Illuminating Dark Data with Battery AI and Advanced Analytics
Batteries are more than energy storage devices. They can be a treasure trove of data-driven insights. However, today’s battery technology lacks the capabilities to collect, process, parse, and analyze data throughout the battery lifecycle to optimize performance and operations.
The unused and underutilized information—dark data—hinders our ability to leverage the power of analytics to inform timely and accurate actions in electrification strategies. Let’s explore how to illuminate dark data to help engineers improve product design, operators optimize revenue opportunities, and service providers meet fluctuating demand.
What is dark data in battery technology?
According to Gartner, dark data is the information assets organizations collect, process, and store during regular business activities. However, companies fail to tap into the data’s potential to inform operational and strategic decisions.
Dark data is money left on the table.
In battery technology, dark data encompasses all the untapped data generated throughout the battery lifecycle, from production to disposal. Most organizations hold onto the data they’ve collected from battery systems but fail to analyze and pull actionable insights from it to extend the value of their assets or inform business decisions.
Dark data may include telemetry from energy systems, battery cells, and other connected electrical circuits:
Manufacturing and production data may help us understand how temperature, pressure, humidity, etc. during production impact battery performance and quality.
Operational data from sensors may provide insights into how temperature, voltage, current, and state of charge (SoC) affect performance optimization or failure prediction.
Metrics on charge/discharge cycles, load profiles, and environmental conditions may offer insights into usage patterns, battery behavior, and longevity.
Maintenance logs may be analyzed to identify common failure modes, improve maintenance schedules, or support predictive analytics for proactive/preventive maintenance.
Degradation metrics may improve second-life applications and end-of-life recycling to minimize wastage and improve sustainability.
Environmental data may show how ambient temperature, humidity, and other conditions affect battery performance and lifespan.
The benefits of tapping into dark data
Tapping into dark data helps organizations maximize the benefits of data analytics to improve how they manage, maintain, and optimize battery solutions.
You may enhance proactive maintenance by correlating sensor data and operational logs to predict potential failures, minimize unplanned downtime, and extend battery lifespan. You may also gain insights into battery behaviors under various conditions to optimize performance and control costs.
Data analytics helps drive innovation by providing insights into how batteries behave in real-world conditions. Usage patterns may reveal new use cases, support the development of new applications, and help open new markets.
Real-time data from sensors and analytics help monitor temperature, voltage, and other critical parameters to enhance a battery system’s safety and reliability. Organizations can also implement risk mitigation strategies to improve long-term resiliency by understanding failure patterns and anomalies.
Challenges and considerations for harnessing dark data from battery systems
While there’s a lot of potential for battery systems to collect data and generate insights, we still face many challenges in turning information into action:
Data volume and variety
To tap into the power of analytics, organizations must start by generating a vast amount of data, including high-resolution sensor data, manufacturing logs, operational metrics, and more. However, processing, storing, and analyzing the information requires a complex data infrastructure and advanced capabilities.
Moreover, compatibility issues often make integrating data from disparate solutions challenging. Many legacy systems can’t handle the volume and complexity of modern data analytics. Yet, upgrading these systems can be costly and time-consuming while the avalanche of information may cause confusion or disruptions.
Data storage and management
Storing large volumes of data requires significant infrastructure investment. Organizations must balance the storage costs against the value of retaining data by distinguishing noise from actionable insights. Also, they must implement data management and governance policies for data retention and archiving to ensure compliance throughout the data lifecycle.
Data quality and consistency
Most traditional battery management systems (BMSs) and solutions don’t have the capabilities to collect granular data required for in-depth analytics. Moreover, inaccurate or incomplete data may lead to incorrect insights and poor decision-making.
Additionally, data from different systems and lifecycle stages comes in multiple types, formats, and units of measurement. Organizations must implement a standardization process to unify the information before using it to generate insights.
Data analytics capabilities
Leveraging dark data requires advanced analytics capabilities like AI and machine learning, which can be costly to build or acquire. Meanwhile, finding and retaining data scientists and engineers with expertise in handling and analyzing large datasets has become increasingly challenging due to the growing demand.
Data insights and action
While some battery analytics algorithms may be able to handle vast amounts of data and identify patterns, most aren’t sophisticated enough to generate meaningful and actionable insights. Besides, companies must have the structure to act on the information promptly at various levels (e.g., system, operation, strategy) to optimize outcomes.
Data privacy and security
Like any business operations involving data collection, analytics, and storage, privacy and security are top concerns for battery analytics. Organizations must implement robust data practices and security measures to comply with regulatory requirements, safeguard proprietary information, and protect user data from unauthorized access and theft.
Shedding light on dark data through advanced battery technologies
The ability to tap into dark data will revolutionize battery analytics and accelerate electrification. It’ll provide deep insights to enhance battery performance, predict failures, and optimize management. A systematic approach is essential for harnessing its potential.
1. Data collection and management
Identify all data sources across the battery lifecycle, including manufacturing processes, operational usage, maintenance logs, and recycling. Deploy sensors and monitoring devices to collect granular manufacturing, operational, maintenance, and end-of-life data.
Implement robust data processing and storage solutions to handle large volumes of diverse data types. Use trusted cloud platforms for scalability, flexibility, and accessibility to accommodate fast-growing data volume. Also, establish data governance policies and security controls to ensure data quality, regulatory compliance, and user privacy.
Additionally, use data integration tools (e.g., ETL/ELT software) to compile and standardize data with various formats, units of measurement, etc. from disparate sources to enhance interoperability and consistency.
However, most of today’s battery solutions/BMSs don’t provide a surface for scraping data. The Tanktwo Battery Operating System (TBOS) enables data collection with an API interface to unlock data from different operational levels at various degrees of granularity for external analyses.
2. Advanced analytics capabilities
Clean and preprocess data to remove noise and address missing values. Then, use big data analytics platforms to process and analyze vast amounts of information efficiently. They enable organizations to handle real-time data streams and generate immediate insights at scale.
Use machine learning and AI applications to uncover patterns, anomalies, and correlations. For example, Tanktwo’s AI-driven self-learning system creates and improves upon models based on data collected from real-life conditions and real-time operations to continuously analyze battery behaviors and adjust its predictions to provide accurate insights.
Also, implement visualization tools to help stakeholders understand complex data insights, track key performance indicators (KPIs), identify trends, and inform long-term battery and electrification strategies.
3. Turning insights into action
Analytics is only as valuable as its ability to inform timely and meaningful actions, such as optimizing battery management practices, improving operational cost-efficiency, supporting predictive maintenance, reducing wastage, and maximizing asset value and performance.
With Tanktwo’s AI capabilities and Dycromax™️ architecture, operators can collect data, monitor battery performance, and change battery behaviors on the fly based on shifting operating conditions and requirements. Our software platform also automatically adjusts charging cycles and routing to optimize battery longevity and performance.
For example, operators may determine when to charge an EV fleet with solar power based on weather forecasts and demand. They may charge the vehicles today when the sun is out to anticipate a spike in demand tomorrow when the weather is forecasted to turn cloudy. They may also charge batteries to 90% capacity to meet demand with the understanding that the additional revenue is sufficient to offset the wear on the batteries.
Besides minute-to-minute adaptation, organizations may use data insights to drive innovation in product development and electrification strategies. They may also establish a feedback loop to refine and improve battery management and analytics processes continuously.
TBOS: Built for data analytics from the ground up
Unlike traditional battery systems where data collection and analytics are tacked on as an afterthought (if at all), we built our SDB solution from the ground up to turn data into meaningful insights and insights into reactive and proactive actions.
Organizations can use the insights and reports to inform immediate actions and long-term strategic planning. For example, they may predict demand and automate processes based on usage patterns to optimize operations or maximize revenue generation.
Additionally, the built-in analytics capabilities and robust security mechanisms mean you don’t need to hire a large team of data analytics, engineers, and security experts to build the foundation for an effective and compliant data strategy. Meanwhile, machine learning capabilities distinguish noise from actionable insights to support timely decisions.
With the increasingly critical role of data analytics in every aspect of business operation, illuminating dark data is essential for the next stage of electrification. Our Battery Strategy Workshop helps you explore how you can apply battery analytics to optimize operations and accelerate your electrification strategy.
