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portfolio

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publications

Online RNN Model for SOC Prediction in Next Generation Hybrid Car Batteries

Published in Journal 1, 2020

This article was submitted for publication and details a Vanilla LSTM applied to a Battery This article was submitted for publication and details a Vanilla LSTM applied to a Battery time-series dataset

Recommended citation: Citation pending http://Tov-Nephesh.github.io/files/Article1.pdf

ONLINE STATE OF CHARGE PREDICTION IN NEXT GENERATION VEHICLE BATTERIES USING DEEP RECURRENT NEURAL NETWORKS AND CONTINUOUS MODEL SIZE CONTROL

Published in Journal of Energy and Power Technology, 2020

This paper is a continuation of the study from article 1. It takes a deeper dive into the topic of Deep Learning for a HEV application.

Recommended citation: Hespeler, Steven. (2020). "Paper Title Number 2." Journal 1. 1(2). http://tov-nephesh.github.io/files/JEPT_article_v4[9390].pdf

talks

An Intelligent Predictive Algorithm Utilized to Assess Influential Factors Effecting the Recharge Process of a Zinc Polyiodide Flow Battery

Published: October 20, 2016

I presented at the 1st International Workshop on Quality and Productivity, located at the Universidad Autonoma de BajaCalifornia, in Ensendada, Baja California, MX. This presentation was focused on presenting Random Forest techniques for identifying the most influential physical battery parameters in a predictive battery model.

Implementation of Machine Learning Techniques to Predict the Prominent Factors Effecting the Life Cycle of a Flow Battery

Published: December 10, 2016

This presentation was performed for the 1st Colloquium of Engineering and Technology, located at the Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, MX. Here I presented Machine Learning techniques for identifying and quantifying parameter importance for batteries.

Steven C. Hespeler