Powertrain Data Analytics and Prognostics for a European OEM

Background

The shift toward connected, software‑defined vehicles is pushing the automotive industry to rethink how reliability and performance are assured across ICE, hybrid and EV platforms. Rising data complexity demands stronger analytics, predictive insights, and scalable health monitoring to meet global expectations for safer, smarter, and more resilient mobility solutions.

The automotive OEM required a partner to advance its data‑driven engineering capabilities in order to accelerate validation, improve reliability, and enhance the customer experience.

Challenge

The automotive OEM was grappling with the growing need to modernize its engineering workflows as vehicles became more complex across ICE, hybrid, and electric platforms. The organization needed to evolve its data‑driven capabilities to keep pace with faster development cycles and rising expectations for durability and customer satisfaction. This required addressing gaps in prognostics and diagnostics, making better use of real‑world operating data, improving visibility across vehicle programs, and scaling health analytics to support more accurate performance assessments. Overcoming these challenges is essential for the OEM to deliver reliable products, shorten validation timelines, and maintain competitiveness in a rapidly changing mobility landscape.

Solution

Tata Elxsi partnered with the OEM to develop an intelligent tool to streamline and centralize access to vehicle program status data for engineering teams. In parallel, large-scale descriptive analytics were applied to monitor and assess powertrain system health across thousands of vehicles, enabling data-driven insights at fleet scale. In addition, we worked together to design and deploy advanced analytics solutions that accelerate engineering response times and deliver deeper system-level insights. The solution included development of powertrain subsystem prognostics for early detection of component degradation, along with advanced EV charging and communication analytics. Comprehensive powertrain subsystem evaluations were enabled by leveraging real-world vehicle usage data across global regions.​

Integrated Technology Stack​

• Data & Cloud Infrastructure: GCP, AWS & Snowflake​
• Distributed Compute & Orchestration: Dask Clustes,Kubeflow​
• Machine learning & Modelling: Python, Tensorflow, PyTorch,Scikit-learn/XGBoost
• Visualization & Reporting: Tableau.

Impact

• Up to 30% warranty cost reduction through early detection of powertrain degradation trends using predictive RUL analytics
• 3x faster identification of recurring charging issues through automated charging session analytics.
• Up to 30% faster detection of durability and reliability risks through large-scale powertrain system health monitoring.
• Up to 75% reduction in manual data processing effort, enabling faster vehicle program reviews and accelerated engineering decision-making.
• Greater confidence in system performance through environment- and region-specific powertrain validation

Services Rendered

• Prognostics & Health Management
• Data Analytics & Dashboards
• Data Pipeline & Enrichment
• Operations & Interface Layer​