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Optimisation of range extender, battery size and control for different vehicle niches

Overview

In WP6 (Optimisation of range extender, battery size and control for different vehicle niches), 3 deliverables were completed in the OPTIMORE project:

D6.1 - Driving cycles for robust optimization of RE vehicles
D6.2 - Driving cycles for robust optimization of range extender
D6.3 - Analysis of robust sizing and optimal control of RE for different REX vehicles
 

The deliverable D6.3 focuses on robustness of the sizing of the Range Extender (RE), ie, the robustness of the combustion engine, and the battery size in range extender vehicles with respect to the assumption on which the sizing is based. The battery size is the most central design parameter since it is expensive. Also, an unnecessary large RE is important, since that increase weight of the vehicle, but also the price.
The analyses are made from a customer perspective choosing which vehicle to purchase where some information is available and certain, other important information need to be predicted. Specifically we assume the following setting:
 

  • Battery price is known at the moment of purchase, ie, the customer buy one battery that last the whole life of the vehicle.
  • Energy price is uncertain. The customer can assume a certain price but it might turn out differently which then influence the TCO.
  • The use of the vehicle might be different than assumed. It might be driven longer or shorter trips then anticipated, and the number of trips might also be different.
  • The possibilities to charge the vehicle might be different from the assumed one. Will it be possible only to charge over night at home, or also elsewhere at longer/shorter stops?


The significant piece of information for individual drivers is the distribution of driving lengths between charging is possible. It is this distribution which decide the optimal battery size as was explained in wp D6.2. But this distribution influence also the robustness towards changes in any of the assumptions in the sizing. Figure 1 illustrates the integral of the trip length for one individual driver and their optimal battery size is indicated. In the figure, the part of the driving which can be done on electricity, and the part that has to be done using fuel are indicated. From these areas, the sensitivity towards fuel price and electricity price is strait-forward to calculate.

Figure 1. Integral of trip length for one or several drivers. Distance driven on electricity (green area) and on fuel (grey area) for a given battery size (AER1).

The sizes of the areas can be used to calculate the fuel consumption/ CO2 emission. Moreover, in case of a fuel price (electricity price) increase; the cost increase will be directly proportional to the size of the grey area (green area. In case of increased charging opportunities, the distance driven on electricity will increase and the distance on fuel will decrease. The savings will be proportional to the additional green area. 
The best battery size can be very sensitive, or in-sensitive towards changes in the fuel price, as illustrated in Figure 2. The sensitivity depends on the slope of the trip length distribution.



Figure 2. Optimal battery size for three possible TE lines: Distance distribution for an optimal battery size sensitive customer (blue), and an insensitive customer (red).


If more charging possibilities are introduced, that means that the distribution is changed, as illustrated in Figure 3.


 

Figure 3. Example of integral of the frequency of trip lengths for 4 charging scenarios assuming that charging is possible if there is a stop longer than the indicated number of hours. As can be seen, with more charging possibilities, ie, short stop time required for charging, the frequency of long trips decrease and the frequency of short trips increase. Depending on the TE line, the optimal battery size increase or decrease due to the increase of charging possibilities.

If some of the assumptions used to select the sizing of the RE are changed, this basically means that the driver does not have their optimal battery size any more. The cost of this is illustrated in Figure 4 where the pink area illustrates the cost due to non-optimal battery size. Clearly, the size of this area depends on the trip length distribution, and can hence be very different for different drivers.

Figure 4. Additional cost due to a non-optimal battery size. The blue area is proportional to the additional cost.

Figure 5. Cost increase for all the drivers due to a 20% fuel cost increase. Blue + Red bars represent the extra cost in percentage of the TCO. The red bar represents the potential savings if a new optimal battery size is chosen.

The sizes of the areas can be used to calculate the fuel consumption/ CO2 emission. Moreover, in case of a fuel price (electricity price) increase, the cost increase will be directly proportional to the size of the grey area (green area). This will be relevant for the results in where sensitivity towards prices changes is discussed. In case of increased charging opportunities, the distance driven on electricity will increase and the distance on fuel will decrease. The savings will be proportional to the additional green area. 



Figure 6. Distance driven on electricity (green area) and on fuel (grey area) for a given battery size (AER1).

The sizes of the areas can be used to calculate the fuel consumption/ CO2 emission. Moreover, in case of a fuel price (electricity price) increase, the cost increase will be directly proportional to the size of the grey area (green area). This will be relevant for the results in where sensitivity towards prices changes is discussed. In case of increased charging opportunities, the distance driven on electricity will increase and the distance on fuel will decrease. The savings will be proportional to the additional green area. 
 

 

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This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 314252

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