The prediction of state-of-health (SOH) in batteries is a critical parameter for determining remaining useful life, performance degradation, and safety thresholds in energy storage systems. Standardization efforts for SOH prediction methods have gained significant attention due to the increasing reliance on batteries in electric vehicles, grid storage, and aerospace applications. Several organizations, including IEEE, SAE, and IEC, have established working groups to develop test protocols, accuracy reporting guidelines, and certification frameworks to ensure consistency and reliability across the industry.

IEEE has been instrumental in developing standards for battery testing and SOH prediction through its Power & Energy Society and Vehicular Technology Society. The IEEE 1188 standard outlines recommended practices for maintenance, testing, and replacement of valve-regulated lead-acid batteries, but newer working groups are focusing on lithium-ion and other advanced chemistries. The IEEE P2686 working group is developing a framework for SOH estimation, emphasizing model validation, uncertainty quantification, and benchmarking against real-world data. Key challenges addressed include the selection of input variables, such as capacity fade, internal resistance growth, and thermal behavior, as well as the standardization of testing conditions to ensure reproducibility across different laboratories.

SAE International has also contributed to SOH prediction standardization, particularly in the automotive sector. The SAE J2931 standard provides guidelines for communication between electric vehicles and charging infrastructure, but SAE J3168 focuses specifically on SOH estimation for traction batteries. This standard defines test procedures for cycle life evaluation under varying temperatures, charge/discharge rates, and depth-of-discharge profiles. SAE’s approach emphasizes the need for harmonized reporting metrics, such as percentage of initial capacity or equivalent full cycles, to facilitate comparisons between different battery systems. The SAE Battery Standards Steering Committee collaborates with automakers and battery manufacturers to refine these protocols, ensuring they remain relevant as battery technologies evolve.

The IEC has established TC 21/SC 21A, a technical committee dedicated to secondary cells and batteries, which has published several standards relevant to SOH prediction. IEC 62660-2 outlines test procedures for performance and life cycling of lithium-ion cells in electric vehicles, while IEC 62902 specifies methods for determining internal resistance. The IEC 62485-3 standard addresses safety requirements for secondary batteries in industrial applications, including SOH monitoring as part of battery management systems (BMS). IEC working groups are also investigating the integration of machine learning-based SOH prediction models into these standards, with a focus on transparency, explainability, and robustness against adversarial conditions.

Ground truth determination for SOH prediction remains a significant challenge due to the destructive nature of many validation techniques. Traditional methods involve cycling cells to end-of-life under controlled conditions, which is time-consuming and renders the cells unusable for further testing. Destructive physical analysis (DPA) techniques, such as post-mortem electrode characterization or electrolyte decomposition analysis, provide detailed insights into degradation mechanisms but are impractical for real-time SOH estimation. Non-destructive methods, including electrochemical impedance spectroscopy (EIS) and incremental capacity analysis (ICA), are being standardized as alternatives, though their accuracy depends heavily on calibration against ground truth data. The lack of universally accepted reference datasets has led to discrepancies in model performance claims, prompting organizations like the U.S. Department of Energy’s Battery Testing, Analysis, and Modeling program to develop open-access degradation datasets for benchmarking.

Certification requirements for BMS integration of SOH prediction algorithms are becoming increasingly stringent, particularly for safety-critical applications. Regulatory bodies such as UL and TÜV require extensive validation under accelerated aging conditions before approving these algorithms for use in electric vehicles or grid storage systems. Key certification criteria include model stability under varying operating conditions, fault detection capabilities, and fail-safe mechanisms to prevent overestimation of remaining capacity. The ISO 26262 functional safety standard for automotive applications imposes additional requirements, such as adherence to Automotive Safety Integrity Level (ASIL) classifications, which dictate the permissible error rates for SOH prediction depending on the criticality of the application. For example, an ASIL D-rated BMS must demonstrate a failure rate of less than 10 failures per billion operating hours for SOH-related functions.

Liability considerations for prediction errors in safety-critical applications have driven the development of risk mitigation frameworks. Inaccurate SOH predictions can lead to catastrophic failures, such as thermal runaway in electric vehicles or unexpected shutdowns in medical devices. Legal and insurance industries are increasingly scrutinizing the methodologies used for SOH estimation, particularly in cases where prediction errors result in property damage or personal injury. The National Fire Protection Association (NFPA) 855 standard for energy storage systems mandates redundant SOH monitoring mechanisms and regular manual inspections to complement algorithmic predictions. Product liability laws in jurisdictions such as the European Union and the United States place the burden of proof on manufacturers to demonstrate that SOH prediction methods meet industry-accepted standards at the time of deployment.

The standardization landscape for SOH prediction is still evolving, with ongoing debates over the appropriate tradeoffs between model complexity and interpretability. Physics-based models, which rely on electrochemical principles, are favored for their robustness but require extensive parameterization. Data-driven approaches, such as neural networks or support vector machines, offer higher accuracy in some cases but lack transparency in decision-making. Hybrid models that combine both approaches are gaining traction, though their standardization is complicated by the need to define acceptable levels of empirical tuning. Organizations like the International Alliance for Battery Standardization (IABS) are working to bridge these gaps by developing unified evaluation frameworks that account for diverse use cases and technological constraints.

Interoperability between different SOH prediction systems is another area of focus, particularly for second-life applications where batteries are repurposed from electric vehicles to stationary storage. Standardized communication protocols, such as those outlined in the IEEE 1815.1 standard for battery data exchange, are essential for ensuring that SOH estimates remain accurate across different BMS platforms. The growing adoption of cloud-based SOH monitoring systems has further highlighted the need for cybersecurity standards to protect against data manipulation or unauthorized access, as outlined in the IEC 62443 series for industrial communication networks.

As battery technologies continue to advance, standardization efforts must remain agile to accommodate new chemistries, form factors, and operating paradigms. The emergence of solid-state batteries, for example, introduces new degradation mechanisms that may not be fully captured by existing SOH prediction models. Collaborative initiatives between academia, industry, and regulatory bodies will be critical to maintaining the relevance and reliability of these standards in a rapidly evolving landscape. The ultimate goal is to establish a globally harmonized framework that ensures accurate, comparable, and actionable SOH predictions across all battery applications.