internal and external parameters can influence the energy consumption of the
system. The graph shows the complexity of energy consumption modelling and the
number of parameters that have to be considered to obtain a realistic usage scenario.
As a result, only experimental data with specific parameters were chosen. Many
parameters are directly chosen by the customer or the end-user (in this case the
driver). Consequently, setting these parameters implies a thorough understanding of
end-user behaviours, which can be influenced by different factors (social and personal norms, awareness, habitual processes as routine, intentional processes as
willingness for environmental habits and situational influences as surrounding
environment) [21]. In this study, we have chosen a few parameters to analyse based
on expert knowledge of end-user behaviour and the most commonly used parameters in scientific literature.
2.3 Scenario Studied
In order to define the usage scenario, two collection methods were chosen based on
the four kinds in Table 1: average scenario data and experimental data.
First a European usage scenario based on average data was modelled and then
nine specific scenarios were modelled from experimental measurements using
different end-user behaviours and usage settings.
Although it is known that refrigerant leakage [12] significantly contribute to the
total environmental impact of the sue phase of TRUs, in this paper we focus only on
the influence of energy consumption.
2.3.1 Average Usage Scenario
The European average usage scenario was selected. It is a combination of the
business activity and one TRU setting:
• Temperature of transportation (0 °C for fresh product or −20 °C for frozen
product). This parameter is directly linked to the business activity of the
customer.
• Operating mode (start/stop or continuous run). The parameter is chosen by the
end-user. It is selected depending on transported product (sensitive or not) but
there is no obligation from the manufacturer.
This results in an average scenario of use (Table 2) with an average energy
consumption associated of 1 l/h (this value is defined as a normalized value; it is
not the raw value). A weighting factor, obtained from the analysis of 150 TRU
usage data sets based on time, has been defined for each combination of parameters
and it is indicated here to better illustrate the average European sharing.
Is It Useful to Improve Modelling of Usage Scenarios …
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system. The graph shows the complexity of energy consumption modelling and the
number of parameters that have to be considered to obtain a realistic usage scenario.
As a result, only experimental data with specific parameters were chosen. Many
parameters are directly chosen by the customer or the end-user (in this case the
driver). Consequently, setting these parameters implies a thorough understanding of
end-user behaviours, which can be influenced by different factors (social and personal norms, awareness, habitual processes as routine, intentional processes as
willingness for environmental habits and situational influences as surrounding
environment) [21]. In this study, we have chosen a few parameters to analyse based
on expert knowledge of end-user behaviour and the most commonly used parameters in scientific literature.
2.3 Scenario Studied
In order to define the usage scenario, two collection methods were chosen based on
the four kinds in Table 1: average scenario data and experimental data.
First a European usage scenario based on average data was modelled and then
nine specific scenarios were modelled from experimental measurements using
different end-user behaviours and usage settings.
Although it is known that refrigerant leakage [12] significantly contribute to the
total environmental impact of the sue phase of TRUs, in this paper we focus only on
the influence of energy consumption.
2.3.1 Average Usage Scenario
The European average usage scenario was selected. It is a combination of the
business activity and one TRU setting:
• Temperature of transportation (0 °C for fresh product or −20 °C for frozen
product). This parameter is directly linked to the business activity of the
customer.
• Operating mode (start/stop or continuous run). The parameter is chosen by the
end-user. It is selected depending on transported product (sensitive or not) but
there is no obligation from the manufacturer.
This results in an average scenario of use (Table 2) with an average energy
consumption associated of 1 l/h (this value is defined as a normalized value; it is
not the raw value). A weighting factor, obtained from the analysis of 150 TRU
usage data sets based on time, has been defined for each combination of parameters
and it is indicated here to better illustrate the average European sharing.
Is It Useful to Improve Modelling of Usage Scenarios …
235