at which the night-time discount electricity rate is applied, the night-time electricity demand can be much larger than the day-time peak demand without EV. The increase can be mitigated by autonomous charging control according to the time and SOC of EV returning home in most areas. Finally, by using the calculated electricity demand profile, this study assesses the impact of EV charging on voltage profile. The result shows that the impact of EV charging on distribution network voltage can be avoided by applying the proposed autonomous charging control scheme.

Performance Investigation of Wind/Photovoltaic/Fuel cell Hybrid Energy System for Stand-alone Power Supply

Large numbers of households in isolated areas in Nigeria were reported to have lacking access to national grid. Their basic electricity demand is served through fossil fuel generators which are costly and environmentally unfriendly. The fact that extension of national grid lines to those areas becomes difficult, renewable energy resources exploitation is the best alternatives. Wind and solar energy as clean abundantly available resources could be harnessed in a hybrids form with long-term storage devices for enhancing energy optimization and power stability. This paper investigated the performance of Wind-PV-Fuel cell Hybrid Energy Systems for stand-alone application. The performances of Wind-Battery-Fuel cell and PV-Battery-Fuel cell Hybrid Energy Systems were investigated by using Hybrid Optimization Models for Energy Renewable (HOMER), through cycle charging controls. The techno-economic Figure of merits for the optimal configurations from these systems was compared. The results revealed that Wind-Battery-Fuel cell Hybrid had been the best system for serving4.37kWh/day (1595kWh/yr) residential load demand. The system was reported with 321 kWh/yr excess electricity, 8.59 kWh/y unmet load and 10.5 kWh/yr capacity shortage. While, the Net Present Cost (NPC), Operation and Maintenance Cost (O&MC) and Cost of Energy (COE) were $25,951, $185 /yr and $1.229/kWh respectively. The result of sensitivity analysis, predicted the unmet load and capacity shortage to be declined by 0.0% each, with system autonomy of 7.9 days. Meanwhile, the NPC, O&MC and COE have decreased by 24.6 %, 8.1 % and 25.06 % respectively. This analysis confirmed that Wind-Battery-Fuel cell-Hybrid Energy System could be an economically viable and technically feasible for stand-alone power supply for isolated areas.

Impacts of Electric Vehicle Charging Station with Photovoltaic System and Battery Energy Storage System on Power Quality in Microgrid

The expected increase in electric vehicles necessitates an expansion in charging stations. However, this increase could introduce issues to the power grid, such as the deterioration of voltage stability and an increase in microgrid loading. To address these issues, innovative solutions are imperative. One potential solution is the implementation of charging control mechanisms. This paper analyzes the impact of a residential charging station on a low voltage microgrid from the power quality point of view using a one-year operation simulation. Thirty-seven charging station scenarios with different locations within the network were examined, including instances with no charging control and various combinations involving charging control, photovoltaic power plant, and battery energy storage system. The simulation results of the charging station without charging control show a decrease in some power quality indicators, such as exceeding voltage change thresholds after charging station connection and increasing power losses. On the contrary, the scenarios implementing charging control and local resources effectively mitigate the adverse effects of charging stations on the microgrid.

Stand-alone solar lighting system with Programmable Logic Controller

The circuitry modelling of the standalone photovoltaic (PV) maximum power point tracking (MPPT) lithium-ion battery charge controller is presented in this study using the MATLAB/Simulink environment. For the charge controller implementation, a DC-DC converter called a buck topology is used. The Perturb and Observe (P&O) MPPT algorithm monitors the solar panel’s peak power. The lithium-ion battery is charged in three stages by the battery charge controller. The model’s performance is analysed in terms of MPPT tracking performance, battery charging performance, and overall charge controller efficiency performance. With an MPPT efficiency of up to 99.9%, the performance result demonstrates that the MPPT is capable of tracking to the PV panel maximum point at any variation in solar irradiation. For small and medium standalone PV systems, this verified model aids in improved sizing of PV panels and battery energy storage. Techno-economic analysis refers to the assessment of the economic viability and profitability of using this type of lighting compared to other forms of lighting. The maintenance costs and efficiency of these systems can be improved even more by adding a programmable logic controller (PLC).

Artificial Intelligence Integration Driven Smart Grid Alternative Transforming the Future With Renewable, and Non-renewable Energy Sources

The evolving energy landscape necessitates a paradigm shift towards more sustainable, efficient, and resilient systems. This article explores integrating renewable and non-renewable energy sources within Smart Grid alternatives, highlighting the transformative role of Artificial Intelligence (AI). Renewable sources, like solar and wind, are essential for reducing carbon emissions but present challenges due to their intermittent nature. Non-renewable sources provide stability, ensuring a consistent power supply. AI's capabilities in predictive maintenance, demand response, energy forecasting, and grid optimization enhance grid efficiency and adaptability, facilitating the seamless integration of diverse energy inputs. Essential components of standalone systems, including batteries, charge controllers, and power conditioning equipment, are also examined for their contributions to system robustness and reliability. The Smart Grid, supported by AI and advanced technologies, represents a reimagined energy infrastructure poised to meet modern demands while promoting sustainability and resilience. This holistic approach promises a secure energy future, balancing efficiency with environmental stewardship.

Performance evaluation of a novel ev charging topology for standalone pv systems with charge controller

Performance evaluation of a novel ev charging topology for standalone pv systems with charge controller

Stability-Centric Design of a Droop-Mounted Adaptive Nonlinear Control for EV Charging in DC Microgrid

Stability-Centric Design of a Droop-Mounted Adaptive Nonlinear Control for EV Charging in DC Microgrid

Nonlinear Model Inversion-Based Output Tracking Control for Battery Fast Charging

Nonlinear Model Inversion-Based Output Tracking Control for Battery Fast Charging

A Charger Current–Limiting Scheme to Improve Protection Coordination of Electric Vehicle–Integrated Distribution Systems

Although the development of electric vehicle (EV) technology offers opportunities for reducing CO2 emissions through the electrification of transportation, the integration of EVs into distribution systems poses a significant challenge to the reliable operation of existing protection systems. As the penetration level of EVs continues to rise, the fault current characteristic of the distribution system changes, resulting in load de‐energization, equipment damage, and reduced reliability. This paper develops a protection scheme for preserving coordination between main and backup overcurrent relays considering various penetration levels and locations of integrated EVs. By modifying the EV charger control system, the proposed scheme limits the fault current contribution of adopted EV charge stations into the fault point. The developed scheme does not alter the structure of the available protection system of the distribution network and is compatible with both old and nonprogrammable relays. Furthermore, it does not require communication links. The effectiveness of the proposed scheme is validated through several case studies on the Isfahan distribution network. The findings indicate that the operating time of the backup relay in the conventional protection system exceeds the thermal limit at 100% penetration level of EVs installed upstream of this relay as it reaches 1810 ms, while by using the proposed strategy, this time reduces to 776 ms, preserving protection coordination between the main and backup relays.

Design Development and Construction of Solar Powered Fresh Juice Extracting Machine

One of the greatest problems of fruit farmers in Nigeria is their inability to process and add value to their produce. This results from the paucity of processing and preservation facilities or the prohibitive cost of imported ones, especially for low-income farmers who produce the bulk of the commodity. It therefore becomes imperative to design, develop and construct low-cost, durable, efficient and environmentally friendly machines to meet the needs of both the urban and, more importantly, the rural farmers with a view to improving their income and also making their produce available and affordable all year round. The machine components include feeding hoppers, a top cover, an auger conveyor housed in a cylindrical barrel, a juice sieve, a juice collector, a waste outlet, a transmission belt, a main frame, pulleys, bearings, 3hp DC motor, a control panel, a solar panel, 20Ah charger controller 100Ah Solar battery, breaker and control switch.

SIMULATION MODELING OF MULTI-PORT DC-DC CONVERTER IN MPPT SOLAR BATTERY CONTROLLERS UNDER NEURAL NETWORK CONTROL

Photovoltaic generation system - energy system, designed to convert useful solar energy through photovoltaic systems. It can consist of several components, including a solar array, DC/DC and DC/AC semiconductor converter, battery, filter or transformer, control system (CS). Depending on the application, photovoltaic systems can be operated as part of a self-contained power plant or operate on a network. Thus, it is possible to distinguish several basic configurations of photovoltaic generation systems. The standalone generation system is the most common configuration of photovoltaic generation systems, which contains rechargeable batteries (AB). This system is completely independent of the centralized power supply networks and is suitable for comfortable energy supply to consumers. The use of AB makes it possible to increase the reliability of the photovoltaic system and to extend the possibilities of using e.g. battery power is used during insufficient light or when the load exceeds the generation of solar cells. The scope of such configurations are lighting systems of residential and non-residential objects, power supply of houses and buildings, security systems and emergency power supply, power supply of remote residential and non-residential objects, Power supply to spacecraft, etc. Autonomous generation systems typically contain two transducers. The DC/DC converter acts as a battery charge controller. The control system for such a transducer may include the function of tracking the maximum power point for the maximum use of solar energy. The excess energy will be stored in AB. The DC/AC converter converts the DC current energy into the AC energy of the required frequency and voltage. The advantage of such a system is the possibility of using solar energy, both during the day and at night, due to the power of AB and the possibility of using the system at remote sites where there is no grid power supply. The disadvantage of such a system is the loss of double conversion of solar energy and the high cost of batteries. Artificial Neural Network (ANN) provides an alternative way to solve complex problems. A neural network, with the right structure, can compute the values of any continuous function with some predetermined accuracy. The neural network requires no knowledge of the internal parameters of the solar module, learns quickly, has the ability to optimize and approximate. Therefore, the use of INS to track the maximum power point is relevant and of practical and scientific importance. Keywords: multi-port DC converter, artificial neural network (ANN), rechargeable batteries (AB), pulse width modulation (PWM), solar panels (SP), MOSFET power keys

Soft-switching dual Active Bridge Converter-based Bidirectional On-Board Charger for Electric Vehicles under Vehicle-to-Grid and Grid-to-Vehicle Control Optimization

Electric Vehicles (EVs) are rapidly replacing conventional fuel vehicles, offering powerful, emission-free performance. This paper introduces an innovative three-phase bidirectional charger for Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) applications, strengthening the connection between EVs and the power grid. The charger employs a two-stage power conversion approach with advanced converters and a simplified dq-based charging control strategy. An efficient AC-DC converter facilitates smooth transitions between modes, responding to grid directives for active and reactive power. A Soft Switching Dual Active Bridge (SS-DAB) DC-DC converter optimally interfaces with the EV battery pack, while dual active LCL filters suppress harmonics, enhancing system performance. Simulated results confirm the charger’s effectiveness in a 3.5kW prototype using MATLAB/Simulink.

A Proportional and Weight Based Decentralized Charge Controller of Electric Vehicles for the Improvement of Local Voltage Profile

A Proportional and Weight Based Decentralized Charge Controller of Electric Vehicles for the Improvement of Local Voltage Profile

Photovoltaics Efficiency on Automatic Fish Feeding Distributors Device Using Internet of Things

Renewable energy is crucial for facilitating the shift towards a more sustainable society. It plays a significant role in establishing a clean energy system, mitigating greenhouse gas emissions, and enhancing the well-being of both current and future generations. The study included field observation and data analysis approaches. The study utilized IoT-based automatic fish feed spreaders, photovoltaic units, batteries, SCC (solar charge controller), Android phones, thermometers, multimeter tester, LCDs, ultrasonic sensors, and a microcontroller circuit. This study aims to acquire power, electrical energy, and efficiency measurements from solar systems employed in an Internet of Things (IoT) enabled automatic fish feeder apparatus. The results show that the power consumption needed to operate the IoT-based automatic fish feeder gadget is 48.75 watts. The peak of solar irradiance was recorded at 12.00 WIT, averaging 900.27 W/m2. The lowest intensity was at 09.00 WIT, averaging 422.78 W/m2. Photovoltaics can produce electrical power at 22.75 watts per hour, leading to a daily electrical energy output of 159.25 Wh. The highest photovoltaics efficiency was 16.37% with 465.3 W/m2 of solar irradiance, 30.7°C of temperature and 2.144 m/s of wind speed. The temperature and wind speed has significantly affected on photovoltaics efficiency.

AI enabled fast charging of lithium-ion batteries of electric vehicles during their life cycle: review, challenges and perspectives

This review presents a thorough investigation of factors affecting fast charging, battery modeling, key state estimation and fast charging control strategies and provides a forward-looking perspective on AI enabled fast charging technology of LIBs.

Developing a Transactive Charging Control Framework for EV Parking Lots Equipped With Battery and Photovoltaic Panels: A MILP Approach

Developing a Transactive Charging Control Framework for EV Parking Lots Equipped With Battery and Photovoltaic Panels: A MILP Approach

An Automatic Watering System Based on an Arduino Microcontroller and Soil Moisture Sensors with Solar Panel Power Plant

The large eggplant farming land often makes it difficult for farmers to water. In addition, the water content in the soil does not meet the minimum requirement, which is 80%-90%. Hence, adequate watering with a high level of accuracy is necessary. This study aims to design and invent an automatic watering system for eggplant farming land with an area of 150 m2. The method was to design automatic sprinklers using programming and fritzing software using Arduino IDE. Soil moisture data was obtained from the soil moisture sensor and displayed on the I2c LCD so that farmers could monitor the soil moisture level. The pump used PB-WH1100BCP with a specification of 1100 W with a discharge of 145 l/minute and a head of 30 m. This automatic watering system was supplied with a 500 Wp photovoltaic solar cell with a current value of 2 A, and a voltage of 20V was obtained when the sun was shining and could work effectively for 6 hours per day. To store electric current, this system was equipped with a VRLA 12V- 200Ah battery connected in series, and the current charging was controlled by the Solar Charge Controller (SCC). It can supply electric current to the pump for 24 hours. This automatic watering system has been tested and functions well on eggplant farmland.

Design, Construction and Performance Evaluation of Solar Charge Controller for Street Lighting Application

Solar powered equipment and applications are gradually making their ways into various sector of our day to day life. We need a storage or battery to store the solar energy harnessed during day. A Solar Charge Controller (SCC) which is a part of a solar power system has been designed using Proteus Software, to maintain batteries used in solar power harvesting and utilization in a charged state, without risk of over-charging or over-discharging, ensuring battery durability. The charge controller has battery management system in built, responsible for executing the desired charging algorithm based on the battery chemistry characteristics. After the circuit design and construction, tests like Power Supply test, Accuracy test, charging test and discharging test were conducted. From the results of the tests, the power supply met its requirement to out-put a constant voltage of 5Vfor the microcontroller and LCD circuit. The charging test result shows the charging characteristics of the charge controller. The discharging test result is a negative linear graph showing that the battery is discharging from 14.4 V to about 9.5 V where the microcontroller disconnected the battery from the load. The results showed that the charge controller was able to control and regulate the charging process well. Calculated Mean percentage error was 0.04.