Segundo estudio: factores que intervienen en la redacción académica

This chapter presents the simulation results of various Energy Management Strategy (EMS) controllers developed using the techniques previously described. The chapter begins with a brief description of how the controllers are tested. Following this, the results of the initial Stochastic Dynamic Programming (SDP) optimised controller are presented and compared firstly to the baseline controller (based on the Microcab H4’s current strategy) and also to a controller optimised purely on the fuel consumption.

It is found that for the current design, the 1.2kW fuel cell is insufficient to maintain the battery State of Charge (SoC) under normal usage even when the optimal control strategy is applied. Therefore, the results are recalculated for a 4.8kw stack representative of that in the newer Microcab H2EV. It is found that the current control strategy is no longer appropriate for the system design and that the degradation inclusive controller reduces the estimated degradation by approximately 15% for only a 4% increase in fuel consumption when compared to the strategy optimised purely on the fuel consumption. This gives an overall running cost saving of around 9%.

6.1 Testing Procedure

As mentioned in the previous chapter, the output of the SDP algorithm can be represented by a four-dimensional look up table which can easily be implemented in the full vehicle model developed in Chapter 3. This model can then be used in order to simulate various duty cycles in order to assess the performance of the controller. Initial results are obtained using the grid spacing given in Table 5.1 and the SDP parameters given at the end of pre-vious chapter.

The Microcab H4 is designed for very low speed campus driving and cannot complete any standard drive-cycle. Therefore, logged data of the journeys captured at Loughborough and Birmingham have been used for testing. Individual journeys identified in Chapter 4 have been input as a speed reference into the model. These journeys are then simulated and the EMS is used to control the output power of the DC/DC converter based on the vehicle speed, acceleration, battery SoC and the previous control action. The operating efficiency of various components can then be calculated from the results, along with an estimation of the fuel consumption and fuel cell degradation.

6.1.1 Weighting Factors - Hydrogen and Fuel Cell Stack Cost

In this analysis, the weighting in the cost function associated with the fuel consumption and the degradation have not been chosen arbitrarily, but instead have been chosen based on the monetary value of each. However, the definition of these financial costs is not a trivial task. One potential option is to simply use the purchase cost of each, experienced during the design of the vehicle in question. For an automotive manufacturer, this would be the obvious decision and could be based on highly reliable data, making the cost function extremely relevant to the exact vehicle and therefore the optimisation would minimise the real-world financial running cost.

However, the Microcab H4 is a concept vehicle and thus the purchase cost of the fuel cell is highly inflated compared to that of a production vehicle. Similarly, the hydrogen fuel supply available at Loughborough University is used primarily for research and devel-opment purposes and as a result, the associated cost of the supply is not representative of the anticipated “forecourt” cost for such vehicles. The bespoke nature of such vehicles and the manner in which they are used means that there is considerable variability in such cost estimates which depends on the supplier, production quantities and exact system design.

Although using such an estimate would be highly justifiable, the results would not translate very well to other vehicle designs, making comparison with similar work difficult.

Instead, it has been decided to consider a scenario in which the fuel cell stack is mass produced, using present day (2015) estimates of mass production costs from the literature.

This provides two main benefits compared to using the actual costs experienced with the vehicles. Firstly, as mentioned already, the estimates in the literature for mass produced vehicles are much more consistent allowing for easier comparison to similar work. Sec-ondly, these costs estimates are based on the scaled up manufacturing cost of the fuel cells themselves and thus do not include additional components such as the electronic control unit. These figures are also not subject to supplier profit margins, exchange rate variability and technical assistance which are all but impossible to separate from the purchase prices of “off-the-shelf” stacks such as the Ballard Nexa or Horizon H5000.

The US Department of Energy (DoE) targets a fuel cell cost of $35/kW [12] for fuel cell vehicles to become viable, although recent estimates tend to be marginally higher, in the

range of $50+/kW (2015) [13], $49-53/kW (2015) [107], and $61/kW (2009) [108]. Similarly, dispensed hydrogen costs are expected to be within the range of $2.10/kg to $8.26/kg (2015) [13, 107], depending on the method of production and transportation costs.

Unless otherwise stated, the data shown in this chapter assume a fuel cost of $3/kg of hydrogen and a fuel cell cost of $50/kW. This gives the 1.2kW fuel cell used in the Microcab a replacement cost of $60, and the 4.8kW fuel cell used in later analysis a cost of $240.

These figures are clearly much lower than the actual purchase costs associated with the vehicle; however, they have been used for the reasons listed above. These figures are based on recent estimates from the literature for current technology, assuming mass production economy-of-scale. In reality, the prices are likely to be significantly higher for current fuel cell vehicles due to lower production volumes, but are likely to be lower for vehicles released in the future due to more advanced technology. As these prices are used only to weight the degradation and fuel consumption appropriately, the actual magnitudes of the prices are of little importance compared to the ratio between the two.

6.1.2 Controller Design

The primary controller examined has been optimised to minimise the overall running cost inclusive of both fuel consumption and the proportional cost of fuel cell replacement due to voltage degradation. This controller is referred to as the Degradation Inclusive (DI) con-troller or strategy. This strategy is novel with respect to the inclusion of quantifiable degra-dation metrics into the cost function. Two other controllers have been developed for com-parison purposes; the first is based on the current EMS strategy of the Microcab, and the second is a SDP strategy representative of recent work in the literature.

6.1.2.1 Baseline Controller - Current Microcab Strategy

The baseline controller is the Microcab H4’s current control strategy. This strategy attempts to control the DC/DC converter’s output power in order to maintain a battery pack voltage of 57.6V. This represents a voltage of approximately 14.4V for each battery, and serves to ensure every battery is fully charged by the fuel cell. The fuel cell will therefore react to both a low battery SoC and the drop in voltage associated with ohmic losses due to high current demand. As the motors may draw more than 10kW of electrical power, and the maximum power output of the DC/DC converter is only 1.2kW, the batteries tend to be depleted while the vehicle is moving, and therefore the voltage will drop below 57.6V. In this case, the DC/DC converter will run at maximum power until the voltage returns to the set point.

6.1.2.2 Minimal Fuel Consumption (MFC) Controller

The second controller developed for comparison is a SDP controller optimised solely to minimise the fuel consumption. This represents recent work in the literature, and serves as a good baseline for comparison to other work in the area. The fuel consumption only SDP controller has been developed using exactly the same methods as the degradation inclusive controller, aside from the removal of second term in the cost function (Equation 5.2.7); that related to the cost of the fuel cell degradation. This comparison allows the effect of the inclusion of degradation metrics into the optimisation to be separated from the normal fuel consumption optimisation.