Battery analytics illustrates how competitive advantage in GreenTech grows through operational learning. A cloud-based platform can improve its diagnostics as it encounters different battery chemistries, operating conditions and ageing patterns. The resulting knowledge becomes embedded in data preparation methods, reference datasets, model parameters and validation routines. Protecting this accumulated learning is a strategic management task for a company preparing to scale.

The practical value of this intelligence depends on its use in customer and partner decisions. Operators need reliable information about battery condition and safety. Energy traders can use more accurate state-of-charge and state-of-health estimates to inform trading decisions. Insurers and financing partners receive selected results for the assets they insure or finance. For stationary storage, these outputs may be largely standardized, while their integration into different decision processes determines their commercial value.

This creates a challenge at the boundary between sharing results and exposing the capabilities that produce them. Customers and partners need usable information, clear interfaces and confidence in the analysis. The provider must decide how to meet these needs while retaining control over the models, benchmarks and learning processes that strengthen its position across deployments. As integrations expand, decisions about API access, permitted uses and rights to improvements shape the company’s future ability to benefit from its own experience.

This challenge connects directly to the CEIPI IP Business Academy analysis “The GreenTech Strategy Gap”. The analysis describes a mismatch between the interconnected strategic decisions facing sustainable innovation companies and IP advice presented through separate legal categories. Battery intelligence makes that need for integration tangible: patent strategy, confidential know-how, data access and collaboration agreements must support the same commercial objective.

Practice-based questions give students in CEIPI’s IP management programmes an opportunity to examine these choices in a concrete business setting. They connect the selection of protection mechanisms with decisions about growth, partnerships and the knowledge a company needs to retain.

We are therefore pleased to include this industry case study with Dr. Georg Angenendt, Managing Director of Angenendt Consulting GmbH. His practical question examines how a battery intelligence company can protect the learning layer of its analytics platform while delivering useful results into customer and partner systems. The case also reflects the separation between cloud-based analysis and the subsequent operating or trading decisions made by third parties.

Dr. Georg Angenendt explains the practical relevance of the case:

“In battery analytics, the lasting advantage lies less in any single algorithm than in the learning generated across a large and diverse database of battery systems. Delivering results into customer and partner integrations puts this advantage at risk if it is not done in the right way. This case reflects a decision every scaling battery intelligence company faces: the trade-off between openness towards customers and partners and protecting its own IP and the learnings from its analyzed battery fleet.”

Mini Case Study

A European battery intelligence scale-up has developed a cloud-based analytics platform for large battery energy storage systems (BESS) and electric fleets. The platform works primarily with raw measurement data from battery management systems, including cell and module voltages, current and temperature, together with operating conditions. Values already calculated by the battery management system, such as state of charge, are used mainly for comparison. The platform combines data preparation, physical battery models, statistical benchmarks and machine-learning methods to identify safety risks, performance losses, accelerated ageing and warranty-relevant deviations.

After successful deployments, the company is expanding internationally. Its platform is becoming part of the decision infrastructure used by asset owners, operators, integrators, energy traders, insurers and financing partners. Deployments require reliable data connections to different battery management systems and customer data environments. Battery chemistries, system topologies, data quality and operating profiles vary considerably between projects.

The platform gains value from experience across the installed base. Incoming data is cleaned, normalized and interpreted. The system generates diagnostic features, reference curves, model parameters, thresholds, risk indicators and recommended actions. These elements improve as the company encounters additional battery types, operating conditions and failure patterns. The resulting learning layer supports more accurate diagnostics and faster deployment for future customers.

Commercial scaling now creates a strategic dilemma. For stationary storage, analytics outputs are largely standardized across customers. The practical challenge is to make these results usable in customers’ operational and commercial decisions. Operators and energy traders use state-of-charge and state-of-health information, warnings and recommendations to support operating and trading decisions. Selected results are delivered directly to energy traders via APIs. Insurers and financing partners receive a defined subset of results directly via APIs for the assets they insure or finance, including relevant warnings and recommendations. Customers and partners need understandable results and workable interfaces, while each additional disclosure may expose part of the knowledge that creates the platform’s advantage.

The company is considering several complementary protection routes. Potential patent candidates include remote diagnostic processes, state estimation, fault detection and degradation analysis based on measured battery behaviour. Trade secrets may protect data transformations, feature engineering, calibration methods, threshold logic, validation routines, battery fingerprints and benchmark datasets. Contracts can govern access to customer data, permitted uses and onward sharing of analytics outputs, cross-customer learning and improvements generated during a deployment.

The technical and organizational architecture adds another difficulty. The analytics platform operates in the cloud and does not directly control customer hardware or provide control feedback to the on-site asset. Measurement, cloud analysis, delivery of results and subsequent action involve different systems and actors. Operators and energy traders decide how to act on the information within their own workflows. Competitors may reproduce diagnostic functions internally, offer them as a service or integrate them into other systems. The company needs a protection strategy that reflects this separation and offers a realistic basis for detecting imitation.

Management is preparing the next phase of growth across new markets, battery chemistries and ecosystem partnerships. It needs an IP architecture that defines the strategic control points of the platform, supports collaboration and preserves the company’s ability to capture value from the learning generated by its technology and deployments.

Practical Question

How should a battery intelligence company design its IP strategy to secure durable control over the learning layer of its cloud-based analytics platform while enabling standardized results and selected API outputs to support the operational and commercial decisions of customers and partners?

Which diagnostic functions, state-estimation methods and processing steps should be prioritized for patent protection, taking account of computer-implemented invention requirements and the practical detectability of third-party use? How should the strategy address the separation between cloud-based analytics and subsequent action by operators or energy traders, where the platform itself does not control the battery?

Which algorithms, parameters, diagnostic rules, validation methods, datasets and implementation capabilities should remain confidential, and how should the company organize secrecy so that these assets remain usable across a growing international team and partner network?

How should customer and partner agreements allocate rights to raw data, normalized data, derived indicators, model improvements, deployment-specific adaptations and cross-customer learning? Which standardized outputs, interfaces and explanatory information can be shared without exposing the underlying learning layer? How should API access, permitted use and onward sharing be defined for operators, energy traders, insurers and financing partners?

Please recommend a prioritized IP roadmap for the next 12 to 18 months that connects patent strategy, trade-secret governance, data and collaboration agreements, portfolio development and enforceability with the company’s planned international scaling.

Why This Question Matters in Practice

This question becomes relevant when battery analytics moves from isolated engineering projects into operational and commercial decision infrastructure. Its results inform decisions about safety, asset availability, lifetime, energy trading, warranty discussions, insurance and financing. Its commercial position depends on whether customers and partners view the platform as a source of differentiated intelligence or as an interchangeable software service. Standardized outputs must deliver practical value across these different decision contexts.

The economic value is distributed across several layers: access to operational battery data, data quality, diagnostic features, physical and statistical models, failure signatures, validation experience, cloud architecture, customer workflows and accumulated knowledge from multiple deployments. A competitor may need only selected layers to offer a commercially credible alternative. The IP strategy must therefore protect the combination that produces reliable decisions and customer outcomes.

Patent strategy is especially demanding in this environment. The assessment must address the technical contribution of diagnostic and state-estimation methods based on measured battery behaviour. It must also examine the consequences of separating data acquisition, cloud processing and subsequent operational action across different actors. The platform supplies information and recommendations without directly controlling customer hardware. A proposed protection strategy therefore needs to explain which steps occur within the platform, which depend on third parties and how use of the protected method or system could be detected and evidenced.

Data and collaboration rights shape the platform’s future learning capacity. A contract that gives away broad rights to derived data or customer-specific improvements can fragment the technology base. Restrictions on cross-customer learning can reduce diagnostic quality and slow entry into new markets. Unclear ownership of adaptations can create dependence on integrators. API arrangements also need to distinguish the right to use specified results for particular assets and decisions from access to the models, parameters, benchmarks and learning processes behind those results.

The practical challenge is to create controlled openness. Customers need confidence in the platform’s conclusions, partners need usable results and interfaces, and the company needs continued access to the learning that improves its service. A coherent IP architecture can align these requirements by defining which layers are exclusive, confidential, contractually governed, licensed or openly documented. The answer determines bargaining power, pricing power, partnership freedom and the defensibility of the battery intelligence business as it scales.

Dr. Georg Angenendt

Dr. Georg Angenendt is Managing Director of Angenendt Consulting GmbH and co-founder and former Chief Technology Officer of ACCURE Battery Intelligence. His professional background combines battery research, software development and the commercialization of cloud-based battery analytics. At ACCURE, he helped translate scientific expertise into predictive analytics supporting the safety, reliability and lifetime of battery storage systems. After six years of building the company, he stepped down from his operational CTO role in April 2026 and remains connected to ACCURE as a co-founder.

Georg studied electrical power engineering and earned his doctorate at RWTH Aachen University, where his research focused on battery safety, ageing and optimized operation. His scientific work included testing and modelling lithium-ion batteries and improving the operation of energy storage systems. Alongside his entrepreneurial activities, he mentors emerging technology companies through RWTH Innovation and Scale-up.NRW, supporting teams in technology commercialization, company development and the transition from research to market. His experience connects the technical foundations of battery intelligence with the practical challenges of scaling a data-driven technology business.

Here you can find the lecture: “IP Strategy for Data-Driven Businesses: Protecting Knowledge and Enabling Growth” regarding this exam question.

👉 CEIPI Lecture