Driving cycle


A driving cycle is a series of data points representing the speed of a vehicle versus time.
Driving cycles are produced by different countries and organizations to assess the performance of vehicles in various ways, as for example fuel consumption and polluting emissions.
Fuel consumption and emission tests are performed on chassis dynamometers. Tailpipe emissions are collected and measured to indicate the performance of the vehicle.
Another use for driving cycles is in vehicle simulations. More specifically, they are used in propulsion system simulations to predict performance of internal combustion engines, transmissions, electric drive systems, batteries, fuel cell systems, and similar components.
Some driving cycles are derived theoretically, as it is preferred in the European Union, whereas others are direct measurements of a driving pattern deemed representative.
There are two types of driving cycles:
  1. Transient driving cycles involve many changes, representing the constant speed changes typical of on-road driving.
  2. Modal driving cycles involve protracted periods at constant speeds.
The American FTP-75, and the unofficial European Hyzem driving cycles are transient, whereas the Japanese 10-15 Mode and JC08 cycles are modal cycles.
Some highly stylized modal driving cycles such as official European NEDC are designed to fit a particular requirement and bear little relation to real world driving patterns. On the contrary, the current WLTP is striving to mimic real word driving behavior. The most common driving cycles are probably the WLTP, NEDC, SORDS and the FTP-75, the later corresponding to urban driving conditions solely.
Driving cycle design is the core technology for these standard cycles. Optimization and Markov chains are employed to design a driving cycle.
Drive cycle recognition is applying to Hybrid Electric Vehicle.

Data Collection

Data collection from the test road is the most important activity. Test road measured data are the inputs to the 'Drive Cycle' preparation activity.
The procedure involves instrumentation of the test vehicle to collect information while driving on the test road. There are two major types of data to be collected, Driver Behavior data and Vehicle versus Road data. The Vehicle versus Road data are used to prepare the road drive cycle and the Driver Behavior data to prepare the Driver model. For example, to calculate a vehicle's fuel consumption either in computer simulation or in chassis dynamo-meter which is going to be launched in India, it must run on an Indian road with an Indian Driver. Indian Drive Cycle with a European driver model does not give a fair comparison of the on road trials.

Driving Cycle Design

The "Drive-cycle" basically is the representative of the road. Drive cycles are used to reduce the expense of on road tests, time of test and fatigue of the test engineer. The whole idea is to bring the road to the test lab or to the computer simulation.
Two kinds of drive cycle can be made. One is DISTANCE DEPENDENT and the other one is TIME DEPENDENT. The DISTANCE DEPENDENT is the actual replica of the test road whereas TIME DEPENDENT is the compressed version of the actual time taken to conduct the test on road. Examples of TIME DEPENDENT drive cycles are European NEDC cycle, FTP-75. TIME DEPENDENT drive cycles are used specifically for chassis dynamo meter testing because in a short time the results can be availed and repeated tests can be done easily.

Driving Cycle Recognition

Based on the type of application drive cycles are made. Drive cycle for passenger cars are different from commercial vehicle.

Driving Cycle Prediction

This is a technique for prediction of future driving cycles and patterns for different types of vehicle applications. These cycles are used as an important input in designing and evaluating future power train systems and vehicle concepts. As of today, obsolete drive cycles are used during the design phase and due to this the changes in traffic conditions and infrastructure which has occurred during the last decade are not taken into account. Therefore, the need for new drive cycles representing today or the next few decades is great. This technique can predict future drive cycle by integrating available measurement data, high-fidelity traffic simulators and traffic models for heavy vehicles. Desirably, traffic simulation models are automatically generated and used to collect predicted drive cycles.