ECCV Control Benchmark for sustainable Transport
A heavy-duty truck with a fuel-cell and battery powered electric powertrain is to be controlled optimally over a given driving mission.
Benchmark Organizers and Contact
Lars Eriksson, Linköping University, Sweden
Robin Holmbom, Linköping University, Sweden
Max Johansson, Linköping University, Sweden
Function e-mail address: firstname.lastname@example.org.
This benchmark covers a very challenging control problem for electromobility that will be of benefit for reaching a sustainable transportation system. In the benchmark problem, an electric truck is to be controlled where hydrogen is the energy carrier and a fuel cell act as an energy converter while a battery is used to support the operation. The benchmark focuses on transportation missions with trucks, where the transportation mission must be fulfilled within a given time, but the technology is generic and applicable to all types of vehicles. To fulfill the mission as energy efficient as possible one faces many challenges to propel the vehicle forward. Subsystems have constraints that need to be fulfilled for the operation and durability of the system. Examples of this are computational power as well as the temperature of the fuel cell in combination with its voltage.
The given electro chemical commercial vehicle (ECCV) model consists of
Where the control challenges are
- Vehicle and Powertrain Control
- Reach the destination in time
- Fulfill speed limits
- Fuel Cell Control
- Transient restrictions due to aging mechanisms.
- Fuel Cell Current
- Cathode Control
- Anode Control
- Battery Control
- Avoid excessive currents
- Keep the battery in a desirable State of Charge (SoC) window.
- DC Bus Control
- Control the DC Voltage that reacts to imbalances in current.
- Coolant System Control
- Keep components cooled within specified limits.
- Two interconnected circuits where the fuel cell has its own cooling circuit.
How to participate?
If you and your team want to participate, you simply send an email to the address indicated above and the organizers will send the Simulink model, as well as an accompanying technical note detailing the problem to solve and a model user guide. At the end of April, the competing teams have to submit a working controller with support code that can be run and evaluated by the Organizers. Finally, to be eligible to win the competition someone that represents the team must be present at the IFAC World Congress to present and discuss the solution with other participants in a special session. After the IFAC World Congress the participating teams are invited to co-author a joint Control Engineering Practice Paper, describing their solutions and comparing the results.
Matlab and Simulink 2019b or later. No additional toolboxes are needed.
Available information and control signals
- Torque Demand
- Brake Signal
- Cathode Throttle Opening
- Cathode Humidity Reference
- Compressor Speed
- Hydrogen Mass Flow
- Fuel Cell Current
- Battery Current
- Burn-off Resistor Current
- Coolant Pump Mass Flow
- Coolant Split Valve
- Coolant Fan Speed
Available sensor signals:
- Vehicle Velocity
- Electric Machine Current
- Battery Voltage
- DC Bus Voltage
- Fuel Cell Voltage
- Cathode Pressure
- Cathode Air Mass Flow
- Cathode Humidity
- Anode Pressure
- Anode Flow
- Fuel Cell Temperature
- Coolant Return Flow Temperature
Current mission-related information available
- Road Slope
- Distance traveled
- Speed Limit
- Speed Limit Ahead of 300 m
- Time left until the latest time of arrival
The mission setup will be that before the mission starts the system receives information on the distance to drive, the latest time of arrival, and the road slope over the mission. This information can be used to plan the trip and to tune the controller for the mission. However, to come close to the real-world problem, the controllers will also be evaluated in off-nominal operation. Such cases could be:
- Other routes than those included in the distribution of the benchmark to avoid over-fitting and cycle-beating.
- Variations in vehicle mass ranging from empty truck to maximally loaded.
- Planning failure, neither route nor slope information is given to the planner. Only a desired average velocity for the scenario is available and would be as if the driver didn’t give any route information but only set the cruise control.
- Unknown event on the road ahead, such as an accident. Where the maximum speed is reduced from the nominal and the timetable is adjusted. To give the controller time to react to this new information there is a look-ahead of 300 m for the speed limit.
All solutions will be run on the same computer by the evaluation team. In the supplied benchmark files a technical note is present that describes how the definitions of the performance criteria below will be evaluated to identify the winner.
- Not allowed to over speed
- Fulfill the delivery deadline
- Total hydrogen consumption
- Computational Time
- Stress on battery
- Stress on fuel cell