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š„ Content Introduction
Lithium-ion batteries serve as the core energy carrier for new energy vehicles, energy storage systems, and portable electronic devices. Their State of Charge (SOC) directly reflects the remaining battery capacity and is a key basis for battery management systems (BMS) to achieve safe control, range prediction, and lifespan optimization. However, SOC cannot be directly measured by sensors and must be estimated indirectly through measurable parameters such as voltage, current, and temperature. The estimation process is easily affected by factors such as battery aging, temperature drift, charge/discharge rate changes, and complex operating conditions, leading to insufficient accuracy of traditional methods (e.g., open-circuit voltage method, ampere-hour integration method) in dynamic scenarios.
With breakthroughs in deep learning technology in the field of time series prediction, improved models based on the Transformer architecture provide new directions for SOC estimation. Among them, the Basisformer model introduces a “basis function decomposition” mechanism, effectively reducing computational complexity while retaining the long-sequence modeling capability of Transformers, addressing overfitting and efficiency issues under high noise and multi-coupling characteristics of battery data. This article will systematically construct a lithium-ion battery SOC prediction model based on Basisformer using PyTorch as the development framework, presenting the technical implementation path from data preprocessing, model design, training optimization to performance validation, providing an engineering reference for high-precision SOC estimation research.
II. Core Fundamentals of Lithium-Ion Battery SOC Estimation
2.1 Definition of SOC and Estimation Challenges
IV. Implementation of Basisformer-SOC Model Based on PyTorch
Using the publicly available lithium-ion battery dataset ā NASA PCoE Battery Dataset (which includes cycling data of 18650 lithium-ion batteries at different temperatures and rates), or a self-made laboratory dataset (collected through battery testing systems such as Arbin BT2000, which includes:
- Dynamic conditions: UDDS (United States Urban Driving Cycle), FTP75 (Federal Test Procedure);
- Static conditions: Constant current charge/discharge (0.5C charging, 1C discharging), pulse charge/discharge (5C pulse for 10s, rest for 30s);
- Data dimensions: Timestamp (s), Voltage (V), Current (A), Temperature (Ā°C), SOC labels (obtained through ampere-hour integration + OCV calibration, serving as ground truth).
ā³ļø Operating Results
š References
[1] Han Bochong, Song Yihan, Zhao Yongheng. A Stellar Spectrum Classification Method Based on Multi-Scale Feature Fusion [J]. Spectroscopy and Spectral Analysis, 2024, 44(8):2284-2288.
[2] Jing Yuyang, Zhang Liqiang. Implementation and Application of Long Short-Term Memory Networks Based on Pytorch [J]. Automation in Manufacturing, 2021(12).
[3] Lin Hong. Research on Image Restoration Algorithms Based on Deep Learning [D]. Guizhou University, 2021.
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Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics site selection, cargo position 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 life 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 optimization portfolio, cloud server combination optimization, antenna linear array distribution optimization, CVRP problem, VRPPD problem, multi-center 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 path planning problem, port scheduling, port shore bridge scheduling, parking space allocation, airport flight scheduling, leak source localization.
š 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 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 system 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 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.
š In image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compressed sensing.
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Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), drone three-dimensional path planning, 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.
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Drone path planning, drone control, drone formation, drone collaboration, drone task allocation, drone secure communication trajectory online optimization, vehicle collaborative drone path planning.
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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 acoustic communication, communication upload and download allocation.
š 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.
š In power systems
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration, orderly charging, MPPT optimization, household electricity.
š In cellular automata
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š In radar
Kalman filter tracking, trajectory association, trajectory fusion, SOC estimation, array optimization, NLOS recognition.
š 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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