Intelligent Manufacturing Process Model of Electric
PDF | On Sep 1, 2021, Cong Zhang and others published Intelligent Manufacturing Process Model of Electric Vehicle Battery Pack and Experimental Verification | Find, read and cite all the research
Anti-interference lithium-ion battery intelligent perception for
Therefore, this article presents an anti-interference lithium-ion battery intelligent perception (ALBIP) model for identifying and classifying thermal fault cells in battery packs, as
An intelligent diagnosis method for battery pack connection faults
Reliable Online Internal Short Circuit Diagnosis on Lithium-Ion Battery Packs via Voltage Anomaly Detection Based on the Mean-Difference Model and the Adaptive Prediction
An intelligent diagnosis method for battery pack connection faults
Multiple lithium-ion battery cells and multi-contact connection methods increase the chances of connection failures in power battery packs, posing a significant threat
Intelligent optimization methodology of battery
However, there is hardly any research found that encompasses all the multidisciplinary aspects (such as materials, SOH, intelligent configuration [assembly], thermal design, mechanical safety, and recycling of materials and
Digital twin and cloud-side-end collaboration for intelligent battery
The experimental setup is shown in Fig. 7. The experimental setup consists of a battery test system, a temperature chamber, a host computer, a BMS master module, a BMS slave module, a battery pack, a 5G data transmission module, a DC power source and some aerials. The battery test system and temperature chamber are used for battery aging tests and
Research on Inconsistency Identification of Lithium-ion Battery Pack
Lithium-ion batteries have been widely used in the field of energy storage, due to the high energy density, wide temperature range and long service life. However, in application, the parameters such as the capacity and voltage of each cell in the battery pack are inconsistent due to unreasonable use, poor operating environment and other factors. In this paper, the qualitative
Dynamic load identification for a power battery pack based on a
The criteria for determining these regularization parameters are also included. In Section 4, load identification results of the battery pack are displayed, taking a driving road condition as an example. Discussions about the identification results and research perspectives are presented in Section 5. An intelligent impact load
Micro-Short-Circuit Cell Fault Identification Method for Lithium
(DOI: 10.1109/TIE.2020.2984441) During the usage of electric vehicles, the battery decays and the cell variations expand in the battery pack. In the discharge process, both the low-capacity cell and the micro-short-circuit (MSC) cell have the abnormal feature that the state-of-charge (SOC) differences increase continuously. Hence, a low-capacity cell is likely to
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Fault Identification of Lithium-Ion Battery Pack for Electric
The battery system is one of the core technologies of the new energy electric vehicle, so the frequent occurrence of safety accidents seriously limits the large-scale promotion and application. An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to estimate the current system status, which improves the accuracy and timeliness
Multi-fault diagnosis for battery pack based on adaptive
Moreover, the battery pack is a highly non-linear system, which is difficult to characterize the behaviors with model-based methods accurately. Thus, the model-free method has attracted much attention due to its better performance, which does not consider parameter identification, battery inconsistency and modeling error, etc.
Future smart battery and management: Advanced sensing
Such a battery pack is mostly supervised using a modularized BMS architecture, where a slave controller collects the current and voltage of cells and send the information to a centralized master controller, while the master controller makes decisions based on the reported data and sends the equalizing commands to the slave controllers according
A novel approach of battery pack state of health estimation using
Due to the energy crisis, more and more people pay great attention to the field of new sources of energy [1].The lithium-ion battery, which has the features of high energy density, low self-discharge and long service life, has been widely used in distribution energy storage systems and electric vehicles [2].Meanwhile, the new material [3] is also applied in lithium-ion
Intelligent state of charge estimation of battery pack based on
In this paper, to estimate the battery pack state-of-charge on-line, the definition of battery pack is proposed, and the relationship between the total available capacity of battery pack and
An intelligent fault diagnosis method for lithium-ion battery pack
Download Citation | On Nov 1, 2023, Lei Yao and others published An intelligent fault diagnosis method for lithium-ion battery pack based on empirical mode decomposition and convolutional neural
(PDF) Parameter Identification of Battery Pack
PDF | On Mar 28, 2017, Jujun Xia and others published Parameter Identification of Battery Pack Considering Cell Inconsistency | Find, read and cite all the research you need on ResearchGate
An intelligent diagnosis method for battery pack connection
An intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making. and the coupling relationship of SOC to diagnose the battery pack connection faults through the internal resistance identification algorithm. M. Said and M. Tohir [19] proposed combining the thermal
Fault Identification of Lithium-Ion Battery Pack for Electric
L. Yao et al.: Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle fastest speed, determine the fault location and cause, and give reasonable treatment methods [18].
Intelligent state of charge estimation of battery pack based on
In summary, the current estimation methods of battery pack SOC are mainly divided into three categories. (1) The battery pack is regarded as a battery cell with high voltage and large capacity so that the SOC estimation method of battery cell can be directly applied to the battery pack [18].This method doesn''t take into account the differences between individual
An intelligent fault diagnosis method for lithium-ion battery pack
A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks
Lithium-Ion Battery Health Management and State of Charge
Effective health management and accurate state of charge (SOC) estimation are crucial for the safety and longevity of lithium-ion batteries (LIBs), particularly in electric vehicles. This paper presents a health management system (HMS) that continuously monitors a 4s2p LIB pack''s parameters—current, voltage, and temperature—to mitigate risks such as
An Intelligent Fault Diagnosis Method for Lithium Battery
The battery pack is the critical component in Evs that can provide high energy density to meet the vehicle driving range requirements and offer high power density to fit the acceleration and hill-climbing scenarios. the selection of condition indicators is essential for the intelligent identification of the system state. In data mining
(PDF) Fault Identification of Lithium-Ion Battery Pack
Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network. In summary, the proposed intelligent fault diagnosis method is feasible. It
Intelligent Lithium
battery pack, as well as its highly nonlinear and multi- coupling nature, make it difficult to improve the accuracy of the intelligent prediction of the state of the lithium-ion battery system, leading to the deterioration of performance, rapid aging, and even spontaneous combustion, and other safety issues are increasingly prominent.
Intelligent disassembly of electric-vehicle batteries: a forward
In addition, there are also different functional systems in a pack, e.g., battery management system (BMS) and thermal management system. Many of these components and connections are difficult for robotic manipulation, such as flexible cables and connectors/fasteners difficult to access. Identification and checking: Intelligent labeling (RFID)
An Intelligent Fault Diagnosis Method for Lithium Battery Systems
In this study, an intelligent fault diagnosis method based on data-driven is proposed for the lithium-ion battery system. Accurate and reliable experimental voltage data is
Intelligent optimization methodology of battery pack for
However, there is hardly any research found that encompasses all the multidisciplinary aspects (such as materials, SOH, intelligent configuration [assembly], thermal design, mechanical safety, and recycling of materials and pack) simultaneously for the battery pack design of electric vehicles.
Battery Cloud: Data-Powered Intelligent Battery Management
Battery Ageing • Battery Models • Battery Diagnostics • Battery Pack Design • Electromobility • Stationary Energy Storage • Energy System Analysis 1 Battery Cloud: Data-Powered Intelligent Battery Management for Identification of various system parameters Cell remaining capacity (SoC) Cell impedance (SoH-R) Data-Powered
An intelligent fault diagnosis method for lithium-ion battery pack
Semantic Scholar extracted view of "An intelligent fault diagnosis method for lithium-ion battery pack based on empirical mode decomposition and convolutional neural network" by Lei Yao et al. Intelligent risk identification for drilling lost circulation incidents using data-driven machine learning. Shengnan Wu Yiming Hu Laibin Zhang Shujie
Integrated framework for battery cell state-of-health estimation in
Intelligent state of health estimation for lithium-ion battery pack based on big data analysis J Energy Storage, 32 ( 2020 ), Article 101836, 10.1016/j.est.2020.101836 View PDF View article View in Scopus Google Scholar
Battery voltage fault diagnosis for electric
2.2.3 Voltage prediction for battery pack and mean cell. The MDM has been studied in previous work [24, 39-41] for battery fault diagnosis. The basic principle of MDM is
An intelligent fault diagnosis method for lithium-ion battery pack
The rapid detection and accurate identification of the safety state of lithium-ion battery systems have become the main bottleneck of the large-scale deployment of electric vehicles. To solve this problem, an intelligent fault diagnosis method
6 FAQs about [Intelligent identification of battery pack]
Is there an intelligent diagnosis method for battery pack connection faults?
To this end, the study proposes an intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making.
What is intelligent fault diagnosis method for lithium-ion battery pack?
An Intelligent Fault Diagnosis Method for Lithium-ion Battery Pack Based on empirical mode decomposition and Convolutional Neural Network is proposed.
What is the fault diagnosis voltage for a battery pack?
For the upper-limit voltage of the battery pack, the fault diagnosis voltage was 410 V when the actual voltage of the battery pack recorded by the sensor was 450 V. The fault level for this condition is denoted No. I.
Can deep learning be used to identify faults in lithium-ion battery systems?
6. Conclusion In this study, an intelligent fault diagnosis method for the lithium-ion battery system based on data-driven by utilizing deep learning is proposed to identify fault information timely and accurately. However, it is challenging to identify faults in a timely and accurate way due to the interference of noise signals.
How to identify a fault in a reconfigurable battery system?
To effective and accurate identification of failures for the battery, Schmid et al. (2021) developed a fault diagnosis method by using the fuzzy clustering algorithm. In this algorithm, the switches of reconfigurable battery system were used to isolate the fault of the electric vehicles.
Can a support vector machine detect a lithium-ion battery fault?
This article proposes a novel intelligent fault diagnosis method for Lithium-ion batteries based on the support vector machine, which can identify the fault state and degree timely and efficiently.