Lower consumption and fewer emissions thanks to an integrated system of coaching

How future cross-platform driver assistance systems can reduce energy consumption, emissions, costs, and the length of transport operations

17. May 2016 | Consulting

Cutting back on greenhouse gases and energy consumption in an eco-friendly way remains one of the biggest challenges in the automobile and transportation industries. To lower real consumption specifically, FEV Consulting has developed an integrated optimization approach that involves the entire process chain for one trip from end to end. The driver and his driving behavior play a key role.

The conditions under which a vehicle‘s fuel, or energy, consumption is to be lowered are extremely individual. Previous approaches that managed to cut emissions focused primarily on improving individual components. However, FEV‘s system inspection optimizes the entire process chain and all its dependent factors. That includes preparation for the trip or transport, selecting the means of transportation, and optimizing the manner of driving and operational strategy while taking the outside world into account. The integrated approach to system optimization is divided into three areas of coaching: vehicle, driver, and transport.

Vehicle coaching: Making cars and buses smarter

Instead of further developing the hardware of individual components, optimized operational strategies significantly help reduce a vehicle‘s fuel consumption. Today‘s hybrid powertrains are capable of adapting to travel routes and can, for example, optimize battery charging tasks and other components. One trend in this area is flexible, model-based (rather than rule-based) operational strategy. The rules allow operations to be controlled with the optimum calibration for each situation, using the latest sensor and operational data.

However, there is still one big unknown in the models: the ability to predict exactly how drivers will behave. If drivers do not behave as predicted, that could cause the entire operational strategy to fail in a worst-case scenario, leading to increased fuel consumption. We provide vehicle coaching in an attempt to avoid precisely that. By enabling the vehicle to „learn“ its driver‘s behavior, it can adapt to the driver and situation and process driver requests optimally (for fuel consumption). For example, if a driver regularly takes curves too fast, the coach can briefly adjust the accelerator pedal position, provided outside circumstances allow it. Ideally, it can even initiate coasting times sooner or extend them in order to increase the (electric) range.

The increasing connectivity of vehicles will eventually enable the application of swarm intelligence, allowing vehicle coaches to coordinate with each other. The coaches compare driver intentions and operational strategies to initiate optimized group maneuvers. The maneuvers may need a local judge to make decisions and determine the best approach in certain situations — for example, when nearing a traffic light. As an alternative, there is also a way for vehicles to agree with one another based on a specific protocol, which would first need to be laid out and implemented.

Driver coaching: motivating operators to behave ideally

Sustained improvements in driver behavior are becoming more and more important and gaining potential in the age of electric vehicles and connected cars. The driver coach knows the profile of the road and the traffic situation ahead and can realize increased potential for savings by executing optimized coasting maneuvers at high speeds. It gives drivers precise instructions on how best to behave behind the wheel. The learning driver coach also knows which instructions work well on certain drivers and which ones are less effective. Advanced HMI and motivational techniques (such as active gas pedal, heads-up eco displays, and gamification) are needed to exploit the driver coach‘s full potential without distracting drivers too much. Thanks to iterative improvements, the coach adjusts its strategy for communicating with the driver and can also select personalized environmental campaigns to boost the driver‘s motivation in specific areas.
An instructional method, or „campaign,“ can apply cutting-edge, playful, motivational mechanisms („game mechanics“) from the game industry, thereby ensuring the greatest possible success. User-oriented design is essential for achieving maximum benefit from the assistance system. Today‘s assistance systems and eco displays are still in the beginning stages, as illustrated in our study titled „Gamified Eco-Coaches.“

It is precisely the interplay between functions that produces benefits. The vehicle coach can predict the effect of the driver coach and adjust how it regulates individual components. A very teachable driver, for instance, can be expected to adhere to the recommended speed for rounding a curve with high probability, enabling him to begin coasting sooner. If he doesn‘t, the driver coach can transmit feedback to the driver through the accelerator pedal. That encourages the driver to navigate the next curve at the ideal speed on his own.

The driver coach can also combine information from past drives and current sensor data to recommend an optimized speed profile for the driver, taking traffic and road conditions into account.

Mobility Coach

Mobility Coaching: Holistic system optimization to reduce real fuel consumption

 

 

 

 

 

 

Transport coaching: Multimodal transport management

The greater the complexity of a transport operation, the higher the potential for the transport coach. Its job is to avoid trips or select means of transport that consume less energy. The coach plans transport operations by collecting information for current and future decision-making, such as traffic volume, expenses, duration, and return routes. Its interaction with the other coaches enables advanced transport and fleet management. By analyzing transport tasks, income and expenses, transit networks, and provider information, the system can make decisions in real time at actual and standard costs. The first transport coaches have already been deployed in fleet management systems. In the area of passenger transport, vehicle manufacturers have introduced the first multimodal route planners such as the one found on the BMW i series. The first non-proprietary apps (such as the Fraunhofer Society‘s MyWay project) for mobile devices are also showing promising results.

Smart networking offers tremendous potential

To lower real consumption significantly, it will no longer be enough to concentrate on the further development of individual coaches. The strong interdependencies between planning a trip, the driver‘s behavior, and the vehicle‘s behavior require integrated system optimization from one end of the transport chain to the other. The weighting of the sometimes disjointed goals is both crucial and highly specific (costs, emissions, duration, etc.).
Current examples and projects show that the following key elements determine success or failure:

• Clearly-stated project goals, e.g., CO2 accreditation, USP for end clients, costs of consumption, etc.
• Cross-platform and multi-system solutions with maximum integration
• Integrated view of transport routes
• Advanced driver motivation techniques
• Clearly-worded instructions using suitable HMI concepts (see, hear, feel)
• Agile development and rapid prototyping

The circumstances in which each system improvement is to take place are very individual and affected by the underlying conditions. In fleet management, there have already been attempts to introduce telematics systems that may lead to further optimization. Yet, OEMs in the passenger car sector have only simple assistance systems for driver coaching and third-party apps with a lack of integration available to them.

So, FEV Consulting possesses many important skills for optimizing systems, both individually and across the board. We have in-depth technical expertise in powertrains, commercial vehicles, and passenger vehicles, plus project experience in the areas of digitization, eco-coaching, motivational techniques, app and platform development, and business model design.

Game mechanics

Comparison of „Game mechanics in use“

Intelligent transport

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