The development of digital twin technology for batteries has gained significant traction as industries seek to optimize performance, predict degradation, and enhance safety across battery applications. A critical aspect of this evolution is the establishment of interoperability standards that ensure seamless data exchange and model compatibility across platforms. Two key standards under development, ISO/AWI 59040 and IEC 63296, aim to address these needs by providing frameworks for digital twin implementation in battery systems. Alongside these, data model architectures such as the Asset Administration Shell (AAS) and semantic ontologies are emerging as foundational tools for structuring battery data in a machine-readable format. However, challenges remain in achieving cross-platform compatibility, maintaining lifecycle data continuity, and addressing gaps in standardization for diverse battery chemistries and applications.

ISO/AWI 59040 is a forthcoming standard focused on digital twin interoperability for industrial applications, including energy storage systems. It outlines requirements for data exchange, model integration, and lifecycle management, with specific considerations for battery systems. The standard emphasizes the need for a unified approach to digital twin representation, ensuring that models can communicate across different software environments. Similarly, IEC 63296 targets battery management and diagnostics, incorporating digital twin functionalities to enable real-time monitoring and predictive maintenance. Both standards aim to reduce fragmentation in digital twin implementations, but their scope is limited to general principles rather than chemistry-specific or application-specific details.

A key enabler of interoperability is the Asset Administration Shell, a framework developed within Industry 4.0 initiatives to standardize the digital representation of physical assets. For batteries, the AAS provides a structured way to encapsulate data such as state of charge, state of health, and thermal properties in a standardized format. This allows different systems to interpret and utilize the data consistently, regardless of the underlying platform. The AAS framework supports submodels for various aspects of battery operation, including electrochemical performance, mechanical integrity, and safety parameters. By adopting AAS, stakeholders can ensure that digital twins remain compatible across the supply chain, from cell manufacturers to end-users.

Semantic ontologies further enhance interoperability by defining relationships between battery-related concepts in a machine-understandable way. Ontologies such as Battery Interface Ontology (BattINFO) and Smart Battery Data Ontology (SBDO) provide vocabularies and taxonomies that standardize terminology for parameters like capacity fade, impedance rise, and cycle life. These ontologies enable automated reasoning and data integration across heterogeneous systems, reducing the need for manual mapping between different data schemas. For example, a digital twin using BattINFO can seamlessly share degradation data with a maintenance system using SBDO, provided both adhere to the same ontological principles.

Despite these advancements, cross-platform compatibility remains a challenge due to the diversity of digital twin implementations. Many proprietary solutions exist, each with unique data formats and interfaces, making it difficult to integrate models from different vendors. Even with standards like ISO/AWI 59040, variations in interpretation and implementation can lead to inconsistencies. For instance, one platform might represent state of health as a single scalar value, while another might use a multi-dimensional vector accounting for different degradation modes. Bridging these differences requires not only standardized data models but also agreed-upon methods for data transformation and validation.

Lifecycle data continuity presents another hurdle, as battery digital twins must operate across multiple stages from manufacturing to end-of-life. Data collected during production, such as electrode porosity or electrolyte fill levels, must be accessible to the digital twin during operation to inform degradation models. However, gaps often exist between manufacturing execution systems and field deployment platforms, leading to incomplete or siloed data. Standards like IEC 63296 attempt to address this by specifying data logging requirements throughout the battery lifecycle, but practical implementation depends on collaboration between manufacturers, integrators, and operators.

Standardization gaps are particularly evident when considering different battery chemistries and applications. Lithium-ion batteries dominate the digital twin landscape, with most standards and frameworks tailored to their characteristics. However, emerging chemistries like solid-state, sodium-ion, and lithium-sulfur exhibit unique behaviors that require specialized models. For example, solid-state batteries may experience interfacial degradation mechanisms not present in liquid electrolyte systems, necessitating additional parameters in the digital twin. Similarly, application-specific requirements vary widely: electric vehicle batteries prioritize fast-charging models, while grid storage systems focus on long-duration degradation. Existing standards lack detailed guidance for these variations, leaving developers to extend or adapt frameworks on a case-by-case basis.

Efforts to close these gaps are underway, with industry consortia and research initiatives proposing extensions to existing standards. For instance, some groups are working on chemistry-specific submodels for the AAS, enabling digital twins to capture nuances like lithium plating in high-energy cells or sulfur redistribution in lithium-sulfur systems. Others are developing application profiles that define mandatory and optional data fields for different use cases, such as aerospace or marine environments. These profiles aim to balance flexibility with interoperability, allowing customization while maintaining baseline compatibility.

The role of validation and certification in digital twin interoperability cannot be overlooked. Without standardized testing procedures, it is difficult to ensure that models perform as intended across platforms. Preliminary work in this area includes reference datasets for benchmarking digital twin accuracy and protocols for verifying compliance with interoperability standards. However, widespread adoption of these practices will require buy-in from major stakeholders, including battery manufacturers, OEMs, and software providers.

Looking ahead, the maturation of battery digital twin interoperability will depend on continued collaboration between standards bodies, industry players, and academia. Priorities include refining data models for diverse chemistries, establishing validation frameworks, and promoting the adoption of common ontologies. As the technology evolves, the integration of real-time data streams from IoT devices and advanced analytics will further enhance the fidelity and utility of digital twins. The foundation laid by ISO/AWI 59040, IEC 63296, and associated frameworks provides a starting point, but sustained effort is needed to realize the full potential of interoperable battery digital twins across the ecosystem.