An Indirect State-of-Health Estimation Method Based on Improved Genetic and Back Propagation for Online Lithium-Ion Battery Used in Electric Vehicles
Author:
Affiliation:
1. School of Electrical Engineering, Xi'an University of Technology, Xi'an, China
2. School of Electrical Engineering and Electronics, University of Liverpool, Liverpool, U.K.
3. School of Engineering, University of Birmingham, Birmingham, U.K.
Funder
National Natural Science Foundation of China
Key Research and Development Program of Shaanxi Province
China Scholarship Council
State Scholarship Fund International Clean Energy Talent Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/25/9990993/09849057.pdf?arnumber=9849057
Reference39 articles.
1. Improved Genetic Algorithm to Optimize the Wi-Fi Indoor Positioning Based on Artificial Neural Network
2. An Adaptive Genetic Algorithm for Personalized Itinerary Planning
3. Two-Side Coalitional Matching Approach for Joint MIMO-NOMA Clustering and BS Selection in Multi-Cell MIMO-NOMA Systems
4. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve
5. A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain
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