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In the move towards cleaner diesel emissions, the European On Board Diagnostics (EOBD) legislation mandates monitoring of drift of air mass flow sensor. Drift of a sensor is defined as the phenomenon in which output signal slowly deviates independent of the measured property. Long term drift usually indicates a slow degradation of sensor properties over a long period of time. Drift monitoring of the air mass flow sensor involves comparing the signal from the sensor with a reference signal under special operating conditions. Boost pressure sensor, which measures absolute intake manifold pressure and intake air temperature, is used to calculate the reference signal. For engines with constant geometry turbo charger, boost pressure sensor is solely used for drift monitoring. Therefore, it was a challenge to come up with a means of finding the drift in air flow mass sensor without boost pressure sensor.

Validation of vehicle structure is at the core of reduction of product development time. Robust and accelerated validation becomes an important task. In service the vehicle is subjected to variable loads. These act upon the components that originate from road roughness, manoeuvres and powertrain loads. Majority of the body in white and chassis structural failures are caused due to vertical loading. Measured road load data in test track have variable amplitude histories. These histories often contain a large percentage of small amplitude cycles which are non damaging. This paper describes a systematic approach to derive the compressed load cycle from the measured road load data in order to produce representative and meaningful yet economical load cycle for fatigue simulation. In-house flow was developed to derive the compressed load time history.

In current competitive environment automobile industry is under heavy pressure to reduce time to market. First time right design is an important aspect to achieve the time and cost targets. CAE is a tool which helps designer to come up with first time right design. This also calls for high degree of confidence in CAE simulation results which can only be achieved by undertaking correlation exercises. In automobiles most of the structures are subjected to vibration from dynamic loads. All the dynamic road loads are random in nature and can be very easily expressed in terms of power spectral density functions. In the current scenario structural durability of the parts subjected to vibration is done partially through modal performance and partially though frequency response analysis. The only question that arises is what amplitude to use at what frequency and how to map all the accelerated tests dynamic load frequency spectrum to simulation domain.

Drive cycles have been an integral part of emission tests and virtual simulations for decades. A drive cycle is a representation of running behavior of a typical vehicle, involving the drive pattern, road characteristics and traffic characteristics. Drive cycles are typically used to assess vehicle performance parameters, perform system sizing and perform accelerated testing on a test bed or a virtual test environment, hence reducing the expenses on road tests. This study is an attempt to design a relatively robust process to generate a real world drive cycle. It is based on a Six Sigma design approach which utilizes data acquired from real world road trials. It explicitly describes the process of generating a drive cycle which closely represents the real world road drive scenario. The study also focuses on validation of the process by simulation and statistical analysis.

The life of a two-wheeler and its parts depend much on its usage during its years of running. The quality of its parts determine the life and efficiency; however the handling of the two-wheeler also plays a major role in estimating it's life and other performance parameters. Hence, it is beneficial to have an efficient system which enhances the life of a two-wheeler and also gives better mileage. This paper constitutes an efficient drive pattern system which addresses the above. This system consists of two main parts: the data collection system and an Android-based mobile application which runs on a mobile phone. The data collection system collects data from various sensors on the vehicle and then the data is processed and sent to the mobile phone of the rider during the run time of the two-wheeler. The application uses this data to depict useful information like drive pattern and various indicators.

Ride is essentially the outcome of coupled dynamics of various involved sub-systems which make it too complex to deal analytically. Tires, amongst these, are known to be highly nonlinear compliant systems. Selection of tires specifications such as rated tyre pressure, etc. are generally decided through subjective assessment. While experts agree that tyre pressure affects the attributes such as ride to a noticeable degree, the quantification of the change often remains missing. In the current work, vibration levels of various sub-systems relevant to ride in an SUV are measured for three different tyre pressures at different speeds over the three randomly generated roads. For the purpose, artificial road profiles of classes A, B and C are synthesized from the spectrum of road classes defined in ISO 8608:2016 and reproduced on a four-poster test rig.