Enhancing Vehicle Emission Monitoring Through Tree-Based Machine Learning: Optimizing IUPR Under BSVI Stage 2 Regulation

The new Bharat Stage (BS) VI Stage 2 regulation for automotive vehicles in India requires monitoring the performance of emission control components, such as Selective Catalytic Reduction (SCR) systems, Diesel Oxidation Catalysts (DOCs), Diesel Particulate Filter (DPF), Nitrous Oxides (NOx) Sensors, and Exhaust Gas Recirculation (EGR). The regulation also mandates that a minimum In-Use Performance Ratio (IUPR) must be met, which is the ratio of the number of times a component's performance is monitored to the number of drive cycles the engine has undergone. The IUPR must be tracked throughout the vehicle's lifetime after an initial run-in period. In an effort comply with the minimum IUPR requirement, the engine and after-treatment system calibrations must ensure that the conditions and threshold ranges for enabling performance monitoring of emission-critical components are met across all vehicles operating duty cycles and varying geographic conditions. This study explores the novel method of using Tree Based Machine Learning classifier coupled with tree traversing technique to improve the IUPR for different vehicle duty cycles and emission components. The findings of the proposed techniques are presented that determines the ideal threshold ranges, therefore optimizing the performance monitoring window for a certain duty cycle and lowering the manual efforts by many times.