DocumentCode :
1943655
Title :
Battery pack state of charge estimator design using computational intelligence approaches
Author :
Peng, Jinchun ; Chen, Yaobin ; Eberhart, Russ
Author_Institution :
Dept. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
fYear :
2000
fDate :
11-14 Jan. 2000
Firstpage :
173
Lastpage :
177
Abstract :
This paper presents a novel design of a battery pack state of charge (SOC) estimator for electric vehicles using computational intelligence techniques. The main framework of the estimator is a three-layer feedforward neural network with four inputs and one output (estimated SOC). The inputs are the battery pack current, accumulated ampere hours, average pack temperature and minimum voltage of the battery modules. A strategy is developed to select the training data set from a large amount of the original testing data sets under different drive cycles and operating conditions. A modified particle swarm optimization (PSO) algorithm is used to train the proposed neural network. The designed SOC estimator is validated and evaluated using the testing data under different drive profiles and temperatures. The errors of the SOC estimates are well within the acceptable range compared to that obtained by using traditional mathematical models. The resulting SOC estimator is computationally efficient and can be easily implemented using low-cost microprocessors.
Keywords :
battery testers; computerised monitoring; electric vehicles; feedforward neural nets; learning (artificial intelligence); secondary cells; accumulated ampere hours; average pack temperature; battery pack current; battery pack state-of-charge estimator design; computational intelligence; computational intelligence approaches; drive profiles; electric vehicles; mathematical models; microprocessors; particle swarm optimization algorithm; temperatures; three-layer feedforward neural network; training data set; Batteries; Computational intelligence; Electric vehicles; Feedforward neural networks; Neural networks; State estimation; Temperature; Testing; Training data; Voltage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Battery Conference on Applications and Advances, 2000. The Fifteenth Annual
Conference_Location :
Long Beach, CA, USA
ISSN :
1089-8182
Print_ISBN :
0-7803-5924-0
Type :
conf
DOI :
10.1109/BCAA.2000.838400
Filename :
838400
Link To Document :
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