AI ML Tool for Enhancing RDE Trip Validity G. Sathya Narayanan* 1, Rajesh Kibile 2, Milind Bhamare 1, Kedar Marathe 2 1Tata Motors Ltd, India 2Tata Technologies, India KEYWORDS – Real Driving Emissions, Trip validity, Trip Normality, Artificial Intelligence, Machine Learning. ABSTRACT - Objective: Real Driving Emission test, is the most important regulatory standard to be met for BS6 compliance. RDE tests verify compliance to emission limits during on-road trials. Government authorities have set ~30 boundary conditions on conducting these trials to ensure unbiased emission assessment. Failing to meet even 1 condition renders the trial invalid and needs to be repeated. The success rate of executing a valid RDE test was just 30% prior to the use of our innovation. To ensure high success rate, there needed to be an intelligent system that continuously directs the driver by analyzing and forecasting emission patterns. Methodology: To ensure high success rate, historical data of PASSED & FAILED trials is fed as input to an AI engine. The engine builds relationship between driving behavior, terrain patterns, traffic conditions and corresponding emission patterns. CO2 emissions during a RDE trial are highly retrospective. Hence, CO2 patterns during a trial may get affected by something that driver did during very early phase of a trial. To train a machine learning model which takes into account such long-term effects, a novel 3-step approach is developed. The trained ML model is deployed on a laptop connected to vehicle systems. During the trial, by accessing vehicle & emission data in real-time, the system characterizes vehicle’s behavior, predicts future emissions based on current driving style, and provides actionable instructions to driver. The system is adaptive to ever-changing parameters that impact emissions, and generates assistance that ensures valid trial under any environmental and traffic conditions. Results: Post tool development, RDE trip validity has increased from 35% to 75%, which has benefitted to expedite RDE development and meet program timelines with less number of RDE tests. On broad level, this tool helps significantly to optimize our vehicles to perfect trade-off point, where we achieve robust RDE emissions along with optimal fuel efficiency (CO2) & benefit on reduction of greenhouses gases. Limitations of the study: Currently the AI model trained on historical PASS/FAIL data, is unique to every vehicle. For any major change in the powertrain, or vehicle, the model needs to be retrained and fine-tuned based on 2-3 RDE trial data. Once the model is retrained, it can be used for further RDE trials. Based on historical data across various vehicle models augmented with numerical representation of vehicle aggregates and properties like weight, shape, etc. we are in a process of training a generic AI model which will work across various vehicles without the need of fine-tuning. Uniqueness • Going beyond real-time status: Apart from only showing the real-time status, the system predicts emission patterns in the future and assists the driver to avoid non-compliant behavior. The AI-algorithm models driver's never-before-seen driving behavior, effects of terrain, traffic, and engine calibration to achieve this. • Assistance not depending on route/recipe: Humidity, temperature, traffic conditions etc. keep on changing during trials, and hence it is impossible for a driver to follow a pre-defined recipe to achieve validity. Our system ensures valid trial under any environmental /traffic conditions. • The unique 3-step modelling approach is unavailable in any commercial AI-ML platform. Conclusion: The technical challenge of complying the CO2 based trip normality requirement on the test vehicle was successfully addressed with the AI-ML based tool by which the real-time monitoring and prediction of futuristic CO2 values was made possible, along with simplest voice commanding option was used for providing real-time guidance for the driver to align his driving style for meet the CO2 normality requirement during on-going trial itself. This has significantly improved the trip validity from 35% to 75%.

Author - Mr. Jagdish Jadhav, Tata Motors Ltd, India 1, (*) Co-authors - Mr. Kunal Gaikwad, Tata Motors Ltd, India 1, Mr. Sandeep Nevase, Tata Motors Ltd, India.1 KEYWORDS – Light weighting, BIW Analysis, Automotive benchmarking, PMXU study, VAVE Objective: Automotive benchmarking is a crucial aspect in-order to innovate, enhance and evolve by physical and digital analysis of competitor’s vehicles to understand their functionality, design and packaging. This data is useful to perform VAVE and optimize the part to achieve weight and cost reduction. Body-in-white (BIW) viz. Vehicle’s welded steel structure is a vital part and has high potential for light weighting as it contributes to nearly 40% of vehicle’s kerb weight. The main objective is to know the recent trends and technologies by doing benchmarking and deep analysis of competitor’s BIW to achieve significant light weighting targets. Methodology: The methodology below aims to describe the approach adopted to deeply analyze the BIW and achieve significant light weighting targets. We have adopted a 12 step detailed approach towards BIW teardown and analysis comprising of: 1. Torsion and Bending testing of BIW to analyze the stiffness value. 2. 3D scanning of BIW interior and exterior. 3. Dismantling BIW Panels wise. 4. Identifying weld spot strategy used by competitors. 5. Analyze sealant strategy used by competitors. 6. 3D scanning of BIW panels. 7. CAD Conversion of 3D scan data. 8. Material analysis to identify the material composition and grades. 9. Measure BIW panel stiffness. 10. Identify grommets and melt sheet strategy used by competitors. 11. Structured VAVE Approach. 12. PMXU strategy. Results:  Development of competency for BIW analysis with a structured approach.  Understanding competitor’s platform strategy in BIW design.  Evolution of a Novel process in Level Next benchmarking complying with industry 4.0.  Achieve weight and cost reduction targets.  Achieved reduction in carbon footprints.  Benchmark Database creation for future projects. Limitations of this study: Since this was the Level Next approach we had limitations to generate a CT scan inspection of the BIW before physical teardown. The CT scan technology would have helped us for better visualization of internal panels of BIW. What does the paper offer that is new in the field including in comparison to other work by the authors? The structured approach to bring accurate digital validation of competitor’s welded steel structure i.e. BIW is novel in its sense. For any OEM they have design data of their own product but there is no way to get competition design data on which we can perform certain objective level analysis which is comparable with OEM’s product design. The structured step by step process developed by the team will ensure capturing of competition data systematically and then would be converted into the digital form to perform the objective level analysis. Conclusions: By developing our systematic process of BIW level teardown and benchmarking we have brought the benchmarking to whole new level by developing this capability as a way of life, we could understand the competition strategy over a period of time and at the same time can build our strategy for our future products. This process will be the continuous practice so as to capture the technological advancements in terms of Material Science, Manufacturing process and light- weighing strategy.