scholarly journals Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

Energies ◽  
2017 ◽  
Vol 10 (7) ◽  
pp. 919 ◽  
Author(s):  
Jichao Hong ◽  
Zhenpo Wang ◽  
Peng Liu
Keyword(s):  
Big Data ◽  
Electric Vehicles ◽  
Thermal Runaway ◽  
Battery Systems

scholarly journals Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

2017 ◽  
Author(s):  
Jichao Hong ◽  
Zhenpo Wang ◽  
Peng Liu
Keyword(s):  
Big Data ◽  
Real Time ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Thermal Runaway ◽  
Entropy Method ◽  
Diagnosis And Prognosis ◽  
Data Platform ◽  
Battery Systems ◽  
Battery Packs

This paper presents a thermal runaway prognosis scheme based on the big-data platform and entropy method for battery systems in electric vehicles. It can simultaneously realize the diagnosis and prognosis of thermal runaway caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data was collected in the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing to verify the effectiveness of the presented method. The results show that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. Furthermore, a temperature security management strategy for thermal runaway is proposed on the basis of the Z-score approach and the abnormity coefficient is set to make real-time precaution of temperature abnormity.

scholarly journals DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2977 ◽  
Author(s):  
Da Li ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Spatial Clustering ◽  
Lithium Ion ◽  
Thermal Runaway ◽  
Battery System ◽  
Diagnosis And Prognosis ◽  
Dbscan Clustering ◽  
System Diagnosis ◽  
Battery Systems ◽  
Diagnosis Method

Battery system diagnosis and prognosis are essential for ensuring the safe operation of electric vehicles (EVs). This paper proposes a diagnosis method of thermal runaway for ternary lithium-ion battery systems based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. Two-dimensional fault characteristics are first extracted according to battery voltage, and DBSCAN clustering is used to diagnose the potential thermal runaway cells (PTRC). The periodic risk assessing strategy is put forward to evaluate the fault risk of battery cells. The feasibility, reliability, stability, necessity, and robustness of the proposed algorithm are analyzed, and its effectiveness is verified based on datasets collected from real-world operating electric vehicles. The results show that the proposed method can accurately predict the locations of PTRC in the battery pack a few days before the thermal runaway occurrence.

Challenge-Driven Printing Strategies Toward High-Performance Solid-State Lithium Batteries

2022 ◽  
Author(s):  
Jing Wang ◽  
Xingkang Huang ◽  
Junhong Chen
Keyword(s):  
Solid State ◽  
Electric Vehicles ◽  
Lithium Batteries ◽  
High Performance ◽  
Form Factors ◽  
Portable Devices ◽  
Li Ion Batteries ◽  
Battery Systems ◽  
Li Ion

Solid-state lithium batteries (SSLBs) are promising candidates for replacing traditional liquid-based Li-ion batteries and revolutionizing battery systems for electric vehicles and portable devices. However, longstanding issues such as form factors,...

Health-Aware and User-Involved Battery Charging Management for Electric Vehicles Using Linear Quadratic Control

2015 ◽  
Author(s):  
Huazhen Fang ◽  
Yebin Wang
Keyword(s):  
Control Theory ◽  
Electric Vehicles ◽  
User Satisfaction ◽  
User Involvement ◽  
Linear Quadratic Control ◽  
Health Awareness ◽  
Linear Quadratic ◽  
Battery Charging ◽  
Simulation Based ◽  
Battery Systems

This paper studies control-theory-enabled charging management for battery systems in electric vehicles (EVs). Charging is a crucial factor for the battery performance and life as well as EV users’ anxiety. Existing methods run with two shortcomings: insufficiency of battery health awareness during charging, and failure to include the user into the charging loop. To address such issues, we propose to perform charging that deals with both health protection and user-specified charging needs or objectives. Capitalizing on the linear quadratic control theory, a set of charging strategies are developed. A simulation-based study demonstrates their effectiveness and potential. We expect that charging with health awareness and user involvement will improve not only the battery longevity but also user satisfaction.

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