The EV battery can be hierarchically utilized by the two-stage control framework to improve economic efficiency. In the first stage energy management, a novel heuristic algorithm called the walrus optimization algorithm (WaOA) is employed to implement the optimal energy scheduling of microgrid for minimizing operating costs. In the second stage
Three scenarios are applied to the LIB based on the order of RC model for numerical simulations. Each scenario considers the effects of load and fade at various temperatures, as illustrated in Fig. 5.The battery model results by the WaOA are compared to those using other optimization techniques, including the Nelder Mead Algorithm (NMA), Quasi
To verify the proposed algorithm, a current profile comprising 1C and 0.5C currents was applied without the algorithm, as shown in Fig. 16 (a-b). Applying the current profile without the algorithm resulted in the battery charging at high temperatures without additional current limiting, which caused the battery temperature to rise up to 57 °C
This thesis summarises the research work in the development of the battery status estimation algorithm. The work initially focused on the mathematical descriptions of lead acid batteries, and a mathematical model based on this study was then developed and implemented to describe the process of battery discharge. Genetic Algorithms (GAs) were used as a tool to identify the
In this work, the second order resistor-capacitance (RC) circuit is equivalent to the battery model and the particle swarm optimization (PSO) algorithm is employed for
The energy of capacitance is charged by the battery whose SOC is higher than the other batteries through the circuit to avoid the battery being overcharged. Then, the SOC of batteries gradually
The ECM simulates the internal reaction (polarization reaction and diffusion effect, etc.) and external electrical response (double-layer capacitance and electrode internal resistance, etc.) through resistance and capacitance elements to describe the dynamic characteristics of the battery. The conventional integer-order ECM cannot fully match the
Within this phase, the Bald Eagle algorithm incorporates a control parameter denoted as a, which governs the extent of position adjustments and spans a range of 1.5 to 2. Concurrently, a random number r, ranging from 0 to 1, is employed. In this stage, the algorithm pinpoints a region based on the information gathered in the preceding phase
Lithium batteries are widely used in new energy vehicles due to their high energy density, low self-discharge rate, long cycle life, and environmental friendliness .To ensure safe and efficient operation, the battery management system (BMS) must monitor the battery''s state in real time, including the state of charge (SOC), a critical parameter.
Wang Y G et al. estimated the battery SOC using EKF algorithm based on an equivalent circuit model, combined with a small active equalisation circuit to achieve the battery pack equalisation control.Liu R et al. also used EKF algorithm for estimating the battery SOC, and balanced the inconsistency of the battery pack by using capacitive circuit.
In this broader context, researchers are focused on developing advanced algorithms to indirectly estimate battery capacity using existing external measurement techniques, resulting in a
A fused convolutional neural network (FCNN) algorithm based on the battery capacity is proposed. This algorithm innovatively connects two CNNs in series. The first layer
This research article demonstrates how to get a precise lithium-ion battery (LIB) model using one of the artificial intelligence algorithms called the Walrus optimization algorithm (WaOA). The model''s accuracy affects several transient and dynamic analysis simulations, which are carried out for power systems, electric vehicles, and many transportation applications.
There are different sorts of batteries accessible such as Antacid battery, Lithium particle battery, Silver oxide battery, Nickel cadmium battery, Nickel metal hydride battery, etc. There are numerous types of capacitors like ceramic capacitor, mica capacitor, paper capacitor, electrolytic capacitor, electrochemical capacitor, super capacitor, half breed super capacitor,
Without constructing perfect lithium-ion battery equivalent model, the data-driven method can realize off-line SOC estimation based on the characteristics of battery charge/discharge data .As the mainstream of current data-driven methods, various neural network (NN) models are investigated for SOC estimation .Jiao et al. constructed a GRU
PDF | On Sep 1, 2019, Phuong-Ha La and others published A Single-Capacitor Equalizer Using Optimal Pairing Algorithm for Series-Connected Battery Cells | Find, read and cite all the research you
This paper proposes a method to predict the capacity of lithium-ion batteries with high accuracy. Four key features were extracted from current and voltage data obtained during
Battery Management System Algorithms: There are a number of fundamental functions that the Battery Management System needs to control and report with the help of algorithms. These
DOI: 10.3390/EN10050714 Corpus ID: 20033645; Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering @article{Song2017InsulationRM, title={Insulation Resistance
A novel online identification algorithm of lithium-ion battery parameters and model order based on a fractional order model Xiangdong Sun Jingrun Ji Biying Ren Guitao Chen Qi Zhang Department of Power Electronics and Motors, Xi''an University of Technology, Jinhua South Road, Xi''an, China Correspondence XiangdongSun,DepartmentofPowerElectronics
This article seeks to bridge these gaps by introducing and evaluating the application of some human-based algorithms in the parameter optimization of the Li-ion Battery 3rd Order ECM, highlighting ISGTOA as a reliable tool for accurate battery model parameter estimation. ISGTOA draws inspiration from classroom teaching dynamics but diverges by
Robust Control Algorithm for a Bi-directional EV Battery Charging System Ali Sharida1,3, Sertac Bayhan2, and Haitham Abu-Rub3 1 L is the DC side capacitance and R L is the load resistance
An active balancing method based on the state of charge (SOC) and capacitance is presented in this article to solve the inconsistency problem of lithium-ion batteries in electric vehicles. The...
Keywords: state of charge (SOC), second-order resistor-capacitance (RC) equivalent circuit model, extended Kalman filter algorithm, lithium-ion battery, MATLAB/simulink Citation: Xie J, Wei X, Bo X, Zhang P, Chen P, Hao W and Yuan M (2023) State of charge estimation of lithium-ion battery based on extended Kalman filter algorithm.
Based on the identified diffusion capacitance and the built relationship between diffusion capacitance and battery SOH, the battery SOH can be estimated. From Eq. (9), the maximum value of the fitness function is 100%. Due to the existence of noise of the measured current and terminal voltage, it is impossible to fit the curve with fitness value of 100%. In this
Accurately estimating the state of charge (SOC) and state of power (SOP) of the battery is essential for optimizing the use of electric quantity and ensuring the safe and efficient operation and energy management of the battery system of electric vehicles. In this paper, a particle swarm optimization algorithm is used to identify the model parameters of
In the analytic approach to the lumped capacitance model, a focus is placed on rapidly determining the thermal behavior of battery cells. It has been established that during charging and discharging at various C-rates, the heat generated in the battery cells arises predominantly from Joule''s heating loss, entropic phenomena, and polarization effects [17, 18],
Its model accuracy is high, it has five resistance and capacitance parameters to be identified, and the calculation amount is acceptable. Therefore, considering the complexity of the algorithm and the accuracy of the model, the second-order RC model is chosen in this paper. Fig. 1 is the second-order RC equivalent circuit model of a lithium-ion battery. UL and Uoc are the terminal
Pulse discharge and exponential fitting of lithium battery are used to obtain corresponding parameters. The simulation is carried out by using fixed resistance capacitance and variable resistance capacitor respectively. The accuracy of variable resistance and capacitance model is 2.9%, which verifies the validity of the proposed model.
Accurate and reliable SOC estimation plays a vital role in the engineering application and development of LIBs. A multi-time scale joint algorithm combining FFRLS and
Therefore there are a number of battery management system algorithms required to estimate, compare, publish and control. State of Charge. Abbreviated as SoC and defined as the amount of charge in the cell as a percentage compared to the nominal capacity of the cell in Ah. SoC Estimation Techniques . A look at the estimation of State of Charge (SoC) using voltage
Mathematical Modeling of Li-Ion Battery for Charge/Discharge Rate and Capacity Fading Characteristics using Genetic Algorithm Approach Kannan Thirugnanam, Himanshu Saini and Praveen Kumar
A battery parameter test platform is built to test the charge-discharge efficiency, open-circuit voltage and state of charge relationship curve, internal resistance and capacitance of the
Experimental results confirm that this method effectively reduces SOC disparities, enhancing both charging and discharging capacities. Additionally, to accurately predict battery
Data-driven algorithms are also widely used in battery fault diagnosis. Yao et al. developed an intelligent fault diagnosis algorithm for batteries based on support vector machines (SVM), and optimized the kernel function and penalty factor of support vector machine through cross-validation and grid search to achieve fault hierarchy management of battery
The method is a combination of regression shapelet, MRMR algorithm and XGBoost, where shapelet is used to quantify the shapelet distance for capturing battery degradation trend, MRMR algorithm is responsible for representative shapelet selection and XGBoost works as the final regressor mapping from shapelet distance to battery capacity.
ALGORITHM–BASED BATTERY STATE OF CHARGE ESTIMATE METHOD The constructed battery model in Battery Equivalent Circuit Model reflects the relationship between the battery
Circuit Model presents the battery equivalent circuit model. Extended Kalman Filter Algorithm Based Battery State of Charge Estimate Method illustrates the EKF algorithm–based battery SOC estimate method. Experiment Procedure and Results implements the experiment and gives the discussion. Finally, conclusion is drawn in Conclusion.
Battery algorithms play a vital role in hybrid and electric vehicle applications, since the accuracy of battery algorithms have a significant impact on the energy efficiency and the battery''s life. The energy management of hybrid and electric propulsion systems needs to rely on accurate information on the state of the battery (such as how much energy is left in the battery
Furthermore, in order to verify the structural importance of the FCNN algorithm, the fused 3DCNN, 3DCNN and 2DCNN algorithms with no battery capacity are used to estimate the SOC. The training data of 5# battery is described in detail below and the estimated results of all batteries are given at the end.
This enhances overall battery capacity, optimizing performance and extending operational range. Faster Balancing Speed: The algorithm prioritizes cells with the largest SOC differences, enabling a faster and more efficient balancing process than standard methods, which improves energy equalization during both charging and discharging.
The estimation method is applied to the balancing of batteries to improve the accuracy of estimating SOC. S. Meanwhile, the battery B charges the supercapacitor E through the inductance L. First, the switching tube S is on. The battery B supplies the balancing current. Then the switch is off, the energy stored in the
This paper proposes a SOC estimation algorithm, which successfully applies the 3DCNN algorithm to the SOC estimation of lithium-ion batteries, and innovatively uses the battery capacity as an input to improve the estimation accuracy of the SOC by the neural network.
The first layer uses a fused 3DCNN algorithm to estimate the battery capacity, and the second layer uses a 2DCNN algorithm and the new dataset for the SOC estimation. Different from other dataset construction methods, the battery capacity and SOC estimation in this paper require a small data length and discharge cycle.
In the output dataset, the battery capacity has been given in the data center provided by NASA. These data are calculated from the total power discharged after the end of each discharge cycle. Each discharge cycle corresponds to a battery capacity. According to the definition, the calculation of the SOC is as follows:
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