Charging Control System Research Articles

Research on charging strategy based on improved particle swarm optimization PID algorithm

Aiming at the electric vehicle charging pile control system has the characteristics of multi-parameter, strong coupling and non-linearity, and the existing traditional PID control and fuzzy PID control methods have the problems of slow charging speed, poor control performance and anti-interference ability, as well as seriously affecting the service life of the battery, this paper designs a kind of improved particle swarm algorithm to optimize the PID controller of the charging control system for electric vehicle charging piles, and utilizes the improved particle swarm algorithm to Adaptive and precise adjustment of proportional, integral and differential parameters, so that the system quickly reaches stability, so as to improve the accuracy of the system control output current or voltage. Simulation results show that the optimized system response speed of the improved particle swarm algorithm is improved by 3.077 s, the overshooting amount is reduced by 1.01%, and there is no oscillation, which has strong adaptability and anti-interference ability, and can significantly improve the control accuracy and charging efficiency of the charging pile control system.

Malicious mode attack on electric vehicle coordinated charging and its defense strategy

Power systems exploit Internet of Things (IoT) to coordinate the huge charging demands of numerous electric vehicles (EVs) in intelligent transportation systems (ITSs). However, since connected with public networks, the coordinated charging control system is in a low-level cyber security and greatly vulnerable to malicious attacks. The malicious mode attack (MMA), a new cyber-attack pattern, generates high amplitude forced oscillations, which seriously threats the stability of power systems and the power supply of charging stations. However, the MMA model and attack process is not clear. Moreover, it is difficult to actively eliminate the MMA risk through the existing cyber defense technologies. Therefore, this paper analyzes the characteristics of MMA and provides a multi-information active distribution rejection control (MIADRC) method for the physical smart grid to defense MMA. First, the potential threat of MMA is clarified by investigating the vulnerability of the IoT-based coordinated charging load control system. And an MMA process like Mirai is given as an example, which affects the electricity charging system by penetration, infection and attack. Then, an MMA model is established for impact analysis, where expressions of the mean response (i.e. expected value) and stochastic response (i.e. standard deviation) of the MMA forced oscillation are derived to discover main impact factors. Further, to mitigate the impact of MMA, a defense strategy based on multi-index information active disturbance rejection control is proposed to improve the stability and anti-disturbance ability. Finally, simulations are conducted to verify the existence and characteristics of MMA threats. And it is verified that the proposed MIADRC can suppress 91.8 % of the MMA oscillation amplitude. As far as the authors know, it is the first time that the characteristics of MMA are investigated and multi-information-based defensive strategy for MMA is proposed, which enhances system damping through compensating for the attack disturbance.

Automated ball charge control system for grinding units

Automated ball charge control system for grinding units

Design of battery state of charge monitoring and control system using coulomb counting method based

<span>Lead-acid batteries are commonly used in photovoltaic systems to store solar energy for continuous use. However, lead-acid batteries have a relatively short lifespan due to frequent over-charging and over-discharging. A battery management system (BMS) is essential for accurately predicting the battery state of charge (SoC) value in order to extend the battery lifespan. In this research, a BMS is developed using the coulomb counting method to estimate the SoC value of a lead-acid battery. The coulomb counting algorithm provides a reliable estimation of the battery’s SoC value by calculating the incoming and outgoing currents. The BMS also uses two normally closed relays to prevent overcharging and over-discharging. The first relay turns on when the SoC reaches 100% full charge and turns off when the SoC decreases to 70%. The second relay turns on when the SoC reaches 20%. The BMS was tested using Blynk, a cloud-based internet of things (IoT) platform. The results showed that the BMS successfully provided monitoring and reliable control of the lead-acid battery, with a low margin of error. This demonstrates that the developed BMS can be practically implemented in photovoltaic (PV)-battery systems to extend the battery lifespan and improve the overall performance of the system.</span>

Analysis of Battery Testing Protocols in the Transition from the Lab to the Field: A Test Case Using Advanced Zn-MnO2 Batteries in Off-Grid Solar Microgrids

Understanding cycling performance of batteries under varying conditions is crucial for continued development of emerging chemistries. Currently much of this work is focused on cycling single cells in well-controlled lab experiments. There are few module cycling studies in the open literature. Studies of full-sized fielded systems are even rarer. As a result of this data gap, there is limited understanding of how single cell cycling can be used to predict performance of fielded systems prior to their deployment.Sandia National Laboratories (SNL) recently deployed an advanced Zn-MnO2 secondary battery energy storage system in an off-grid solar plus storage system. Using this deployment as a test case, we will detail how performance and degradation for batteries can vary between the lab and the field. We will also discuss ways that single cell and module lab cycling can be improved to better predict operation in fielded systems.In preparation for this deployment, SNL conducted single cell cycling experiments in the lab to simulate operation in the field and determine the expected life of the systems. The first test program utilized elements of the IEC 61427-1 standard for photovoltaic off-grid application to simulate the temperature and predicted state of charge (SOC) range the cells will be cycled during field operation. The simulated field cycling targeted SOC ranges of 10 to 40% and 80-100% over 150 cycles with temperature varied between -4 and 35oC to match the ambient conditions in the field during each season. The second type of cycling test was a temperature dependence study to determine how temperature impacted cell degradation. Cells were cycled at C/10 at their full SOC range and at a single temperature until they reached 40% capacity retention. Both cycling studies predicted that low temperature operation would increase the rate of degradation.The system was deployed in May of 2022, and since that time the SNL team has collected a significant amount of performance data to help analyze system level behaviors as compared to observations from cell level testing. These include battery interactions with the solar charge controller, limitations on power inverter settings, and variance between the average system level state of charge and the test protocol. Additionally, the team is analyzing conditions found in the field, with regards to ambient temperature, solar insolation and battery charging rates, and customer usage patterns for comparison with cell-level testing protocol.Observations of the deployed system’s performance indicate there is a need for more nuanced testing protocols for lab testing that can better approximate field conditions. These may include dynamic temperature ranges that vary throughout a charge/discharge cycle, and solar charging assumptions that account for reduced insolation due to cloud cover. This can help further inform the development of charge control algorithms, system hardware and software that can help improve the overall performance of solar microgrid energy storage systems.Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2023-02502A

A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile

This paper investigates the electric vehicle (EV) charging scheduling problem for public EV charging stations (EVCSs) that can accommodate heterogeneous charging demands, aiming to flatten the aggregate load on the power grid and reduce the peak demand. In contrast to existing works that mainly focus on a single scheduling strategy, a two-level hierarchical charging scheduling method is proposed, which includes an online booking system (OBS) and a pricing-based charging control system (PCCS). Specifically, the implementation of OBS can reduce the service capacity of bookable chargers, whereas PCCS can facilitate the redistribution of charging demand from peak to off-peak time periods. Moreover, the nonlinear charging profile of the battery is incorporated into the model to better reflect the real charging process. A deep reinforcement learning (DRL)-based approach with a discrete Markov decision process (MDP) formulation is designed to find the optimal scheduling solution, where deep Q-network (DQN) and deep deterministic policy gradient (DDPG) are combined to handle the hybrid action space with both discrete and continuous actions. A comprehensive set of experiments is carried out to examine the effectiveness of the developed two-level scheduling scheme. The results indicate that the proposed method improves the scheduling effectiveness compared to single-strategy methods by 6.7% to 49.44%.

Robust voltage-controlled transcutaneous energy transfer system for artificial anal sphincter.

The artificial anal sphincter (AAS) system has gained significant attention as a solution for treating fecal incontinence (FI). It relies on transcutaneous energy transfer (TET) as its primary energy source. However, changes in posture or biological tissue can cause misalignment of the coil, resulting in unstable power reception. Inadequate power affects charging efficiency, while excessive power leads to excessive heating at the receiver side. Consequently, achieving safe and constant voltage charging for the AAS becomes a complex challenge. To maintain a consistent charging voltage and overcome the issue of variations in load and coil coupling strength, this article proposes a wireless charging control system that utilizes an LCC-S-type resonant network and phase shift to adjust the transmitting voltage based on feedback charging voltage in real time. In particular, the PI controller and neural network are introduced to change the phase-shift angle swiftly. The dynamic performance is then evaluated under different misalignments and presented with comparative results. The results indicate that the multilayer perceptron control system outperforms the PI. Under the complex misalignment disturbance, the average error of receiver side load voltage is only 0.007 V, with an average settling time of 960 ms. Additionally, the average temperature at the receiver side is 40.4°C. The experiments demonstrate that the proposed system effectively addresses the misalignment issue in TET during the charging, ensuring constant voltage charging at the receiver side and thermal safety.

Design of Atmega2560 Charge Controller Battery Using Static Bicycle

At this time charging system has been increasingly advanced. advance with technological developments. One of them is the use of microcontrollers whose. applications are growing rapidly their application in charging. Battery Charge Controller is a charging device, to adjust the input voltage and output voltage of the battery so as not to overcharge and overdischarge. In this study, a battery charging control system with inputs produced by a pedal power plant was designed to drain the power from the power cycling generator to the Arduino Uno Microcontroller atmega 2560. The test that have been done on the Battery Charge Controller obtained a voltage of 14 volts, which causes the power supply to the load to be stable.

Instructional Design of Bioinformatics Curriculum for Graduate Student under “Double first-class” Discipline

Development of a closed fly wheel based energy system is a concept for generating energy free that is emission free like the renewable energy sources. It comprises a fly wheel 315kg, 2.5Hp 220volts DC motor,10Hp 250volt Alternator, locally developed hub powered with pump for cooling and hydrodynamic lubrication of the flywheel shaft, charge controller, and smart system. The concept of the design and development of a fossil fuel free generator is driven from the high energy stored in fly wheels. An ecofriendly power generation is needed to reduce emissions that have led to the well-discussed global warming and depletion of ecosystem. The momentary motion of any fly wheel is a function of its mass and its velocity as it affect the duration of its rotation before coming to rest. The interconnectivity among the flywheel, generator, a battery and a DC motor can lead production of a reasonable amount of Electrical Energy. Inertia laws and momentum were used in the design of the flywheel. The use of charging controllers and battery management system (BMS) as well as rectifier are used to maintain steady supply and battery life Generation principles, identification of areas of loses, power stability, energy bank, electronic energy booster (fondly known as electronic gear) and smart system for energy management and sustainability.

Switching Solutions

To support a safe and efficient EV charging control system, latching relays and switching technologies offer a component-level solution

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.

Switching Solutions

To support a safe and efficient EV charging control system, latching relays and switching technologies offer a component-level solution

Heat generation and thermal runaway mechanisms induced by overcharging of aged lithium-ion battery

Overcharging occurs owing to the malfunction of charge control and inappropriate battery management system. Overcharging mechanisms, aging mechanisms, and the influence of aging on overcharging are studied in this work. The results indicate that normal charging, lithium plating, electrolyte oxidation and decomposition, excessive electrolyte decomposition, and solid electrolyte interface decomposition and regeneration occur with charging. Much heat is produced in the reactions between lithium plating and the electrolyte, which is the main reason for thermal runaway during overcharging. The primary aging mechanisms of a battery cycled at 40 ℃ and 10 ℃ are solid electrolyte interface growth and lithium plating, respectively, where the former increases and the latter decreases the safety of the lithium-ion battery during overcharging. The trigger for thermal runaway during overcharging changes from a local, micro-level internal short circuit to solid electrolyte interface decomposition and regeneration when the state of health is less than 70 % cycled at 40 ℃. If the electrolyte is depleted and the temperature is less than that promoting cathode decomposition, thermal runaway does not occur during overcharging.

Switching Solutions

To support a safe and efficient EV charging control system, latching relays and switching technologies offer a component-level solution

An Optimized Fuzzy Controlled Charging System for Lithium-Ion Batteries Using a Genetic Algorithm

Fast charging is an attractive way of charging batteries; however, it may result in an undesired degradation of battery performance and lifetime because of the increase in battery temperature during fast charge. In this paper we propose a simple optimized fuzzy controller that is responsible for the regulation of the charging current of a battery charging system. The basis of the method is a simple dynamic equivalent circuit type model of the Li-ion battery that takes into account the temperature dependency of the model parameters, too. Since there is a tradeoff between the charging speed determined by the value of the charging current and the increase in temperature of the battery, the proposed fuzzy controller is applied for controlling the charging current as a function of the temperature. The controller is optimized using a genetic algorithm to ensure a jointly minimal charging time and battery temperature increase during the charging. The control method is adaptive in the sense that we use parameter estimation of an underlying dynamic battery model to adapt to the actual status of the battery after each charging. The performance and properties of the proposed optimized charging control system are evaluated using a simulation case study. The evaluation was performed in terms of the charge profiles, using the fitness values of the individuals, and in terms of the charge performance on the actual battery. The proposed method has been evaluated compared to the conventional contant current-constant voltage methods. We have found that the proposed GA-fuzzy controller gives a slightly better performance in charging time while significantly decreasing the temperature increase.

Switching Technologies

To support a safe and efficient EV charging control system, latching relays and switching technologies offer a component-level solution

Smart Energy Solutions

Latching relays and switching technologies provide support for a safe and efficient EV charging control system

Optimasi Battery Charging pada Pendingin Minuman dengan Sumber Solar Cell untuk Beban Peltier Menggunakan Buckboost Converter

Currently, many electronic devices use the energy source from the solar cell which is stored in a battery. The battery is a portable, rechargeable power source. Solar energy is very suitable when converted to electrical energy because the amount of sunlight is infinite even though there is a period of time between sunrise and sunset. Converting solar energy to electrical energy requires a solar cell. One method that can be done is using the buck boost converter method with solar cell sources to create a battery charging control system. The Buck Boost Converter method was chosen because it can stabilize the output voltage from the solar cell when the weather is uncertain. If the light intensity of the sunlight is dim, the output voltage of the panel will also be low, then the converter will be in boost mode to increase the voltage level, on the other hand, if the light intensity of the panel output voltage will also be high, the converter will be in buck mode to lower the voltage level. The output voltage of this control system is maintained according to the battery charging voltage standard, which is 14 volts DC.

МОДЕЛЮВАННЯ ПРОЦЕСУ КЕРОВАНОЇ ЗАРЯДКИ ЕЛЕКТРОМОБІЛІВ В УМОВАХ ОБМЕЖЕНОГО ЕЛЕКТРОПОСТАЧАННЯ

Purpose. The purpose of this research is to increase the efficiency of the charging station, which is to meet the demand of energy for charging the batteries of electric vehicles without exceeding the limits of the grid restrictions. Methodology. The model of a controlled charging system was considered to study the efficiency of charging stations with the quantitative connection of electric vehicles to the charger under conditions of power limitation. Results. The load graph during the controlled charging method does not exceed the set limit voltage and power consumption settings, but with the uncontrolled method there is an overload of 16% during peak hours. The main disadvantage of the conventional method of charging with restrictions is that EVs that have been connected to the charging slots later than other EVs, were not charged in conditions of limited power supply. The charging stations require a charging system that does not have such a disadvantage – it is necessary that almost all connected electric vehicles fill their required level of charge and meet the limitations of the network at the same time. Investigating such a system is the task of future research. Originality. Every year the number of electric vehicles in Ukraine is growing rapidly. This also determines the development of a network of charging stations, both commercial and for domestic use. According to the Rules for the use of electricity, the contract for electricity consumption specifies the allowable capacity for the household or enterprise. Violation of this limit may cause the facility to become disconnected from the power supply with additional penalties. Practical value. The solutions to the problem of charging a large number of EVs are mainly based on different types of planning and creating a profile of their charging scenarios with technical limitations. These restrictions depend on the situation of the charging station (the number of connected EVs, their charging levels, the possibility of simultaneous charging for a few EVs, and the declared charging time). The charging period convenient for the EV owner and the network load schedule may conflict. The development of a realtime charging control system should provide each EV guaranteed charging even in conditions of limited power consumption. References 13, tables 4, figures 18.

Optimization and Enhancement of Charging Control System of Electric Vehicle Using MATLAB SIMULINK

Several algorithms and approaches have been adopted for wide range of controlled systems. One of the most interesting algorithms was the Rider Optimization Algorithm (ROA), which has been widely used in many applications for the control system. In this paper, the ROA algorithm has been utilized in the Controlled System (Charging Control System associated and decrease the steady-state error, high overshoot, decreasing for rising time and settling time these issues that we are looking for disentangling it in the controlled System to get furthest reaches of depending capacity that makes Controller work in a best case. of Electric Vehicle) to enhance and optimize the charging procedure. Results obtained shows significant enhancement where the parameter values achieved for the Maximum overshooting, raising time, peak time and the settling time were zero%, 0.0341, 0.7, 0.1188 second respectively, which could be considered as more realistic results as compared with latest work in the same field. Such results have showed a significant enhancement as compared to the related results achieved in the same manner.