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Contactless Battery Sensing: A Survey
Saravana Ram Srinivasan1, Pedro Callado de Paiva1, Aditi Dharmadhikari1
1College of Engineering and Computer Science, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Sensors (Basel, Switzerland)
|February 27, 2026
Summary
Wireless battery management systems offer a scalable alternative to traditional wired designs for electric vehicles and IoT devices. This review covers advancements in wireless sensing and machine learning for efficient battery health monitoring and diagnostics.
Area of Science:
- Electrical Engineering
- Materials Science
- Computer Science
Background:
- Growing demand for electric vehicles (EVs), wireless sensor networks (WSNs), and Internet of Things (IoT) devices necessitates efficient battery health monitoring.
- Conventional wired Battery Management Systems (BMS) are costly, lack scalability, and are prone to failures.
- Emerging trends focus on wireless, low-power, and contactless alternatives for improved battery management.
Purpose of the Study:
- To review emerging sensing solutions and machine learning techniques for battery state and health estimation.
- To examine Wireless Battery Management System (WBMS) advancements from theory to prototypes.
- To explore innovative diagnostic approaches and algorithmic frameworks for real-time diagnostics.
Main Methods:
- Review of current literature on wireless sensing technologies and machine learning algorithms for battery monitoring.
- Analysis of WBMS advancements, including health monitoring, cycle tracking, thermal management, and second-life applications.
- Discussion on integrating electrochemical impedance spectroscopy (EIS) and ultrasonic sensing with IoT and machine learning.
Main Results:
- Identification of key sensing solutions and machine learning techniques for accurate battery state and health estimation.
- Overview of WBMS progress, highlighting practical applications and theoretical frameworks.
- Exploration of integrated diagnostic approaches combining advanced sensing and AI.
Conclusions:
- Wireless Battery Management Systems present a viable and scalable solution for modern energy storage needs.
- Integration of advanced sensing (EIS, ultrasonic) and machine learning is crucial for intelligent, real-time battery diagnostics.
- Significant research opportunities exist for deploying intelligent, wireless battery monitoring in cyber-physical systems.
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