A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur

By A. Fotouhi, D. Auger, K. Propp, S. Longo, Mark Wild. 2016

685 citations recorded by the source corpus.

Accurate prediction of range of an electric vehicle (EV) is a significant issue and a key market qualifier. EV range forecasting can be made practicable through the application of advanced modelling and estimation techniques. Battery modelling and state-of-charge estimation methods play a vital role in this area. In addition, battery modelling is essential for safe charging/discharging and optimal usage of batteries. Much existing work has been carried out on incumbent Lithium-ion (Li-ion) technologies, but these are reaching their theoretical limits and modern research is also exploring promising next-generation technologies such as Lithium–Sulphur (Li–S). This study reviews and discusses various battery modelling approaches including mathematical models, electrochemical models and electrical equivalent circuit models. After a general survey, the study explores the specific application of battery models in EV battery management systems, where models may have low fidelity to be fast enough to run in real-time applications. Two main categories are considered: reduced-order electrochemical models and equivalent circuit models. The particular challenges associated with Li–S batteries are explored, and it is concluded that the state-of-the-art in battery modelling is not sufficient for this chemistry, and new modelling approaches are needed.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c913d31dd3d7a7fb2ce75e13ba30e898e90f6b83

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In Automotive Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Overview of Lithium-Ion battery modeling methods for state-of-charge estimation in electrical vehicles (2018, background): “Typical empirical models.”
Failure statistics for commercial lithium ion batteries: A study of 24 pouch cells (2017, background): “(1) f(t), the failure probability density function pdf, which is the fraction of all of the cells in a population that fail on cycle t.”
A Comprehensive Study of Implemented International Standards, Technical Challenges, Impacts and Prospects for Electric Vehicles (2018, neutral): “batteries [68].”