A study of eco-driving behavior in hybrid electric vehicles (HEVs) by a team from Technische Universität Chemnitz (Germany) and the University of Southampton (UK) has found that simply providing drivers with a technology that has the potential for high energy savings—i.e., a hybrid—is not sufficient to result in high energy savings; systems must be developed in a way that facilitates energy efficient behaviors.
Put another way, they found that eco-driving motivation does not guarantee ecodriving success. Their study showed that even among drivers who were highly motivated for eco-driving, the individual differences in applied ecodriving strategies were still substantial. Building on the results, the team presented a number of suggestions for the design of systems that facilitate ecodriving. A paper on their study is published in the journal Applied Ergonomics.
|The conceptual framework of adaptive control of ecodriving strategy selection in HEV driving. The depiction shows processes at the trip-level. Long-term relationships between variables are omitted for clarity. Franke et al. Click to enlarge.|
HEVs are key for sustainable road transport as they can reduce fuel consumption without necessitating complex changes in energy-supply infrastructure (in contrast to plug-in or fuel cell electric vehicles). Yet, ultimately, sustainability strongly depends on the actual energy efficiency that users achieve in everyday usage. User behaviour is, therefore, a critical factor with regard to the ultimate effect that such systems have on making the road transport system more sustainable.
Ecodriving has emerged as a term that encompasses all the influences users have on the real-world energy efficiency of a road vehicle. As well as strategic and tactical ecodriving measures (e.g., optimize tyre pressure, route choice), specific driving behaviours (i.e., operational ecodriving strategies) are a core element of ecodriving. Electric drivetrains have been discussed as particularly challenging due to the novelty of their energy dynamics (e.g., consumption dynamics of electric propulsion, bidirectional energy flow resulting from regenerative braking). HEVs represent the most complex drivetrain in this respect because of the extremely dynamic interplay between the different drivetrain components, and the central role of bidirectional energy flow. Hence, maximising HEV fuel-efficiency can be considered particularly challenging, not only requiring ecodriving motivation, but also a sufficient level of technical system knowledge. From the perspective of green ergonomics a key challenge, therefore, is to advance understanding of user-energy interaction and support drivers’ ecodriving efforts.—Franke et al.
For the study, the team used 39 HEV drivers—Toyota Prius Gen 2 and Gen 3 and Prius c—with above-average fuel efficiencies and collected interview data, questionnaire responses, and long-term fuel efficiency recordings.
The team probed for motivation and knowledge; eco-driving strategy selection; conceptualizations (i.e., how do HEV drivers conceptualise HEV energy efficiency); and false beliefs.
They found large individual differences in strategy selection. They also found that the drivers expressed a large number of different conceptualizations of HEV energy efficiency, as well as various false beliefs that could affect fuel economy.
They also found certain decision biases in user-energy interaction. As an example, some of the results suggested that there might be something like an “energy conversion fallacy”—many drivers were not aware of the considerable losses incurred when converting energy, and often reported over-utilization of electric propulsion and regenerative braking, overvaluing their energy efficiency.
The drivers also provided suggestions for advanced ecodriving support systems in HEVs, which the research team consolidated and presented as general design guidelines, including:
Comprehensive feedback. Four key types of user-interface information appear crucial: (1) predictive information (for supporting adaptation to upcoming road events); (2) concurrent tracking feedback (to support the targeting of optimally efficient drivetrain states); (3) real-time performance feedback (i.e., indicators of momentary energy efficiency); and (4) aggregated performance feedback (i.e., aggregated indicators of energy efficiency).
Ease of perception. System design should maximize ease of perception and minimize distraction. For instance, drivers suggested haptic presentation for concurrent tracking feedback, enabling peripheral monitoring of momentary performance feedback, and generally shifting ecodriving information to the central gaze direction.
Strategy acquisition support. Ecodriving support systems should support efficient strategy acquisition. The data suggested two opportunities in particular: (a) a tutor system that provides explicit advice on correct strategies, and the reasons for strategy effectiveness; or (b) a system that communicates this advice implicitly via the interface information described above, thereby supporting trial-and-error learning.
Automated functions. Ecodriving is always a balance of different driver motives and current situation characteristics, and some drivers expressed their dissatisfaction with the design of current simple automated system interventions. Shared control may be a fruitful approach—e.g., an adaptive eco cruise control (eco-ACC) for efficient longitudinal control that allows drivers to influence key parameters (e.g., the intensity of energy efficiency optimization, system flexibility to change speed) so that automated vehicle control matches drivers’ momentary preferences.
System transparency display. A display that shows drivetrain dynamics (e.g., energy flows) can be beneficial for ecodriving. Some drivers argued that a precise understanding of the drivetrain functionality would be helpful in preventing false strategies, and several drivers requested more information on system states.
Configurability. Drivers of different levels of system understanding (or technical knowledge or ecodriving skill) will have different information needs. Further, differences in strategy preferences can also lead to different information needs. For an optimal ecodriving support system, some level of adaptability (e.g., the option to switch between basic vs. advanced eco-driving support) is necessary.
It could be argued that facilitating efficient user-energy interaction (i.e., sustainability-by-design, increasing usability of the user-energy interaction) is a particularly powerful approach for the support of sustainable behaviours, as it would enable all those with basic motivation to utilize energy resource efficiently (i.e., this might partly bypass the need to establish strong positive attitudes towards environmentally friendly behaviours). This underlines the potential of green ergonomics. The ultimate goal of green ergonomics is to develop a general theory of user behaviour in low-resource systems (i.e., resource efficient systems); the present study is one step in this long-term agenda.—Franke et al.
Thomas Franke, Matthias Georg Arend, Rich C. McIlroy, Neville A. Stanton (2016) “Ecodriving in hybrid electric vehicles – Exploring challenges for user-energy interaction,” Applied Ergonomics, Volume 55, Pages 33-45 doi: 10.1016/j.apergo.2016.01.007