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As the FTP-75 drive cycle does not have a prescribed gear shift pattern, automotive OEMs have the flexibility to design. Conventionally, gear shift pattern was formulated based on trial and error method, typically with 10 to 12 iterations on chassis dynamometer. It was a time consuming (i.e. ~ 3 to 4 months) and expensive process. This approach led to declaring poor fuel economy (FE). A simulation procedure was required to generate a gear shift pattern that gives optimal trade-off amongst conflicting objectives (FE, performance and emissions). As a result, a simulation tool was developed in MATLAB to generate an optimum gear shift pattern. Three different SUV/UV models were used as test vehicles in this study. Chassis dyno testing was conducted, and data was collected using the base and optimized gear shift patterns. Dyno test results with optimized gear shift pattern showed FE improvement of ~ 4 to 5% while retaining the NOx margin well above engineering targets.

Determination of vehicle specifications (for example, powertrain sizing) is one of the fundamental steps in any new vehicle development process. The vehicle system engineer needs to select an optimum combination of vehicle, engine and transmission characteristics based on the product requirements received from Product Planning (PP) and Marketing teams during concept phase of any vehicle program. This process is generally iterative and requires subject matter expertise. For example, accurate powertrain sizing is essential to meet the required fuel economy (FE), performance and emission targets for different vehicle configurations. This paper analyzes existing vehicle specifications (Passenger Cars/SUVs - Gasoline/Diesel) in different automotive markets (India, Europe, US, Japan) and aims to determine underlying trends across them.

Poor clutch life is a major issue for some light commercial vehicle models. Clutch overheating is the primary cause for clutch failure. Some of the reasons include inappropriate gear selection by the driver, poor low-end dynamic torque availability from an engine, heavy stop and go traffic, vehicle overloading resulting in excessive clutch slippage especially in gradients, riding of the clutch pedal by the customer etc. These situations lead to a high thermal energy dissipation at the clutch, increasing clutch wear and in extreme conditions leading to not only poor shift quality but also eventual clutch failure. Unfortunately, it is not practical to monitor clutch temperature in a production vehicle due to high costs or technical challenges involved. This paper describes 1-D thermal modeling of single plate dry clutch typically used in passenger car/truck and bus applications. The objective of simulation is to estimate the temperature rise on the clutch facing and clutch housing.

Each OEM has a distinguishing drivability character that defines its image in the market to achieve brand differentiation. Drivability is one of the important factors along with fuel economy that determines the success of a vehicle vis-à-vis its competitors. It can be said that the need for good drivability among customers is increasing day by day similar to the need for high fuel economy. Drivability is the response that a vehicle delivers to the inputs of the driver which are mainly accelerator, brake, clutch, gear and steering. The dynamic response of the vehicle is mainly in terms of velocity and acceleration. The way the response is delivered will characterize the drivability of a vehicle. The drivability event discussed in this paper is throttle tip-in response which is one of the critical evaluation factors for defining the character of a Sports Utility Vehicle.

Objective metrics for performance evaluation of ride, handling and steering are required to compare, validate and optimize dynamic behavior of vehicles. Some of these objective metrics are recommended and defined by International Organization for Standardization (ISO) and Society of Automotive Engineers (SAE), which involve data processing, statistical analysis and complex mathematical operations on acquired data through simulation or experimental testing. Due to the complexity of operations and volume of data, evaluation is often time consuming and tedious. Process automation using existing tools such as MS Excel, nCode, Siemens LMS, etc. includes several limitations and challenges, which make it cumbersome to implement. This work is about development of a centralized platform for quantification, visualization and comparison of ride, handling and steering performance metrics obtained from testing and simulation data as per relevant ISO standards.