Volume 03: Proceedings of 11th International Conference on Applied Energy, Part 2, Sweden, 2019

Big Data Driven Lithium-ion Battery Modeling Method: A Deep Transfer Learning Approach Shuangqi Li, Hongwen He*, Jianwei Li

Abstract

Battery Is the Bottleneck Technology of Electric Vehicles (Evs), Which Has Complex and Hardly Observable Inside Chemical Reactions. to Reduce the Training Data Volume Requirement in Artificial Intelligent Algorithm Based Battery Model, This Paper Presents a Deep Transfer Learning Algorithm Based Battery Modeling Method. the Deep Belief Network – Extreme Learning Machine (Dbnelm) Algorithm Is Used for Battery Modeling Issue in This Paper to Excavate the Hidden Features in Battery Data Set and Improve the Accuracy and Stability. the Results Show That the Proposed Transfer Learning Algorithm Based Battery Modeling Method Is Able to Achieve a Highly Accurate Simulation for Battery Dynamic Characteristics Under an Insufficient Data Set, and the Mean Absolute Percentage Error of the Established Model Is Within 3%.

Keywords electric vehicle, lithium-ion battery, modeling, deep learning, transfer learning.

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