Battery Management System Demonstrator Board design using EMC System simulation

Doridant, A.; Abouda, Kamel; Givelin, Philippe; Thibaud, B.

doi:10.1109/emceurope.2019.8872061

Cited by 15 publications

(12 citation statements)

References 6 publications

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“…The current flowing through series lithium-ion batteries must be strictly equal, but differences between series batteries can cause the battery to overcharge or discharge during the charge and discharge process [ 41 , 42 ]. This will make a significant impact on battery life, battery safety, and battery capacity utilization efficiency.…”

Section: Discussionmentioning

confidence: 99%

A Battery SOC Estimation Method Based on AFFRLS-EKF

Zhang

et al. 2021

Sensors

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The lithium-ion battery is the key power source of a hybrid vehicle. Accurate real-time state of charge (SOC) acquisition is the basis of the safe operation of vehicles. In actual conditions, the lithium-ion battery is a complex dynamic system, and it is tough to model it accurately, which leads to the estimation deviation of the battery SOC. Recursive least squares (RLS) algorithm with fixed forgetting factor is widely used in parameter identification, but it lacks sufficient robustness and accuracy when battery charge and discharge conditions change suddenly. In this paper, we proposed an adaptive forgetting factor regression least-squares—extended Kalman filter (AFFRLS–EKF) SOC estimation strategy by designing the forgetting factor of least squares algorithm to improve the accuracy of SOC estimation under the change of battery charge and discharge conditions. The simulation results show that the SOC estimation strategy of the AFFRLS–EKF based on accurate modeling can effectively improve the estimation accuracy of SOC.

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Section: Discussionmentioning

confidence: 99%

A Battery SOC Estimation Method Based on AFFRLS-EKF

Zhang

et al. 2021

Sensors

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“…where σ is the standard deviation of the measurement noise and h k is the capacity fading model in Equation (1). Equation ( 2) expresses the likelihood by considering a Gaussian distribution of the measurement noise of a single particle.…”

Section: Data-based Pf For Comparisonmentioning

confidence: 99%

“…A malfunction of batteries and their reduced lifetime that hinders an adequate energy supply to drive the vehicle may lead to accidents in the worst case. Accordingly, various approaches have been proposed to increase the lifetime of batteries while maintaining the size of a battery pack and protecting from electromagnetic interferences (EMIs) [1,2]. Such various efforts have improved the performance and safety of batteries used in electric vehicles, but there is still a possibility of malfunction at any time.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics

et al. 2021

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We propose a robust and reliable method based on deep neural networks to estimate the remaining useful life of lithium-ion batteries in electric vehicles. In general, the degradation of a battery can be predicted by monitoring its internal resistance. However, prediction under battery operation cannot be achieved using conventional methods such as electrochemical impedance spectroscopy. The battery state can be predicted based on the change in the capacity according to the state of health. For the proposed method, a statistical analysis of capacity fade considering the impedance increase according to the degree of deterioration is conducted by applying a deep neural network to diverse data from charge/discharge characteristics. Then, probabilistic predictions based on the capacity fade trends are obtained to improve the prediction accuracy of the remaining useful life using another deep neural network.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

2021

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Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. 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. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.

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Battery Management System Demonstrator Board design using EMC System simulation

Cited by 15 publications

References 6 publications

A Battery SOC Estimation Method Based on AFFRLS-EKF

A Battery SOC Estimation Method Based on AFFRLS-EKF

Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics

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

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