Emerging Battery Testing Approaches: A New Era of Diagnostics
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As the world becomes increasingly electrified—from smartphones to electric vehicles (EVs)—the demand for high-performance, long-lasting, and safe batteries has never been greater. Lithium-ion batteries, although powerful and widely used in high-tech consumer applications, are prone to issues such as current leakage, dendrite formation, and thermal runaway. Each of these issues can cause catastrophic failure. To address these risks, engineers and researchers are developing new testing approaches that probe deeper into battery behavior, uncovering microscopic degradation mechanisms and offering real-time insights into battery health. In this blog, we examine some of the new and emerging diagnostic tests for batteries being developed, highlighting the ways these approaches deliver advanced energy storage.
The Push for Safer, Smarter Batteries
If the lithium-ion (Li-ion) fires in the smartphone and EV space over the last few years have taught us anything, it is that there is a continued need to build more stringent and robust safety and testing practices. These incidents have underscored the importance of robust testing protocols that go beyond surface-level diagnostics. Emerging battery testing approaches include physical and simulation-based testing that can probe current dynamics and battery structures at much smaller levels to check that batteries are running optimally and safely, as well as to detect any microscopic issues before they develop into a major thermal event. These methods include graphene Hall sensors and electrochemical mass spectrometry (EC-MS), and they are utilized in quality control (QC), in-field diagnostics, and research and development (R&D), providing a more comprehensive understanding of battery behavior throughout its lifecycle.
Graphene Hall Sensors: Mapping Current with Precision
One of the most promising innovations in battery diagnostics is the graphene Hall sensor, which measures the magnetic field in batteries to determine the local current. In Hall-effect sensors, when the active sensing surface is placed into a magnetic field, the charge carriers in the material deflect, causing a potential difference across the material. This process generates a voltage in the material, and by measuring the deflection and generated voltage, the strength of the magnetic field can be determined. In graphene-based sensors, the Hall effect is amplified due to graphene’s exceptional electrical properties, allowing for precise measurement of localized currents and fast response times.
Because they are non-contact, non-destructive, and can monitor localized currents without affecting battery operation, graphene Hall sensors can be deployed in real time, even in operational environments like EVs. Using graphene Hall sensors, engineers can:
- monitor charge and discharge cycles,
- assess the state of charge (SOC) and state of health (SOH),
- detect current spikes and leakage,
- identify hotspots that could lead to thermal runaway, and
- map current distribution across the battery.
Graphene Hall sensors are especially well-suited for monitoring and testing one of the biggest safety challenges of higher powered Li-ion batteries: thermal runaway. Thermal runaway is a rapid increase in temperature that results from a defect. These rapidly spiking temperatures propagate through the cell and the battery pack, leading to fires and potentially an explosion. Graphene Hall sensors can be used as a battery monitoring tool to map currents and defects before they become serious thermal events. In this way, graphene Hall sensors can act as an early warning system for thermal runaway and other serious battery safety issues.
Electrochemical Mass Spectrometry
While graphene sensors focus on electrical behavior, electrochemical mass spectrometry (EC-MS) offers a window into the chemical processes occurring inside batteries. One current example of this diagnostic approach is Spectro Inlets, a Danish company pioneering the use of EC-MS to study gas evolution in Li-ion cells,[1] which is a key indicator of degradation.
Traditionally, analyzing gas emissions requires complex setups in controlled environments. EC-MS simplifies this by using microfabricated membranes that interface directly with the battery cell, allowing gases to diffuse naturally into the spectrometer without the need for additional carrier gases or electrolyte loss. This technique enables researchers to identify specific degradation pathways, measure gas production rates over time, and predict real-world battery life spans more accurately than simulations alone.
EC-MS is particularly valuable in R&D, where understanding the root causes of battery aging can lead to more durable designs. Although currently used on a small scale, the technology has attracted attention from organizations like the Faraday Institution, signaling its potential for broader adoption.
Physics-based Simulations’ Role in Battery Testing
While physical testing is essential, physics-based simulation approaches are becoming equally vital in battery development. Simulations are important during the development of batteries to show how they should theoretically perform across several given scenarios. Physics-based models have gained popularity for battery monitoring and diagnostics. Recently, physics-based simulation models specifically geared toward batteries have emerged that more accurately predict the internal properties and degradation of batteries over time.
One standout platform is Python Battery Mathematical Modelling (PyBaMM), an open-source tool that enables engineers to simulate degradation mechanisms, performance metrics, and lifetime predictions based on fundamental physics.[2] With thousands of engineers already using PyBaMM worldwide, it’s becoming a cornerstone of battery R&D. The platform allows for:
- detailed analysis of degradation scenarios,
- optimization of battery design and materials, and
- integration with experimental data for hybrid testing approaches
Conclusion
Battery testing is entering a new phase—one defined by precision, sensitivity, and predictive capability. Whether it’s detecting microscopic faults before they become hazards or simulating complex degradation pathways, the tools now available are reshaping how we understand and manage battery performance.
As power demands grow and safety standards tighten, these emerging diagnostic methods will play a pivotal role in ensuring that batteries are not only powerful but also reliable and safe. The combination of physical and simulation-based testing will be the key to unlocking the full potential of energy storage technologies in the years to come.
[1]https://www.faraday.ac.uk/success-stories/spectro-inlets/
[2]https://pybamm.org/