Modular Model to Generate Drive Cycles for Estimating the Energy Consumption and Fuel Consumption of Heavy-Duty Vehicles.

The present work proposed in the thesis aims at developing a new methodology for generating artificial distance-based drive cycles and their validation for the sales tool. The sales tool is called Calculation and visualization applications (CAVA) which is used for the energy and fuel consumption calculation of heavy-duty vehicles (HDV’s) based on the inputs from the customers. CAVA needs a new tool for the energy consumption calculation. Therefore, the idea is to generate artificial drive cycles based on three coefficients: Average driving velocity, stop frequency (stops per 100Km), average absolute value road gradient. These three coefficients are the ones which will be input from the customers for CAVA. The idea is to pre-process all the data to create drive cycles which are sufficient to accurately estimate energy and fuel consumption. The stated three coefficients can be derived from operational data or existing drive cycles. The methodology described in the thesis uses the operational data logged from Scania vehicles running all over the world. The range of useful node points for average velocity, stops per 100 km and average absolute value of road gradient are processed from this operational data by statistical analysis. Gradient profiles are generated using a power spectral density approach and autocorrelation principles for randomly generated data sets for given average absolute values of road gradient. The velocity profile is constructed from a basic cycle followed by entering the desired number of stops and alteration of the final velocity profile according to the given acceleration and deceleration curves. The generated cycles are validated with a vehicle model against Vehicle energy calculation tool (VECTO) cycles and operational data to check the applicability of the methodology. The validation is performed in a simulation tool that uses longitudinal vehicle dynamics to simulate the desired vehicle model. The validation results for fuel and energy consumption of generated cycles against VECTO cycles and operational data show a deviation nearly within +-10% for most of the cases. Some of the cases lie outside the scope of operational data limits and hence the deviations are more than +-10%. It was analyzed that the deviation depends on the energy content of the cycles given by characteristic acceleration if stop frequency of cycles in comparison is same. The characteristic acceleration shows the transient nature of the cycle by taking into account kinetic, potential energy and number of stops. The energy content of a cycle is kinetic and potential energy over the complete cycle. The energy content is dependent on the velocity and gradient profile of cycles. It was also analyzed and reported that the base cycle design is a major factor for reducing the deviation in fuel and energy consumption of the generated cycle from VECTO cycles and operational data.