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Can Knowledge Graph Extend Battery Life in Electric Vehicles? You bet.

By Maria Singson | March 31, 2021

The ultimate innovation in electric vehicles is extending battery life without sacrificing the driving experience. In a highly competitive market, now more than ever, convincing consumers to convert to electric cars boils down to competing in battery life, where consumers count every extra second between charging that they are promised. Even though driving styles and conditions can affect battery life, the ultimate scorecard is still about battery performance itself. Mastech InfoTrellis recently helped an electric car company design an easy-to-read heads-up dashboard (HUD) with predictive maintenance capabilities so that drivers can adapt their driving styles while the car beams up driving data to an Intelligence Hub and continually receives dynamic battery life forecasts to inform the driver.

How did we do this? Why, with AI Accelerators applied on knowledge graph, of course!

At Mastech Infotrellis, we have AI Accelerators designed to democratize AI Analytics when solving business problems. With Ontology cards giving intelligence to the knowledge graph and SIE making all data graph-ready, the rest of the AI Accelerators “live” on the graph. While the first two enable enterprises to get started with knowledge graphs, the rest enable them to leverage graphs ASAP.


Signals to alert the drivers

With Ontology cards guiding the metadata needed, and the Smart Ingestion Engine making GPS and sensor data graph-consumable, Mastech InfoTrellis was able to customize a HUD that correlated driving style to battery consumption in real-time. Behind the interface, the car manufacturer can triangulate sensor and GPS data with driving behavior, thanks to the predictive maintenance knowledge graph we have built. On the HUD itself, a more customer-centric knowledge graph helped design a simple interface that is user-friendly so that it does not distract the driver and does not deter from the car’s aesthetics, and yet sends the proper warnings to the driver as needed.

With DQi, the car manufacturer was able to have ongoing monitoring of the car’s conditions, specifically focused on factors that contributed to battery life. Meanwhile, Feature Miners were used to look for signals of car malfunction or excessive battery usage based on driver behavior. With knowledge graph behind the scenes, crucial signals of malfunction – some intuitive, some not – were brought to light, creating early warnings for the car manufacturer to improve the design of its battery, but more importantly, for the driver to always be informed of the condition of their car and battery as directly correlated with their driving behavior.

Safety and security boost customer trust

This case study on electric vehicle manufacturing illustrates the power of knowledge graph and AI Accelerators. When installed on automobile dashboards and relied upon by millions of drivers every day as they commute to work or travel long distances, knowledge graph-powered dashboards gain the customers’ trust quickly. Ultimately, extending battery life is about informing the driver on the best actions and driving decisions given the car’s current condition. When there are fewer failures of parts, battery included, customer satisfaction and trust improve. Mastech InfoTrellis helped the electric vehicle manufacturer achieve a 37% lift in NPS (net promoter score), translating into $200M+ of revenue.

Maria Singson
Maria Singson
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VP & General Manager, Data Science

She has founded startups in analytics, retail and the fashion industries.