IDTechEx: AI-Assisted Diagnostics in Battery Management
Effective monitoring tools are essential as battery degradation poses risks such as unexpected failures and potential safety hazards.
“To counteract these concerns, battery health (SoH) and degradation must be tracked,” states Daniel Parr, Technology Analyst at IDTechEx.
By measuring electrochemical properties such as voltage, current and resistance over time, these tools ensure the safety and reliability of large battery deployments.
The importance of battery management systems
A battery management system (BMS) is critical in collecting key data points about battery performance.
However, as Daniel explains, “the system itself has limited analytic capacity.” This limitation has led to the rise of external diagnostics, where data is uploaded to the cloud for analysis.
By leveraging advanced machine learning techniques, these external diagnostics can more efficiently use the data provided by BMSs.
Machine learning in battery diagnostics
Machine learning, particularly supervised learning methods, has emerged as a powerful tool in battery diagnostics. “Supervised learning methods are the primary algorithms employed in AI-assisted battery diagnostics,” Daniel explains.
These algorithms tackle two main challenges: regression and classification.
Regression: Estimating continuous properties like state of charge (SoC), remaining useful lifetime (RUL), or state of health (SoH).
Classification: Sorting cells into distinct categories, such as ‘good’ vs. ‘bad’ or ‘highly degraded’ vs. ‘less degraded.’
“Traditional methods rely on physics-based calculations and modelling, but the complexity of battery degradation modes often challenges these,” Daniel highlights.
In contrast, data-driven techniques like artificial neural networks (ANN), decision trees and k-means clustering offer a more nuanced understanding of cell degradation, often outperforming traditional models.
Early detection and risk mitigation
AI-assisted diagnostics are particularly effective at identifying specific degradation modes early.
“It identifies degradation early, allowing cell charging profiles and usage strategies to be modified to limit degradation growth,” Daniel notes.
The proactive approach extends battery lifetimes by 10-20% and significantly enhances deployment safety. The impact extends beyond technical benefits. Insurers are increasingly recognising the value of these diagnostics.
“Improvements in lifetime and reductions in risk are vital to insurers who seek to minimise the cost and number of payouts,” Daniel adds.
Consequently, insurers offer better premiums to fleet and BESS owners who implement machine-learning diagnostic services.
Key players and market trends
The European market has become a hub for cloud-based diagnostics, with companies like ACCURE and TWAICE leading the charge.
“ACCURE is currently the market leader in the BESS space,” shares Daniel.
For example, HDI insurance offers reduced premiums to clients using ACCURE’s diagnostic services. Similarly, TWAICE partnered with NARDAC in June 2024, releasing a whitepaper highlighting the benefits of diagnostics in risk management.
Future growth in AI-assisted diagnostics
The global market for AI-assisted diagnostics, particularly third-party cloud-based services, is projected to reach a capacity of 190 GWh by 2035, growing at a CAGR of 23.4%.
“The growth will be fuelled partly by arrangements between insurance providers and diagnostics players,” Daniel continues.
Reduced premiums and extended battery lifetimes provide a compelling value proposition for BESS and mobility stakeholders.
“IDTechEx predicts that AI-assisted battery diagnostics will become the norm across much of the BESS industry,” Daniel concludes, highlighting the transformative potential of these technologies in enhancing safety, reducing costs and ensuring sustainability in the energy storage sector.
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