✅ Author Profile: A research enthusiast and Matlab simulation developer, skilled in data processing, modeling simulation, program design, complete code acquisition, paper reproduction, and scientific simulation.
🍎 Previous reviews can be found on my personal homepage:Matlab Research Studio
🍊 Personal motto: Seek knowledge through investigation; complete Matlab code and simulation consultation available via private message.
🔥 Content Introduction
The Kalman filtering method is currently one of the most commonly used methods for SOC estimation, with a large amount of literature focused on improving the Kalman filtering method. In estimating the state of charge (SOC) of a battery, the SOC is treated as an internal state variable of the battery system, and the recursive algorithm achieves the minimum variance estimation of SOC. During the implementation of the algorithm, it maintains good accuracy and has a strong correction effect on the error of the initial value, as well as a strong suppression effect on noise. However, since the battery model is nonlinear, the Kalman filtering method cannot be directly used to estimate SOC. Therefore, a common approach is to use the Extended Kalman Filtering (EKF) algorithm to estimate SOC. The EKF algorithm adds a linearization step during the derivation of the filtering equations of the Kalman filtering algorithm: during state estimation, a real-time linear Taylor approximation is made at the estimated value of the previous state for the system equations; during prediction, the measurement equations are also linearly approximated at the corresponding predicted position.
In addition, this group fully considers the impact of temperature and charge/discharge rates on the algorithm, setting a composite Kalman gain correction coefficient, and based on fuzzy control theory, updates the observation matrix in the extended filtering with time updates and measurement correction values, ensuring the accuracy of the SOC estimation algorithm.
⛳️ Results
📣 Sample Code
for k=1:N
%if I(k)<=0
% in=I(k);
% ip=0;
%else
% in=0;
% ip=I(k);
% end
y(k)=X(1)+X(2)/SOC(k)+X(3)*SOC(k)+X(4)*log(SOC(k))+X(5)*log(1-SOC(k))+X(6)*I(k);
Err_Messure(k)=abs(y(k)-U(k));
🔗 References
[1] Lin Cheng, Zhang Xiaohua, Xiong Rui. SOC estimation of power batteries based on fuzzy Kalman filtering algorithm [J]. Power Technology, 2016, 40(9):5. DOI:10.3969/j.issn.1002-087X.2016.09.030.
Search for code tips on WeChat public account: Must be done on a mobile device. Here are the specific steps:
1. Open the WeChat public account and navigate to the desired public account where the article is located
2. After entering the public account, click the small person icon in the upper right corner
3. Click the search icon in the upper right corner
4. Search for the content you want to find in the search bar, and you can view all articles containing that keyword. For example, search for the keyword “drone” below
🎈 Some theoretical references are from online literature; if there is any infringement, please contact the author for removal.
👇 Follow me to receive a wealth of Matlab e-books and mathematical modeling materials
🏆 Our team specializes in guiding customized MATLAB simulations in various research fields, helping to realize research dreams:
🌟 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics site selection, cargo location optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, container ship loading optimization, pump combination optimization, medical resource allocation optimization, facility layout optimization, visual domain base station and drone site selection optimization, knapsack problem, wind farm layout, time slot allocation optimization, optimal distribution of distributed generation units, multi-stage pipeline maintenance, factory-center-demand point three-level site selection problem, emergency supply material distribution center site selection, base station site selection, road lamp post arrangement, hub node deployment, transmission line typhoon monitoring devices, container scheduling, unit optimization, investment portfolio optimization, cloud server combination optimization, antenna linear array distribution optimization, CVRP problem, VRPPD problem, multi-center VRP problem, multi-layer network VRP problem, multi-center multi-vehicle VRP problem, dynamic VRP problem, two-layer vehicle routing planning (2E-VRP), electric vehicle routing planning (EVRP), hybrid vehicle routing planning, mixed flow shop problem, order splitting scheduling problem, bus scheduling optimization problem, flight shuttle vehicle scheduling problem, site selection routing planning problem, port scheduling, port bridge scheduling, parking space allocation, airport flight scheduling, leak source location, cold chain, time windows, multi-vehicle fields, site selection optimization, port bridge scheduling optimization, traffic impedance, redistribution, parking space allocation, airport flight scheduling, communication upload and download allocation optimization
🌟 Time series, regression, classification, clustering, and dimensionality reduction in machine learning and deep learning
2.1 BP time series, regression prediction, and classification
2.2 ENS voice neural network time series, regression prediction, and classification
2.3 SVM/CNN-SVM/LSSVM/RVM support vector machine series time series, regression prediction, and classification
2.4 CNN|TCN|GCN convolutional neural network series time series, regression prediction, and classification
2.5 ELM/KELM/RELM/DELM extreme learning machine series time series, regression prediction, and classification
2.6 GRU/Bi-GRU/CNN-GRU/CNN-BiGRU gated neural network time series, regression prediction, and classification
2.7 Elman recurrent neural network time series, regression prediction, and classification
2.8 LSTM/BiLSTM/CNN-LSTM/CNN-BiLSTM long short-term memory neural network series time series, regression prediction, and classification
2.9 RBF radial basis function neural network time series, regression prediction, and classification
2.10 DBN deep belief network time series, regression prediction, and classification
2.11 FNN fuzzy neural network time series, regression prediction
2.12 RF random forest time series, regression prediction, and classification
2.13 BLS broad learning time series, regression prediction, and classification
2.14 PNN pulse neural network classification
2.15 Fuzzy wavelet neural network prediction and classification
2.16 Time series, regression prediction, and classification
2.17 Time series, regression prediction, and classification
2.18 XGBOOST ensemble learning time series, regression prediction, and classification
2.19 Transform various combinations of time series, regression prediction, and classification
Directions cover wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health state prediction, electricity consumption prediction, water body optical parameter inversion, NLOS signal recognition, precise prediction of subway stops, transformer fault diagnosis
🌟 In image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compressed sensing
🌟 In path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), three-dimensional path planning for drones, drone collaboration, drone formation, robot path planning, grid map path planning, multimodal transport problems, electric vehicle routing planning (EVRP), two-layer vehicle routing planning (2E-VRP), hybrid vehicle routing planning, ship trajectory planning, full path planning, warehouse patrol, bus time scheduling, reservoir scheduling optimization, multimodal optimization
🌟 In drone applications
Drone path planning, drone control, drone formation, drone collaboration, drone task allocation, online optimization of safe communication trajectories for drones, vehicle collaborative drone path planning,
🌟 In communication
Sensor deployment optimization, communication protocol optimization, routing optimization, target location optimization, Dv-Hop location optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI location optimization, underwater communication, communication upload and download allocation
🌟 In signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, electromyography signals, electroencephalography signals, signal timing optimization, electrocardiogram signals, DOA estimation, encoding and decoding, variational mode decomposition, pipeline leakage, filters, digital signal processing + transmission + analysis + denoising, digital signal modulation, bit error rate, signal estimation, DTMF, signal detection
🌟 In power systems
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration, orderly charging, MPPT optimization, household electricity, electric/cold/heat load forecasting, power equipment fault diagnosis, battery management system (BMS) SOC/SOH estimation (particle filtering/Kalman filtering), multi-objective optimization in power system scheduling applications, photovoltaic MPPT control algorithm improvement (perturbation observation method/conductance increment method), electric vehicle charging and discharging optimization, microgrid day-ahead optimization, energy storage optimization, household electricity optimization, supply chain optimization
🌟 In cellular automata
Traffic flow, crowd evacuation, virus spread, crystal growth, metal corrosion
🌟 In radar
Kalman filtering tracking, track association, track fusion, SOC estimation, array optimization, NLOS recognition
🌟 In workshop scheduling
Zero-wait flow shop scheduling problem (NWFSP) , permutation flow shop scheduling problem (PFSP) , hybrid flow shop scheduling problem (HFSP) , zero idle flow shop scheduling problem (NIFSP), distributed permutation flow shop scheduling problem (DPFSP), blocking flow shop scheduling problem (BFSP)
👇