DocumentCode :
3198473
Title :
Battery State-of-charge Estimation Based on Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm
Author :
Jianxun Lv ; Haiwen Yuan ; Yingming Lv
Author_Institution :
Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing, China
fYear :
2012
fDate :
8-10 Dec. 2012
Firstpage :
22
Lastpage :
27
Abstract :
The battery state-of-charge (BSOC), which is a key reference for battery management, can not be measured directly. An estimation approach for the SOC of the individual lithium-ion battery cell used in applications where the battery load is very unstable is studied. The estimator model based on fuzzy neural network (FNN) is proposed, considering the battery terminal voltage, discharge current and battery surface temperature as inputs. Furthermore, an improved particle swarm optimization (PSO) algorithm borrowing the operation of selection and crossover from genetic algorithm (GA) is developed to train the FNN-based estimator model. This hybrid algorithm can incorporate the superiorities of the two heuristic optimization techniques. The results of simulation and experiment demonstrate that the improved PSO algorithm is more adaptive to the initial value of the parameters than the traditional training method using the back propagation (BP) algorithm. The FNN model trained by the improved PSO algorithm has better generalization capacity. At the same class of the training accuracy, higher accurate estimation value of BSOC can be obtained by employing the improved PSO algorithm. This study proposes an effective approach for estimating the BSOC.
Keywords :
backpropagation; battery management systems; fuzzy neural nets; genetic algorithms; lithium; particle swarm optimisation; power engineering computing; secondary cells; BP algorithm; BSOC; FNN-based estimator model; Li; backpropagation algorithm; battery management; battery state-of-charge estimation; battery surface temperature; battery terminal voltage; discharge current; fuzzy neural network; genetic algorithm; heuristic optimization techniques; improved PSO algorithm; lithium-ion battery cell; particle swarm optimization algorithm; training method; Batteries; Fuzzy neural networks; Mathematical model; Prediction algorithms; Sociology; Statistics; Training; battery; estimation; fuzzy neural network; improved particle swarm optimization algorithm; state-of-charge;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Instrumentation, Measurement, Computer, Communication and Control (IMCCC), 2012 Second International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4673-5034-1
Type :
conf
DOI :
10.1109/IMCCC.2012.13
Filename :
6428845
Link To Document :
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