Road-Lab-Math (RLM) Strategy for Improving Vehicle Development Efficiency

2021-26-0193

09/22/2021

Features
Event
Symposium on International Automotive Technology
Authors Abstract
Content
In today’s Indian automotive industry, vehicles are becoming more complex and require more efforts to develop. Also, new and upcoming regulations demand more trials under varied driving conditions to ensuring robustness of emission control. Combined with expectations of customer to get new products more frequently, requires solutions and methods that can allow more trials with required accuracy to ensure compliance to stricter regulation and delivery a quality product. This translates into more trials in less time during the development life cycle.
Recently, to overcome above challenge, there has been focus on simulating the vehicles trials in engine bench environment. ‘Road to Lab to Math’ (RLM) is a methodology to reduce the effort of On-road testing and replace it with laboratory testing and mathematical models. Also, on-road testing of prototype vehicles is expensive as it requires physical parts. Replacing these parts with mathematical models or simulating vehicle like environment on engine helps in reducing cost as well as effort to prepare prototype vehicles.
Considering above context, this study attempts to address the challenges and investigates factors to achieve close correlation of vehicular fuel economy and emissions on engine bench environment and ways to overcome these challenges in order to attain good correlation between vehicle & engine on various drive cycles. In addition to efficiency improvement observed, the investigated factors help to understand the improvement in accuracy each factor provides which may help engineers to optimize efforts in deploying such approach for automotive development.
Meta TagsDetails
DOI
https://doi.org/10.4271/2021-26-0193
Pages
6
Citation
Rajak, R., Dembla, S., Sarna, N., Mehra, H. et al., "Road-Lab-Math (RLM) Strategy for Improving Vehicle Development Efficiency," SAE Technical Paper 2021-26-0193, 2021, https://doi.org/10.4271/2021-26-0193.
Additional Details
Publisher
Published
Sep 22, 2021
Product Code
2021-26-0193
Content Type
Technical Paper
Language
English