Analysis and choice of representative drive cycles for light duty vehicles – case study for electric vehicles

IFAC Proceedings Volumes

Frisk

Nielsen

2013

Driving cycles are used for certification, for comparison of vehicles, and to an increasing extent as an engineering tool in vehicle design. A situation with only a few fixed driving cycles to use would then lead to the risk that a test or design would be tailored to details in the driving cycle instead of being representative. Due to this, and due to the increased use in the development process, there is now a strong need for methods to achieve representative driving cycles that in a wide sense are similar but not the same. To approach this problem area, we define equivalence between driving cycles based on mean tractive force, and develop algorithms and methods for equivalence-modification and equivalence-transformation of driving cycles. There are a number of applications for these methods but one example that is demonstrated is to transform the well-known FTP75 into an equivalent NEDC, and the other way around, to transform the NEDC into an equivalent FTP75.

Section: Introductionmentioning

confidence: 93%

Driving Cycle Adaption and Design Based on Mean Tractive Force

IFAC Proceedings Volumes

Frisk

Nielsen

2013

“…The certification driving cycles are sometimes criticized to not be representative of real-world driving (Zaccardi and Le Berr, 2012;Souffran et al, 2012;Ashtari et al, 2014) which means that using them for evaluation and design could be questioned. A representative driving cycle usually means that several statistical measures, e.g., mean speed or proportion of time in cruising mode, of the driving cycle are close to real-world driving conditions.…”

Section: Populärvetenskaplig Sammanfattningmentioning

confidence: 99%

Evaluation, Generation, and Transformation of Driving Cycles

Linköping Studies in Science and Technology. Dissertations

2015

Driving cycles are important components for evaluation and design of vehicles. They determine the focus of vehicle manufacturers, and indirectly they affect the environmental impact of vehicles since the vehicle control system is usually tuned to one or several driving cycles. Thus, the driving cycle affects the design of the vehicle since cost, fuel consumption, and emissions all depend on the driving cycle used for design. Since the existing standard driving cycles cannot keep up with the changing road infrastructure, the changing vehicle fleet composition, and the growing number of vehicles on the road, which do all cause changes in the driver behavior, the need to get new and representative driving cycles are increasing. A research question is how to generate these new driving cycles so that they are both representative and at the same time have certain equivalence properties, to make fair comparisons of the performance results. Besides generation, another possibility to get more driving cycles is to transform the existing ones into new, different, driving cycles considering equivalence constraints.With the development of new powertrain concepts the need for evaluation will increase, and an interesting question is how to utilize new developments in dynamometer technology together with new possibilities for connecting equipment. Here a pedal robot is developed to be used in a vehicle mounted in a chassis dynamometer and the setup is used for co-simulation together with a moving base simulator that is connected with a communication line. The results show that the co-simulation can become a realistic driving experience and a viable option for dangerous tests and a complement to tests on a dedicated track or on-road tests, if improvements on the braking and the vehicle feedback to the driver are implemented.The problem of generating representative driving cycles, with specified excitation at the wheels, is approached with a combined two-step method. A Markov chain approach is used to generate candidate driving cycles that are then transformed to equivalent driving cycles with respect to the mean tractive force components, which are the used measures. Using an optimization methodology the transformation of driving cycles is formulated as a nonlinear program with constraints and a cost function to minimize. The nonlinear program formulation can handle a wide range of constraints, e.g., the mean tractive force components, different power measures, or available energy for recuperation, and using the vehicle jerk as cost function gives good drivability.In conclusion, methods for driving cycle design have been proposed where new driving cycles can either be generated from databases, or given driving cycles can be transformed to fulfill certain equivalence constraints, approaching the important problem of similar but not the same. The combination of these approaches yields a stochastic and general method to generate driving cycles with equivalence properties that can be used at several instances during the prod...

“…Usually a representative driving cycle means that some criteria of interest is sufficiently close to data from real-world driving. The general consensus is that NEDC is not a representative of real-world driving [Fontaras andDilara, 2012, Zaccardi andLe Berr, 2012]. Zaccardi and Le Berr [2012] studies how several different driving cycles can be used together in order to represent real-world driving.…”

Section: Introductionmentioning

confidence: 99%

“…The general consensus is that NEDC is not a representative of real-world driving [Fontaras andDilara, 2012, Zaccardi andLe Berr, 2012]. Zaccardi and Le Berr [2012] studies how several different driving cycles can be used together in order to represent real-world driving. However, the results of one driving cycle is difficult to compare to the results of another driving cycle since two different driving cycles can have very different vehicle excitation.…”

Section: Introductionmentioning

confidence: 99%

Generation of Equivalent Driving Cycles Using Markov Chains and Mean Tractive Force Components

IFAC Proceedings Volumes

Frisk

Nielsen

2014

In the automotive industry driving cycles have been used to evaluate vehicles in different perspectives. If a vehicle manufacturer focuses only on a fixed driving cycle there is a risk that controllers of the vehicle are optimized for a certain driving cycle and hence are sub-optimal solutions to real-world driving. To deal with this issue, it is beneficial to have a method for generating more driving cycles that in some sense are equivalent but not identical. The idea here is that these generated driving cycles have the same vehicle excitation in the mean tractive force, MTF. Using the individual force components of the MTF in the generation of driving cycles with Markov chains makes it possible to generate equivalent driving cycles that have the same vehicle excitation from real-world driving data. This is motivated since the fuel consumption estimation is more accurate when the MTF components are considered. The result is a new method that combines the generation of driving cycles using real-world driving cycles with the concept of equivalent driving cycles, and the results are promising.