Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Estimation of state of health based on charging characteristics and back-propagation neural networks with improved atom search optimization algorithm

With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.

Study and Implementation on Batteries Charging Method of Micro-Grid Photovoltaic Systems

In the micro-grid photovoltaic systems, the random changes of solar radiation enable lead-acid batteries to experience low SOC (State of Charge) or overcharged for periods of time if directly charged with such traditional methods as decreased charging current, which will reduce lifetime of batteries. What’s more, it’s difficult to find a proper reduction coefficient in decreasing charging current. To adapt to the random changes of circumstance and avoid selecting the reduction coefficient, a new fast charging method named decreased charging current based on SOC is proposed to apply into micro-grid photovoltaic systems. It combines batteries’ SOC with the maximum charging voltage to determine the charging rate without strictly selecting reduction coefficient. By close-loop current control strategy and related scheme, the experiment proves the new method is feasible and verifies that, comparing with decreased charging current, the improved method make batteries’ SOC reach 100% in shorter time as well as the temperature of batteries raise more slowly.

Multi-stage constant current charging strategy considering SOC intervals and voltage thresholds

Conventional multi-stage constant current charging strategies often use higher multiples of current to charge the battery in pursuit of shorter charging times.However,this leads to an increase in battery temperature,while shortening the charging time.This in turn affects the safety of the charging process.Furthermore,the higher charging currents are not ideal for shortening the charging time in the later stages of charging.To solve the aforementioned problems,in this study,a multi-stage constant current charging strategy is presented.This strategy can shorten the battery charging time by using the increase in battery temperature during the charging process as a constraint,using a genetic algorithm to calculate the charging current value,and investigating the phased approach to charging.Finally,the charging strategy is experimentally validated at different ambient temperatures and different initial SOCs.The experimental results show that the charging strategy proposed in this paper not only reduces the amount of calculations,but also reduces the temperature rise by up to 46.4%and charging time by up to 4.2%under different operating conditions.

The SOC Estimation of Power Li-Ion Battery Based on ANFIS Model

On basis of traditional battery performance model, paper analyzed the advantage and disadvantage of SOC estimation methods, introduced Adaptive Neuro-Fuzzy Inference Systems which integrated artificial neural network and fuzzy logic have predicted SOC of battery. It’s a battery residual capacity model with more generalization ability, adaptability and high precision. By analyzing the battery charge and discharge process, the key parameters of SOC are determined and the experimental model is modified in MATLAB platform.Experimental results show that the difference of SOC prediction and actual SOC is below 3%.The model can reflect the characteristics curve of the battery. SOC estimation algorithm can meet the requirements for precision. The results have a high practical value.

Lithium-Ion Battery Pack Based on Fuzzy Logic Control Research on Multi-Layer Equilibrium Circuits

In order to solve the problem of inconsistent energy in the charging and discharging cycles of lithium-ion battery packs,a new multilayer equilibrium topology is designed in this paper.The structure adopts a hierarchical structure design,which includes intra-group equilibrium,primary inter-group equilibrium and secondary inter-group equilibrium.This structure greatly increases the number of equilibrium paths for lithium-ion batteries,thus shortening the time required for equilibrium,and improving the overall efficiency.In terms of control strategy,fuzzy logic control(FLC)is chosen to control the size of the equilibrium current during the equilibrium process.We performed rigorous modeling and simulation of the proposed system by MATLAB and Simulink software.Experiments show that the multilayer equilibrium circuit structure greatly exceeds the traditional single-layer equilibrium circuit in terms of efficacy,specifically,the Li-ion battery equilibrium speed is improved by 12.71%in static equilibrium,14.48%in charge equilibrium,and 11.19%in discharge equilibrium.In addition,compared with the maximum value algorithm,the use of the FLC algorithm reduces the equalization time by about 3.27%and improves the energy transfer efficiency by about 66.49%under the stationary condition,which verifies the feasibility of the equalization scheme.