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Advanced Sensing Technology for Detection of Battery States
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Advanced Sensing Technology for Detection of Battery States

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 3941

Special Issue Editor

Dr. Tongye Wei

E-Mail Website
Guest Editor
Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan 411105, China
Interests: advanced energy storage materials and micro-energy storage devices, mainly for multivalent metal ion batteries, especially in-depth research on the electrode materials and energy storage mechanisms of aqueous zinc-ion batteries; sensitive materials and devices for sensors

Special Issue Information

Dear Colleagues,

Electrochemical energy storage technologies, represented by lithium-ion batteries, are crucial support technologies and key equipment for building new power systems. However, safety incidents frequently occur in lithium-ion battery energy storage systems, mainly due to thermal runaway triggered by safety failures during battery operation. The further spread of thermal runaway can lead to catastrophic consequences such as fires or explosions. Therefore, it is essential to provide timely and accurate proactive safety warnings before energy storage battery failures to prevent thermal runaway and ensure the safe operation of energy storage battery systems.

Using sensors to study the energy storage mechanisms of battery materials is a crucial approach for the real-time monitoring of battery operation and for gaining deeper insights into the electrochemical behavior of batteries under various conditions.

Potential topics include but are not limited to the following:

  • The design and fabrication of sensors;
  • Electrochemical sensors;
  • Stress and strain sensors;
  • Temperature sensors;
  • Gas sensors;
  • Optical sensors;
  • Integration testing of sensor systems in batteries.

Dr. Tongye Wei
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • battery
  • sensors
  • energy storage solutions

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Published Papers (2 papers)

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Research

17 pages, 2796 KB  
Article
Multi-Scale Spatiotemporal Attention Network for Early Warning of Lithium-Ion Battery Thermal Runaway
by Yangyang Liu, Guoli Li and Qunjing Wang
Sensors 2026, 26(10), 3083; https://doi.org/10.3390/s26103083 - 13 May 2026
Viewed by 316
Abstract
Lithium-ion battery thermal runaway has become a key safety hazard restricting the development of electric vehicles. Early precursor signals of thermal runaway are characterized by multi-scale features, weak signal strength and spatial coupling, posing significant challenges for traditional methods in achieving accurate early [...] Read more.
Lithium-ion battery thermal runaway has become a key safety hazard restricting the development of electric vehicles. Early precursor signals of thermal runaway are characterized by multi-scale features, weak signal strength and spatial coupling, posing significant challenges for traditional methods in achieving accurate early warning. To solve this problem, a multi-scale spatiotemporal attention network (MSTA-Net) is proposed for battery thermal runaway early warning. First, a systematic feature engineering process is designed, including signal denoising, normalization processing and multi-level feature construction, to fully extract discriminative information from voltage and temperature signals. Then, the MSTA-Net architecture is constructed, which includes three parallel feature extraction branches: local fine perception branch based on 1D depthwise separable convolution to capture transient anomalies, a temporal evolution modeling branch based on bidirectional gated recurrent units to learn long-term trends, and a global spatial dependence branch based on a graph attention network to model the spatial propagation of thermal runaway. Finally, an adaptive fusion gate is designed to dynamically fuse the features of each branch according to the input context. The experimental results on the self-built battery thermal runaway dataset show that the proposed MSTA-Net achieves a recall rate of 98.7%, an average early warning time of 115 s and a false alarm rate of 0 times/h. Compared with traditional machine learning and deep learning models such as Random Forest, LSTM and Transformer, the model has significant advantages in early warning accuracy, timeliness and robustness. Ablation experiments verify the effectiveness of each component of the MSTA-Net. The proposed method can provide reliable early warning of thermal runaway only by using the existing voltage and temperature sensors of the battery management system, which has important engineering application value. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Detection of Battery States)
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18 pages, 2388 KB  
Article
Experimental Investigations on the Repeatability of the Fire-Resistance Testing of Electric Vehicle Post-Crash Safety Procedures
by Daniel Darnikowski and Magdalena Mieloszyk
Sensors 2025, 25(3), 688; https://doi.org/10.3390/s25030688 - 24 Jan 2025
Cited by 1 | Viewed by 2944
Abstract
The widespread adoption of electric vehicles (EVs) has elevated the importance of rigorous safety standards, particularly for fire resistance in post-crash scenarios. Existing testing protocols, such as Regulation No. 100, utilize petrol pool fires to simulate real-world fire hazards but lack comprehensive analysis [...] Read more.
The widespread adoption of electric vehicles (EVs) has elevated the importance of rigorous safety standards, particularly for fire resistance in post-crash scenarios. Existing testing protocols, such as Regulation No. 100, utilize petrol pool fires to simulate real-world fire hazards but lack comprehensive analysis regarding their repeatability and reliability. This study addresses this critical gap by evaluating the variability and consistency of fire-resistance tests performed on multiple battery energy storage systems (BESSs) under standardized conditions. A custom-built measurement system incorporating thermocouples, anemometers, and hygrometers provided high-resolution data on flame dynamics, ambient conditions, and pool fire efficiency. Statistical evaluations following ISO 5725 series guidelines revealed substantial inconsistencies, including unstable exposure temperatures and sensitivity to local turbulence. These findings call into question the robustness of current testing methods, and we propose an alternative approach employing LPG burners for improved precision and repeatability. By identifying significant flaws in existing standards and offering scientifically grounded enhancements, this work contributes a novel perspective to the field of EV safety, advancing global fire-resistance testing protocols. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Detection of Battery States)
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