Detecting 4 common battery testing errors with AI | Monolith

Read Time: 6-7 minutes

Date: 16th October 2024

The problems engineers face.

Battery engineers are under constant pressure. Whether developing electric vehicles (EVs) or other battery-driven products, the race to bring the next prototype to market faster while meeting stringent regulatory requirements leaves little room for error.

But testing batteries is no simple task—it's a long process, often taking months or even years, particularly in degradation studies. This time pressure can make every testing cycle critical. 

No test is perfect. Things can and do go wrong. However, with the right tools, you can detect issues early and adjust to save precious time and resources.

This is where AI comes in. By leveraging machine learning techniques, engineers can identify and rectify battery testing errors sooner, helping them optimise their testing processes. 

In this blog, we’ll explore four common errors in battery testing and how AI can detect them, helping engineers save time, improve efficiency, and ensure better overall results. 

One of the most significant costs of a failed battery experiment: Time 

What happens if you miss your testing window? Scheduling time with testing equipment is no small feat. Lab resources are often booked weeks or even months in advance. The consequences can be significant if a test fails due to an error. Engineers may have to: 

• Submit new applications for testing. 

• Reserve new slots or negotiate with other teams who had booked the equipment for their tests. 

The ripple effect of a failed test doesn’t just impact your team. It can cause delays across other projects, extending timelines and introducing inefficiencies.

This time, loss comes with additional labour, equipment, and opportunity costs that can seriously harm product development. 

But there’s good news: AI can help you avoid these delays by detecting errors before they derail your experiments. 

Using AI to detect battery testing errors 

AI is already widely used in industries like predictive maintenance and automation. In the world of battery testing, AI’s potential is clear—it can quickly and accurately detect issues that might otherwise go unnoticed until a test has failed.

By integrating AI into the testing process, engineers can catch problems early, saving time and ensuring their results remain accurate and reliable. 

Here are four common errors AI can detect during battery testing: 

Error 1: Operator Errors 

Human error is inevitable, but it can be costly in the context of battery testing. Operator mistakes often occur during the setup phase. Components may be misconnected, or configurations might not match the required test profile.

These errors can only be detected with post-experiment analysis, by which time valuable time has already been lost. Examples of operator errors include: 

• Mislabelled channels: Sensors plugged into the wrong channels, confusing the data. 

• Incorrect test profiles: Inputting the wrong cycling routine, leading to inaccurate test conditions. 

• Wrong configuration settings: Small software errors that have significant consequences. 

AI can help mitigate these errors by continuously monitoring input settings and flagging issues before the experiment begins. This early intervention prevents wasted test cycles and ensures your testing starts correctly. 

Error 2: Sensor/Temperature Abnormalities 

Sensors play a critical role in battery testing, particularly in tracking temperature, which must remain within strict limits.

But when sensors fail due to physical displacement, misalignment, or malfunction, temperature readings may become unreliable, potentially skewing the entire test.

Without AI, these issues may go unnoticed until after the experiment is complete, at this point, the data is often rendered useless. 

AI systems can track sensor data in real time, identifying temperature spikes or dropouts that indicate a potential sensor problem. For example, if a sensor has been displaced or disconnected, AI can flag this anomaly immediately, preventing you from collecting inaccurate data over several weeks.

Additionally, AI can track sensor misplacement or wiring errors, helping engineers avoid the need to scrap entire datasets. 

Error 3: Electrical Errors 

Battery cells are sensitive to electrical abnormalities. Overcharging, over-discharging, or unexpected ageing patterns can damage the cells and reduce their lifespan.

Electrical errors can also lead to safety hazards, particularly if cells are overcharged beyond manufacturer specifications. 

By monitoring electrical activity in real time, AI can detect irregular patterns—such as gradual voltage drops or spikes—and flag potential problems.

Catching these errors early prevents permanent damage to the cells, reduces waste, and ensures that testing remains within safe operational limits. 

Error 4: Mechanical Errors 

Mechanical degradation, such as swelling or venting, often signals a severe issue with the battery’s structure. These physical changes may result from interactions between mechanical and electrical degradation processes, and if not caught in time, they can lead to catastrophic failure. 

AI can track physical symptoms of degradation, such as pressure or volume changes, in parallel with other performance metrics.

For example, AI systems can correlate physical expansion (swelling) with temperature and electrical performance to detect early signs of mechanical failure.

Catching these signs early allows engineers to intervene before the situation escalates, reducing the risk of more severe outcomes. 

Conclusion: How to implement AI in battery testing 

Integrating AI into battery testing workflows may seem complex, but the benefits outweigh the initial effort. AI-powered anomaly detectors can continuously monitor testing channels, catching and rectifying errors before they compromise the experiment.

Tools such as autoencoders can reconstruct signals and compare them to expected outputs, alerting engineers to even subtle discrepancies. 

By implementing AI, engineers can run large datasets that would otherwise be overwhelming to process manually, ensuring that their tests are efficient and accurate. For engineers looking to optimise their battery testing, it’s crucial to: 

• Start collecting and organising key data points now, even if you aren’t ready to implement machine learning immediately. 

• Leverage the power of AI tools to detect errors early and improve the efficiency of your tests. 

• Implement data organisation best practices to ensure smooth integration when you decide to adopt machine learning in the future. 

Our webinar on anomaly detection provides a more in-depth look at how anomaly detection can improve battery testing. 

Improving your battery testing process 

As your organisation grows and testing demands increase, it’s essential to stay ahead of potential issues. AI anomaly detector tools allow you to catch errors early, rectify them swiftly, and keep your testing process running smoothly.

Whether you're already using machine learning or just starting to explore its benefits, capturing the correct data today will position you for future success. 

The key to success in battery testing is efficiency, and AI is the tool that can help you achieve that. By leveraging AI's capabilities to detect common errors, you can ensure that your tests are accurate, timely, and cost-effective.