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🔥 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. The Kalman filtering algorithm estimates the state of charge (SOC) of a battery by treating SOC as an internal state variable of the battery system, achieving minimum variance estimation of SOC through a recursive algorithm. During the implementation of the algorithm, it maintains good accuracy and has a strong corrective effect on initial value errors, as well as a strong suppression effect on noise. However, since the battery model is nonlinear, the Kalman filtering algorithm cannot be directly used to estimate SOC. Therefore, a common approach is to use the extended Kalman filtering algorithm to estimate SOC. The extended Kalman filtering 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, a linear Taylor approximation is also made for the measurement equations 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 corrections, 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 Supply Technology, 2016, 40(9):5. DOI:10.3969/j.issn.1002-087X.2016.09.030.
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🏆 The team specializes in guiding customized MATLAB simulations in various research fields, helping to realize research dreams:
🌟 Various intelligent optimization algorithm improvements and applications
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, 3D 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 distributed generation unit allocation, 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), oil-electric hybrid vehicle routing planning, hybrid 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 localization, cold chain, time windows, multi-parking lots, etc., site selection optimization, port bridge scheduling optimization, traffic impedance, redistribution, parking space allocation, airport flight scheduling, communication upload and download allocation optimization
🌟 Machine learning and deep learning time series, regression, classification, clustering, and dimensionality reduction
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
Covering 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, subway parking precision prediction, 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.), drone 3D path planning, drone collaboration, drone formation, robot path planning, grid map path planning, multimodal transport problem, electric vehicle routing planning (EVRP), two-layer vehicle routing planning (2E-VRP), oil-electric 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, drone secure communication trajectory online optimization, vehicle collaborative drone path planning,
🌟 In communication
Sensor deployment optimization, communication protocol optimization, routing optimization, target localization optimization, Dv-Hop localization optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI localization 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, EMG signals, EEG signals, signal timing optimization, ECG 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/incremental conductance 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, trajectory association, trajectory 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)
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