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Vehicle functional requirements, emission regulations, and thermal limits all have a direct impact on the design of a powertrain cooling airflow system. Given the expected increase in emission-related heat rejection, suppliers and vehicle manufacturers must work together as partners in the design, selection, and packaging of cooling system components. The goal of this two-day course is to introduce engineers and managers to the basic principles of cooling airflow systems for commercial and off-road vehicles.

Motivation With an increase in the complexity involved in modern-day ECUs (Electronic Control Unit), it is very important to verify and validate robustness, functionality, and reliability of ECUs. Till date, Hardware in Loop (HiL) based validation or vehicle level validation is generalized approach used for testing. However, this method requires physical setup, which can incur more cost and time during the development phase. Solution We believe in minimizing the software testing time using Software in Loop (SiL) validation. Creating virtual-ECUs and the plant models is an important step in SiL. We are presenting the modularized and scalable plant models in this paper. Adaptable SiL Solution for EV This paper focuses on the standardization of electric vehicle plant models, which can help users to reduce the software development and software testing time.

Electric Vehicles are rapidly growing in the market yet various failure incidents were noted all along the globe. On the other hand, range anxiety has greatly inspired manufacturers to explore new practices to improve. One of the most important components of an EV is the battery, which converts chemical energy to electrical energy thereby liberating heat energy as the loss. This heat loss when high, capability of system to deliver required energy to wheels is reduced significantly. With a higher heat loss in the battery, system is prone to failure or reduced mileage. Therefore, controlling/maintaining system temperature under safe usable zone even during harsh conditions is ideal. Simple reduction in energy consumption of electrical cooling/heating devices used with regenerative energy techniques can greatly help in range improvement. The intention of this paper is to explore easy methods to improve electric vehicle range in different ambient conditions.

In order to meet stringent emission targets and to achieve better fuel efficiency, closed loop air mass control strategies have become essential across all vehicle segments. Closed loop air mass control mandates measuring fresh air mass entering the engine combustion chamber. However, in Naturally Aspirated (NA) engines, while measuring air mass using conventional methods such as Hot Film Air mass (HFM) sensor, heavy pulsations in the Air-intake results in errors which would impact closed loop air mass control and lead to inconsistencies in emissions. To address this issue, we studied different approaches using HFM sensor with Resonator, differential pressure sensor across the intake air filter and Lambda based air mass control. Based on this empirical study we found that modelling air mass with differential pressure sensor using Bernoulli’s principle (Flow rate ∝ √Differential pressure) results in higher accuracies compared to conventional methods.

Powertrain complexity rapidly increasing to meet fast moving regulation requirements. The BS6 Phase-1 regulation norms were implemented in India from April 1, 2020 and replaced the previous BS4 norms. Phase-2 of the BS6 regulation norms were came into effect on April 1, 2023. To meet this stringent regulation requirement, need effective performance of after treatment systems like DOC, DPF and SCR demands critical hardware selection and implementation. A robust DeNOx emissions strategy is developed in naturally aspirated single cylinder LCV application to meet cycle emissions, real drive emissions and OBD requirements. Naturally aspirated single cylinder engine has its own challenges to meet BS6 norms like higher engine out NOx, dynamic temperature profiles etc. It is always a challenge to adapt EATS in LCV application as these are low-cost vehicles. EATS components and sensors impacts the overall cost of the vehicle.

Abstract- As the automotive industry is coming up with various ADAS solutions, RADAR is playing an important role. There are many parameters concerning RADAR detections to acknowledge. Unsupervised Clustering methods are used for RADAR applications. DBSCAN clustering method which is widely used for RADAR applications. The existing clustering DBSCAN is not aligned very well with its hyperparameters such as epsilon (the radius within which each data point checks the density) and minimum points (minimum data points required within a circle to check for core point) for which a calibration is needed. In this paper, different methods to choose the hyperparameters of DBSCAN are compared and verified with different clustering evaluation criteria. A novel method to select hyperparameters of the DBSCAN algorithm is presented with the paper. For testing the given algorithm, ground truth data is collected, and the results are verified with MATLAB-Simulink.

Abstract- Driver safety has become an important aspect. To have driver safety RADAR is an essential part of vehicles hence RADAR has great significance in the automotive industry. The Radar sensor collects data from surroundings that may have unwanted data that may lead to improper detections of intended objects, so to have proper object detections it is needed to use clustering methods on the radar point cloud data. There are numerous unsupervised clustering methods used for RADAR applications. In this paper, the comparisons of different unsupervised algorithms such as K-Means Clustering, Hierarchical Clustering, KNN (k-nearest neighbors), Cluster Using the Gaussian Mixture Model, and DBSCAN are presented. All these clustering algorithms are evaluated based on various evaluation criteria such as the Silhouette coefficient, Davies Bouldin index, etc. Based on evaluations and comparative studies applications of the clustering algorithms are classified.