Electromagnetic Susceptibility of Battery Management Systems’ ICs for Electric Vehicles: Experimental Study

Abstract

The paper deals with the susceptibility to electromagnetic interference (EMI) of battery management systems (BMSs) for Li-ion and lithium-polymer (LiPo) battery packs employed in emerging electric and hybrid electric vehicles. A specific test board was developed to experimentally assess the EMI susceptibility of a BMS front-end integrated circuit by direct power injection (DPI) and radiated susceptibility measurements in an anechoic chamber. Experimental results are discussed in reference to the different setup, highlighting the related EMI-induced failure mechanisms observed during the tests.

Multiple Sensor Fail-Operational Architecture for Electric Vehicle Powertrain Control System

“…First, it calculates the Kalman gain K k using Equation (19). Next, it corrects the values ofx − k from ( 17) by ( 20) and update the covariance matrix P − k from ( 18) by (21), where H is the measurement Jacobian matrix.…”

“…The common BMS fault can be classified into internal and external faults [19,20]. Internal faults include the characteristic changes of battery due to the overcharge, overdischarge, overheating, thermal runaway, electromagnetic interference induced failure [21], and other behaviors, while the external faults consist of the abnormalities in the cooling system, wiring, or sensors. Many studies have adopted the model-based method to detect sensor faults.…”

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

“…Owing to their high energy density, high power density, long service life, environmental friendliness and low self-discharge rate, lithium-ion batteries (LIBs) have become the prime energy storage system for many applications such as electric vehicles (EVs), gridlevel power storage and several other consumer electronics [1,2]. However, the safe and reliable operating area of the LIB is very narrow, which necessitates a battery management system (BMS) for effective operational control, protection and energy management [3][4][5][6][7]. In addition, due to the limitation of the cell voltage and storage capacity of a single LIB cell, high power applications of LIBs such as EVs and grid-tied energy storage systems require hundreds or even thousands of single battery cells [8].…”

A Battery SOC Estimation Method Based on AFFRLS-EKF

“…The application of machine learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health, and remaining useful life [12]. Electromagnetic interference (EMI) of battery management systems (BMSs) will cause measurement errors of current and voltage signals, which will affect the performance of BSM [13][14][15][16][17][18][19]. For the situation that battery working conditions change rapidly, a recursive calculation method based on the Kalman filter is adopted [20][21][22].…”