ReviewSensors (Basel, Switzerland)2026
Contactless Battery Sensing: A Survey.
Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As demand for EVs (Electric Vehicles), WSNs (Wireless Sensor Networks), and IoT (Internet of Things) devices continues to grow, efficient battery health monitoring has emerged as a critical requirement. Conventional BMS (Battery Management System) designs rely on wired, centralized architectures, which are not only costly and less scalable but also highly prone to operational failures. To mitigate these inherent drawbacks, recent studies have shifted toward exploring wireless, low-power, and contactless alternatives. This paper reviews emerging sensing solutions and machine learning techniques for battery state and health estimation. It also examines WBMS (Wireless Battery Management System) advancements from theoretical frameworks to prototypes, covering health monitoring, cycle/discharge tracking, thermal management, and second-life reuse. Additionally, we discuss integrating techniques including EIS (electrochemical impedance spectroscopy), ultrasonic sensing with IoT systems and advanced machine learning models. Furthermore, it explores innovative diagnostic approaches and highlights algorithmic frameworks for real-time diagnostics. Overall, this work provides a comprehensive view of intelligent, wireless battery-monitoring technologies and identifies key challenges and research opportunities for scalable deployment in cyber-physical systems.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.