A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations

By M. S. Lipu, M. Hannan, Aini Hussain, M. Hoque, P. Ker, M. Saad, A. Ayob. Journal of Cleaner Production, 2018

648 citations recorded by the source corpus.

Abstract Electric vehicles (EVs) have become increasingly popular due to zero carbon emission, reduction of fossil fuel reserve, comfortable and light transport. However, EVs employing lithium-ion battery are facing difficulties in terms of predicting accurate health and remaining useful life states due to various internal and external factors. Currently, very few papers are addressed to summarize the state of health (SOH) and remaining useful life (RUL) estimation approaches. In this regard, the goal of this paper is to comprehensively review the different estimation models to predict SOH, and RUL in a comparative manner. The results identify the classifications, characteristics and evaluation processes with advantages and disadvantages for EV applications. The review also investigates the issues and challenges with possible solutions. Furthermore, the review provides some selective proposals for the further technological development of SOH, and RUL estimation for lithium-ion batteries. All the highlights insight this review will hopefully lead to the increasing efforts towards the development of the advanced SOH and RUL methods for future EV uses.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/68b33453636657ecb7aa8295e718859b1d60a125

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Citations in the corpus

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “[7,21], data-driven approaches are still not covered in depth.”
Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries (2020, background): “On one hand, some review papers have summarized these state-of-the-art applications [22], [23], concluding that several limitations still exist as: 1) data-driven approaches are mainly used to capture battery cyclic aging states but very few attempts have been done for calendar aging diagnosis.”
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, background): “The BMS is requested to estimate the state of health (SOH) accurately and reliably in order to monitor the battery aging conditions and provide guidance for the second use [6].”