Article
Thermodynamics
Song Xiao, Puyang Liu, Kui Chen, Kai Liu, Guoqiang Gao, Guangning Wu
Summary: This paper proposes a method for accurately predicting the capacity of lithium-ion batteries using a backpropagation neural network model optimized by a genetic algorithm. By extracting relevant health factors from the battery discharge curve, analyzing these factors, and comparing them with different optimization algorithms, the accuracy and effectiveness of the method are demonstrated.
INTERNATIONAL JOURNAL OF GREEN ENERGY
(2023)
Article
Thermodynamics
Jianping Wen, Xing Chen, Xianghe Li, Yikun Li
Summary: In this paper, a battery SOH prediction model based on incremental capacity analysis and BP neural network is proposed to predict battery SOH at different ambient temperatures. By analyzing the correlation between the characteristics of IC curve and SOH, the mapping relationship between temperature and IC curve characteristics is established, and the SOH prediction model at different temperatures is obtained. The accuracy of the model is verified by comparing the model test results and experimental results.
Article
Energy & Fuels
Fan Zhang, Zi-xuan Xing, Ming-hu Wu
Summary: This paper proposes a SOH estimation scheme based on BP neural network optimized by a genetic algorithm (GA-BP) and fixed characteristic voltage interval to deal with the randomness of battery data and achieve SOH estimation on random length data.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Jungsoo Kim, Huiyong Chun, Jongchan Baek, Soohee Han
Summary: This paper proposes a novel parameter identification method for lithium-ion batteries using a neural network and genetic algorithm, resulting in more accurate and reliable identification of electrochemical model parameters with high data efficiency.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Wei-Jen Lin, Kuo-Ching Chen
Summary: Developing an accurate and high-efficiency parameter identification method is crucial for predicting the state of health of lithium ion batteries. This study proposes using the first-order derivative of the discharge curve as another fitting target and employs genetic algorithm and deep neural network for multi-objective optimization.
Article
Energy & Fuels
Hong Xu, Shunli Wang, Yongcun Fan, Jialu Qiao, Wenhua Xu
Summary: This study proposes an improved Drosophila algorithm combined with BP neural network to estimate the SOC of lithium-ion batteries. The improved algorithm shows better performance and estimation accuracy compared to traditional algorithms and other commonly used functions.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Xinzhe Yin, Jinghua Li, Shoujun Huang
Summary: This study designed an economic early warning system based on improved genetic and BP hybrid algorithm and neural network, which can accurately reflect actual economic fluctuations with good accuracy.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Energy & Fuels
Chun Chang, Qiyue Wang, Jiuchun Jiang, Tiezhou Wu
Summary: This paper proposes an online method based on incremental capacity and wavelet neural networks, which can estimate the health status of the battery under current discharge. By extracting important variables from IC curves and optimizing the WNN model parameters using genetic algorithm, the SOH of the battery is successfully estimated with an error of less than 3%.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Computer Science, Information Systems
Jiasheng Tian, Jian Shi
Summary: In this article, a method based on the genetic algorithm and the Back-Propagation neural network (GABP) is proposed to improve the accuracy and speed of retrieving atmospheric parameters. The genetic algorithm is applied to optimize the connection weights and thresholds of the BP neural network, and the network is trained again to obtain an ideal model. The tested results show that GABP has better accuracy and faster convergence speed in retrieving atmospheric parameters compared to other algorithms.
Article
Computer Science, Artificial Intelligence
Anrong Xue, Wanlin Yang, Xueming Yuan, Binpeng Yu, Chaofeng Pan
Summary: This paper proposes a novel estimation algorithm for the state of health (SOH) of electric vehicle batteries based on particle filter (PF), quantum genetic algorithm (QGA), and generalized regression neural network (GRNN). The algorithm achieves high accuracy and low computational cost for SOH estimation under actual operating conditions through optimized model structure and parameters.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Chenxi Li, Zhaohui Li, Meng Wu
Summary: In this article, the application of supply chain information sharing and genetic algorithm back propagation neural networks in supply chain management is studied. The concepts and characteristics of supply chain information sharing and genetic algorithm back propagation neural networks are explained. The feasibility of using BP neural network to evaluate supply chain information sharing is analyzed. An assessment indicator system for supply chain information sharing is constructed from four aspects. The principle and algorithm of BP neural network are analyzed, and a network model is constructed.
Article
Thermodynamics
B. Paknezhad, M. Vakili, M. Bozorgi, M. Hajialibabaie, M. Yahyaei
Summary: The study predicts the thermal conductivity of silver nanofluid coated with PVP using a combinational model of multilayer perceptron artificial neural network and genetic algorithm, showing high accuracy and reliability.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2021)
Article
Computer Science, Information Systems
Yanhua Lu, Xuehui Gong, Andrew Byron Kipnis
Summary: This study combines neural networks and grey correlation method to analyze the energy consumption of individual buildings in universities, establishing a reliable model for qualitative analysis.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Electrochemistry
Yang Li, Shunli Wang, Lei Chen, Peng Yu, Xianpei Chen
Summary: In this paper, a lithium-ion battery SOC estimation method based on the Improved Sparrow Search Algorithm (ISSA) optimized BP neural network is proposed. The method is validated through simulation experiments and provides a reliable basis for monitoring the status of other important batteries.
INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE
(2022)
Article
Thermodynamics
Yukuan Song, Zhijun Lei, Xin-Gen Lu, Gang Xu, Junqiang Zhu
Summary: A Sequential Approximate Optimization (SAO) framework using BP neural network and Genetic Algorithm is established for multi-objective optimization of lobed mixer. Design parameters of lobe wavelength to height (eta) and rise angle (alpha) are selected, with optimization objectives of mixing efficiency, thrust, and total pressure loss. Using CFX solver and SST turbulence model, a tetrahedral unstructured grid with 5.6 million cells achieves similar global results. The study shows that reducing alpha while appropriately increasing eta improves thrust and reduces losses, and the non-normalized method is more suitable for the multi-objective optimization problem of lobed mixer. The optimized lobed mixer reduces geometric dimensions and increases efficiency while maintaining thrust.
JOURNAL OF THERMAL SCIENCE
(2023)
