Battery Lifetime

Extended Battery Lifespan

Predictive insights enable proactive maintenance, minimizing wear and tear and extending battery longevity.

Optimized Performance

Analytics-driven strategies enhance charging, discharging, and energy utilization, ensuring peak efficiency.

Reduced Downtime

Early anomaly detection and predictive maintenance reduce unplanned downtime, improving operational continuity.

Cost Savings

Efficient energy usage, reduced maintenance costs, and minimized downtime contribute to significant overall savings.

Battery Intelligent Analytics

The demand for Battery Intelligent Analytics has rapidly increased in today’s world of battery-dependent technologies. With the rise of electric vehicles, renewable energy solutions, and innovative grid systems, batteries have become vital components with significant implications. Inomo’s Battery Intelligent Analytics takes a comprehensive approach to battery management by utilizing advanced data analytics and predictive modelling techniques. By carefully examining battery data, these analytics provide valuable insights into performance trends, degradation patterns, and potential anomalies. This proactive approach enables predictive maintenance, optimized charging strategies, and improved safety measures.In the end, Inomo’s Battery Intelligent Analytics empowers industries to maximize battery systems’ efficiency, longevity, and reliability, which ensures the seamless advancement of modern technologies while minimizing risks and maximizing returns on investment.

Salient Features

State-of-Health Estimation: Assess the overall health and degradation level of batteries over time, providing insights into remaining useful life and performance potential.
Anomaly Detection: Identifies abnormal behaviour or deviations from expected battery performance, triggering alerts for immediate attention.
Performance Optimization: Recommends optimal charging and discharging strategies based on current battery state and usage patterns.
Predictive Maintenance: Utilizes historical data and machine learning algorithms to predict maintenance needs and prevent failures.
Visualization and Reporting: Presents data insights through visualizations, dashboards, and customizable reports for easy understanding and decision-making.

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Add-On possibilities

Multi-Battery Management: Manage and analyze data from multiple batteries or battery systems simultaneously, enabling centralized monitoring and comparison.
Scenario Simulation: Simulate different usage scenarios to predict how batteries perform under various conditions, helping users make informed decisions.
Integration with Asset Management: Integrate with the asset management systems to seamlessly incorporate battery data into overall operational strategies.

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