Journal
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
Volume 2, Issue 2, Pages 140-149Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TTE.2015.2512237
Keywords
Battery management; electric vehicle (EV); energy storage; machine learning; state estimation
Funding
- National Natural Science Foundation of China [51575064]
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To fulfill reliable battery management in electric vehicles (EVs), an advanced State-of-Charge (SOC) estimator is developed via machine learning methodology. A novel genetic algorithm-based fuzzy C-means (FCM) clustering technique is first used to partition the training data sampled in the driving cycle-based test of a lithium-ion battery. The clustering result is applied to learn the topology and antecedent parameters of the model. Recursive least-squares algorithm is then employed to extract its consequent parameters. To ensure good accuracy and resilience, the backpropagation learning algorithm is finally adopted to simultaneously optimize both the antecedent and consequent parts. Experimental results verify that the proposed estimator exhibits sufficient accuracy and outperforms those built by conventional fuzzy modeling methods.
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