State estimation for advanced battery management: Key challenges and future trends

By Xiaosong Hu, F. Feng, Kailong Liu, Lei Zhang, Jiale Xie, Bo Liu. Renewable & Sustainable Energy Reviews, 2019

776 citations recorded by the source corpus.

Abstract Batteries are presently pervasive in portable electronics, electrified vehicles, and renewable energy storage. These indispensable engineering applications are all safety-critical and energy efficiency-demanding such that batteries must be meticulously monitored and manipulated, where effectively estimating the internal battery states is a key enabler. The primary goal of this paper is to present a concise, understandable overview of existing methods, key issues, technical challenges, and future trends of the battery state estimation domain. More specifically, for the first time, the state of the art in State of Charge (SOC), State of Energy (SOE), State of Health (SOH), State of Power (SOP), State of Temperature (SOT), and State of Safety (SOS) estimation is all elucidated in a tutorial yet systematical way, along with existing issues exposed. In addition, from six different viewpoints, some future important research opportunities and evolving trends of this prosperous field are disclosed, in order to stimulate more technologically innovative breakthroughs in SOC/SOE/SOH/SOP/SOT/SOS estimation.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2d2752d7ceec906b40ca9ba223182fe026be983e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Kailong Liu
2. Xianke Lin
3. Simona Onori
4. Xiaosong Hu
5. Yunhong Che
6. Kai Zhang
7. Satadru Dey

In Electrical and Electronic Engineering

1. Kailong Liu
2. Xianke Lin
3. Simona Onori
4. Xiaosong Hu
5. Yunhong Che
6. Kai Zhang
7. Satadru Dey

Citations in the corpus

• Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, background): “3017090 density, as well as low self-discharge rate [2], [3].”
• Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures (2020, background): “A current sensor fault affects the accuracy of the SOC estimation [14] and multistate estimation [15], [16].”