Heuristic Vehicle Design

Applying a heuristic refers to a way of deducing solutions from acquired experience or knowledge.

Heuristic rules are commonly applied when systems cannot be easily modelled, or when models rely on computationally expensive functions. The behaviour of the system is then separated into rules which are used to characterise it (Gigerenzer, & Todd 1999). Heuristic methods are often used when attempting to model human decision making; a pioneering example is the use of heuristics in competitive computer chess programs to optimize points per move within time constraints (Mueller 2006). For this work, a heuristic methodology was developed to model the vehicle design process in order to:

1. Eliminate infeasible option combinations, and 2. Appropriately size and combine components.

For example, diesel engines are typically smaller displacement than otto engines, can only use diesel fuel, and are less likely to be mild hybrids. The heuristic design algorithm (HDA) uses

43 design choices made in the past century of evolutionary vehicle design (J.M.Weaver et al. 2005).

The HDA then uses the available technology options to compose a full set of self-consistent vehicle designs. This set is representative of the current vehicle pool, but is not sales weighted (i.e. no type of vehicle is represented proportionally to how popular it is). This bottom-up, technology-centred approach differs fundamentally from how manufacturers have historically designed and are currently designing vehicles, where a top-down approach is used with market demand driving technology choices.

The heuristic design tools applied in this work also differ from the conventional approach taken to vehicle technology assessment in several other important ways. Researchers typically limit themselves to varying technology on one basic platform to simplify data gathering and model development. This generally results in extrapolating results, as shown in the first pane of Figure 14, which may or may not accurately represent technology performance. The inherent advantage to the heuristic method, shown in the second pane of Figure 14, is that the large number of vehicle sizes, types, and configurations allows interpolation of performance within a broad design set, and hence a greater understanding of the higher order interaction between technology options and a broader scope of analysis. In the figure, red lines reflect how technology performance is evaluated in both cases and it is clear that by extrapolating the underlying trends may not be observed.

Heuristic design allows many technologies to be simultaneously compared for various trade-offs and allows technology family performance envelopes to be identified. Using the heuristic design approach care must be taken when attempting to isolate the individual influence of different technologies on stakeholder criteria. For example, it is often the case in the analysis presented in this chapter that no performance specifications are held constant, i.e. acceleration, consumption, and cost, etc. are all changed based on the technologies chosen by the design heuristics which complicates interpreting the results.

44 explains how the algorithm combines each exogenous technology with every other. Introducing a rule between two exogenous options results in the removal of the number of designs given by Equation 10, where the variable di represents the option categories not affected by the introduction of the rule. The variable hdep accounts for designs added during the recursive phase of the algorithm where dependent options are added based on the exogenous heuristics.

Fuel Consumption (gasoline equivalent L/100km)

Curb Weight (kg)

Fuel Consumption (gasoline equivalent L/100km)

Curb Weight (kg)

45 ntot = Total number of designs

s_i = number of designs in a category

hendog = number of endogenous designs removed

d_i = number of sets not affected by the heuristic hend-rule = number of sets affected by the heuristic hdep = number of dependent designs removed n_set = number of designs remaining in set

A selection of heuristically designed sets is shown in Figure 15 to demonstrate how quickly the number of vehicle designs can become unmanageably large without the application of heuristics to remove nonsensical technology combinations. Figure 15 includes the number of designs (nset) from four distinct heuristic sets to illustrate how the application of heuristic rules affects the volume of designs. The final number of designs described by Equation 11 is typically 0.2–4 % of the maximum number of designs.

46 Figure 15: The factorial relationship between the number of options in a design set and the final number of designs (Heuristic Rules/Technology Options)

An example of a selection process made by the HDA is shown in Figure 16, describing the selection of hybridization and electric path power. Dashed lines represent the exogenous choice of hybrid architecture influencing various endogenous choices, which then influence specific criteria as represented by solid lines. This shows the tiered approach to dividing the options, as well as the vehicle weight, which is a result of technology combinations. Not all of the heuristics are represented explicitly in this figure; instead they are represented by hash marks to reduce clutter.

The mass characteristic is represented in its own column, and represents an important design consideration, as was discussed with respect to decompounding in Chapter 2.

1 10 100 1000 10000 100000 1000000 10000000 100000000

0 20 40 60

Number of designs in set (nset)

Number of options Set A (38 / 28)

Set B (48 / 35)

Set C (63 / 57) Set D (54 / 62)

47 Figure 16: A subset of heuristic rules acting on exogenous and endogenous choices

The heuristic design generator was implemented in MATLAB, and sample code can be found in Appendix D. It is based on a bottom-up method of combining valid combinations that adds technology options to designs which can accept them instead of parsing invalid combinations from a larger set. This implementation was selected due to its efficiency in creating design sets in shorter time than required by the top-down approach.