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In moving vehicles, the dominating energy losses are due to interactions with the environment: air resistance and rolling resistance. It is known that weather has a significant impact, yet there is a lack of literature showing how the wealth of openly available data from professional weather observations can be used in this context. This article will give an overview of how such data are structured and how they can be accessed in order to augment logs gained during vehicle operation or simulated trips. Two efficient algorithms for such data extraction and augmentation are discussed and several examples for use are provided, also demonstrating that some caveats do exist with respect to the source of weather data.

Out of necessity, emission and fuel consumption test cycles are a simplified representation of the real-life use of a vehicle or component that is assumed to be most common. In reality, variations are introduced by both the driver and the environment - and to a lesser degree also by the vehicle itself through performance deviations because of tolerances in the components' characteristics. However, since such simplified test cycles exist and are accepted (or even required by law), OEMs tend to use them also in product development to benchmark their products against the competition, and to make decisions on how to optimize design.While this approach might give acceptable results for on-road vehicles, it fails to capture reality in the case of versatile working machines. Here, the variety of possible applications cannot be covered by one common application but rather demands a mix of several cycles. This has a large impact on the setup and evaluation of physical and virtual testing of working machines, especially those with alternative power systems like hybrids, which will be discussed in the paper.Attempts to simplify the complexity of real-life applications are made in some testing standards and standard proposals, but they over-simplify with the result of prescribing a common cycle that is not representative and which therefore, however tempting, cannot be used for any meaningful benchmarking of emissions and fuel consumption.

In this study of 18 wheel loader operators, test-driving a machine in three different traction force settings, we found strong support for the hypothesis that the operator's control commands can be used to assess the machine's operability, at least in form of ease of bucket filling. The methods chosen to derive the control effort worked well and were computationally efficient.