Brake force
5.6 Outlook
The present research project has shown the development of methods for gathering the individual components of customer usage profiles. Next, the methods must be implemented into the vehicle development process, as shown in Section 5.2. There-fore, it is recommended to collect the specifications of all business units by starting with the act step: asking the right questions, which means defining the purpose. This step will help choosing the appropriate classification resolution and if necessary, the definition of dependencies and multi-dimensional classifications.
Once the individual datasets are collected, further research should be carried out to derive customer usage profiles on a large scale. Nowadays, each of the components of customer usage profiles is treated independent from each other. For example, the
Feature 1 Feature 2 Feature 3
Persona A Persona B
Figure 5.6 – Statistical derivation of personas.
99 %quantile (Q99) of the vehicle velocity is used for a validation procedure, as well as the Q99 for road roughness, and the Q99 for brake pressure, etc. It is unfeasible that one single customer represents the 99 % quantile of every component of cus-tomer usage profiles. In addition, the characteristics are often mutually exclusive. A possible approach would be the definition of stereotypes or so-calledpersonas, which represent a specific behaviour of a group of customers, see Figure5.6. For example, this could be the short-distance rider, the commuter, the tourer, the weekend rider, the aggressive rider, the race-track rider and many more. These personas would represent a well-defined combination of the components of customer usage profiles.
This is a statistical problem and could enable further potential of customer-specific products.
When the collection and derivation of customer usage profiles is established once, the entire vehicle development process will be much more customer-orientated. On the one hand, this reduces development costs, since measurements with expensive prototypes can be avoided. On the other hand, improved vehicle requirements will reduce warranty claims, and thus quality costs can also be reduced. The virtual product development will also benefit from the improved vehicle requirements. Fur-ther research should concentrate on the methodology of virtual load acquisition and the development of a modular system, consisting of three independent components:
the vehicle concept, the rider and the road characteristics, see Figure 5.7. In the early stages of product development, the vehicle concept exists in the form of a nu-merical full-vehicle model, which is state of the art in vehicle development. Next,
Vehicle Concept + Rider + Road Characteristics
Persona A Persona B
Figure 5.7– Three components of a virtual load acquisition.
the target group of customers can be defined using personas, which describe how the vehicle will be used. Finally, the road characteristics describe where the vehicle is used. Thus, the next steps should comprise the derivation of personas and the design of virtual test tracks with the help of the customer usage profiles.
As Wixom and Ross [27] discussed, these data-based insights will better address customer demands and optimise product development. Further activities could be utilising this data for offering customer-specific products. In the motorcycle busi-ness, this could be offering performance parts - for example, a sport exhaust - to the group of sporty riders. By contrast, safety parts such as an airbag jacket, could be advertised to cautious riders. In the near future, it will also be possible to offer digital services similar to the well-known CE industry. It is conceivable that a customer can buy extra performance from the in-vehicle app store. Further real-time applications for customer usage profiles are condition-based maintenance systems. Nowadays, a predefined workshop interval is given. With the knowledge of customer-specific usage, every vehicle could be treated individually. Especially the detection of severe special events could improve in-field quality costs for the manufacturer. Selling the inform-ation to other manufacturers or third-parties is another possibility of monetising this data. Officials could be interested in vehicle usage for improving infrastructure.
Insurances could offer individual contributions depending on the driving style.
Collecting crowd-sourced field-data is empowered by the development of autonom-ous driving vehicles, since the signal processing and data sharing to other vehicles and the backend is one major facet of this new technology. Data plays a fundamental role
in the digital transformation and will gain increasing importance, whereby vehicle data is simply one facet. Industry 4.0 requires production data for a connected production and a continuous improvement in the production system. Further, con-text data such as weather, traffic and mobility will be added to the vehicles as a digital platform, as well as personal data, such as activities and social media. New business models will emerge and the connected vehicle will become a high-tech and high-complex product competing with the well-known CE industry.
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Custom er usage pr ofiles Sub- m odels Sensor s
Acceleration X Acceleration Z Roll rate Yaw rate Inertial Measurement Unit (IMU) Acceleration X Vehicle velocityRoad slope Road slope estimator
Vehicle velocity Angular rate wheel front Angular rate wheel rear Break preassure front Brake pressure rear ABS sensors Suspension travel front Suspension travel rear Suspension sensors Engine torque Gear Engine sensor
Roll rate Yaw rate Vehicle velocity
Roll angle Roll angle Angular rate wheel rear Engine torque Gear
Traction force Driveline model
Vehicle velocity Road slope Traction Force
Mass Mass estimator
Acceleration X Vehicle velocity Acceleration Z Roll rate Roll angle Mass Road slope Traction force Angular rate wheel front Angular rate wheel rear Break preassure front Break preassure rear Suspension travel front Suspension travel rear
Fx Fy Fz Wheel force calculation
Vehicle velocity Roll angle
curviness c Road curviness C Curviness Vehicle velocity Road slope
hilliness h Road hilliness H Hilliness Vehicle velocity Suspension travel front Suspension travel rear
ISO Class (A-H) Road roughness Vehicle velocity Suspension travel front Angular rate wheel front
Impact class Impact detection Mass Vehiucle loading Vehicle loading
Inertial Measurement Unit
Acceleration COG X ax m s−2
Acceleration COG Z az m s−2
Roll rate COG ωx rad s−1
Yaw rate COG ωz rad s−1
ABS sensors
Velocity v m s−1
Angular rate wheel front ωft rad s−1 Angular acceleration wheel front αft rad s−2 Angular rate wheel rear ωrr rad s−1
Brake pressure front pft N m−2
Brake pressure rear prr N m−2
Engine sensor Engine torque Te N m
Gear i
-Suspension sensors
Suspension travel front sft m
Suspension travel rear srr m
Suspension velocity front s˙ft m s−1 Suspension velocity rear s˙rr m s−1
Sub-models
Roll angle φ ◦
Mass m kg
Road slope α ◦
Traction force FT N
Customer usage profiles
Wheel forces FX, FY, FZ N
Mean curve radius r¯c m
Mean curve angle ¯γ ◦
Kappa κ m−1
Curviness c rad
Road curviness C rad
Elevation gain h m
Road hilliness H m
Road roughness classification -
-Special event classification -
-𝑍
𝑌 𝑧 R 𝑋
𝑧 s
𝑧 un 𝑚 un
𝑚 s
𝑘 T
𝑘 s 𝑐 s
Figure B.1– Quarter of Vehicle (QoV).