Interacción auxina-citoquinina

Given the advantages of a hybrid system, the question arises of the best way to combine the multiple power sources in order to create an optimal design. Component sizing has been heavily researched, and a multitude of distinct designs have come forward. Available on the automotive market today are a variety of varying degrees of hybridisation, generally classified into three categories; “Stop-Start Technology”, “Mild Hybrids” and “Full Hybrids”.

Stop-start technology represents the mildest degree of hybridisation where a conventional vehicle is capable of rapidly stopping and starting the engine, quickly enough to reasonably allow the engine to be stopped when the vehicle is stopped, but still allow the engine to be restarted without driver intervention. Some stop-start vehicles, such as the 2013 BMW 1-Series are even capable of low levels of regenerative braking by using the existing alternator.

Mild hybrids are generally defined by the replacement of the starter-alternator system with an electric traction motor allowing higher levels of regenerative braking in addition to stop-start technology. Mild hybrids have less of a cost and weight penalty when compared to full hybrids, but still offer many of the advantages of a hybrid system. Finally, full hybrids are generally defined by the ability to operate in electric only mode for a limited range.

Full hybrids generally offer all of the advantages of a hybrid system, albeit at an increased weight and cost compared to a conventional vehicle.

Most FCHEVs (including the Microcab H4 and H2EV) fall into the full hybrid category because they are generally able to run on battery power alone (although some fuel cell vehicles do not incorporate a significant battery pack and so would fall into the mild hybrid category). The comparative size of each of the components needs to be optimised in order to maximise the benefits of the hybridisation. A lot of research has been performed in this area for both ICE based HEVs as well as FCHEVs. This work generally consist of a parameter sweep simulations of differently sized engines, fuel cells, batteries and capacitors over a single or multiple drive-cycles. Inputs to the parameter sweep are generally the capacity, cost and weight of the components and outputs are the performance, cost and weight of the system. These simulations are heavily affected by the control algorithms used to perform the EMS. Power management and component sizing are the biggest factors that affect the fuel efficiency of FCHEV and Kim and Peng [71] suggest that they should be considered concurrently.

Basic component sizing techniques involve manual calculation of the required compo-nent sizes based on simple rule-based controllers. For example, Wu and Gao [72] describe a method used to size the components of a fuel cell/supercapacitor HEV. Calculations are performed to determine the power required to meet performance targets such as top speed and grade-ability. It is assumed that the cost of the fuel cell and supercapacitor banks are a function of the number of units. The cost, weight and volume of the system are then optimised and the results are subsequently simulated.

A better method for sizing components involves a parameter sweep of component sizes in order to find the best overall system. Schaltz et al. [36] present the results of a param-eter sweep simulation investigating the influence of battery and ultracapacitor sizing on a FCHEV. The energy management for this investigation is rule-based and is used to en-sure that the fuel cell can be run continuously at a fixed power, whilst the supercapacitors and battery pack absorb any short and long-term transients respectively. Schaltz et al. [36]

concentrate on the trade-off between the size, mass and cost of the system and the battery lifetime and concludes that over-sizing the battery pack and ultracapacitors will decrease

the battery degradation significantly for an increase in system cost, but without affecting vehicle performance.

Bauman and Kazerani [73], perform a comparative study in order to assess the effects of using each technology on component lifetime and system cost. The MATLAB simulation uses a rule-based controller in order to manage the SoC of the capacitors and batteries and to limit transient loads on the fuel cell. They conclude that overall, the best designs minimise the cost by sizing the fuel cell to cope with power demand at the highest cruising speed of the vehicle. According to Bauman and Kazerani, the most desirable designs require a battery pack due to the low energy density of using ultracapacitors on their own. The usage of ultracapacitors is marginal, increasing the cost of the system, but improving the fuel economy and lifetime of the battery pack.

Rousseau et al. [5] use simulation to assess the required degree of hybridisation for a fuel cell vehicle. In the simulation, a heuristic energy management strategy is used, specifying a minimum power to turn the fuel cell on and battery SoC targets based on vehicle speed.

It is decided that the vehicle requires a peak power of 160kW, and various combinations of battery and fuel cell power are used to meet this goal. It is found that as the battery size increases, more regenerative braking energy can be recovered. Conversely, as the battery size increases, and the fuel cell size is decreased and more energy is re-cycled through the battery. This gives an optimum degree of hybridisation for fuel economy. It is also found by Rousseau et al. [5] that modifications to the parameters used in the rule-based controller can have significant effect on the results. It is therefore imperative that the control algorithm used accurately represents that of the final vehicle.

Under certain conditions, the control algorithm used for component sizing exercises may give an unfair representation of the results. For example, a heuristic algorithm which prioritises regenerative braking over fuel cell operating efficiency could be designed by ag-gressively targeting a relatively low SoC in the battery. This may result in a high battery capacity appearing relatively unattractive because the controller ensures that capacity is available at all times. Conversely, if the algorithm prioritises the fuel cell operating effi-ciency, perhaps by allowing the battery SoC to vary over a larger range, a higher battery capacity will appear more attractive. Therefore, using an identical control algorithm for each design doesn’t necessarily give an objective comparison. In order to isolate the ad-vantages of each design, the control strategy should be optimised individually.

In order to eliminate effects caused by variations in the controller, it is possible to use dynamic programming to calculate the optimal EMS for each design. Dynamic program-ming techniques allow optimal control, effectively allowing each design to perform to its maximum performance potential. This gives the system designer the best possible result for each configuration. Sinoquet et al. [74] present a parametric study focussed on variations in size of powertrain components for a hybrid vehicle with respect to fuel consumption.

Results are obtained using a Deterministic Dynamic Programming (DDP) controller which shows that a 1.04kWh battery pack gives the best fuel consumption. Lower battery capac-ity results in a loss of recovered braking energy, but higher capaccapac-ity increases the mass of the system, resulting in an overall increase in fuel consumption. Kim and Peng [71]

present a combined optimisation problem using Stochastic Dynamic Programming (SDP) to be used to choose both the component sizing and the power management strategy in order to maximise fuel efficiency.

2.1.6 Summary

In summary, the electrical system of a FCHEV is usually made up of a fuel cell, batteries and an electric motor. The fuel cell is used as the primary power source, with the battery pack included to absorb transient loads in order to protect the fuel cell; however, this will tend to age the battery pack. Supercapacitors are sometimes used either instead of the battery pack or in addition to it. As they a very resilient to transient loading and high currents, they are often used to protect the battery (or fuel cell directly) from transient loads and the high currents associated with regenerative braking at the expense of addition cost and complexity to the system. There have been a number of works examining the optimal sizing of components, mostly using “rule-based” controllers (see Section 2.3.1). These controllers may favour particular system designs due to heuristic assumptions and therefore this bias is eliminated by using optimal control methods. Techniques such as DDP and SDP will exploit the advantages of each individual design allowing for a fairer comparison.