A brief review on key technologies in the battery management system of electric vehicles

By Kailong Liu, Kang Li, Qiao Peng, Cheng Zhang. Frontiers of Mechanical Engineering, 2018

525 citations recorded by the source corpus.

Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c4dd22ef66839320f3bbf0c697a940b34e1fd1eb

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Citations in the corpus

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “While ML methods can be utilized for both SOH estimation and RUL prediction[122,123], there is a large difference between the two applications in terms of input features and the desired output.”
Review of Battery Management Systems (BMS) Development and Industrial Standards (2021, background): “The objective is to determine system safety and reliability for normal functionality and emergencies [37].”
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.”
A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery (2021, background): “tests, cycle-life models also become another hot research field to predict battery RUL [8].”
Inductive Wireless Power Transfer Charging for Electric Vehicles–A Review (2021, background): “Over charging, over-voltage or over-current affects the on the battery lifespan and sometimes even lead to issues like fire or explosions [279].”
Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications (2023, methodology): “electric car models have made extensive use of both Li-ion batteries and nickel-metal hydride (Ni-MH) batteries [3].”