Predictive Control of Drive Systems
In order to counteract global warming, it is necessary to reduce the emissions of greenhouse gases and thus the consumption of fossil fuels as quickly as possible. The need for energy-saving solutions is further reinforced by rising energy costs.
In the mobility sector, these goals are being pursued through increasing electrification of the powertrain. This includes not only the switch to battery electric vehicles but also the use of fuel cell vehicles and the hybridization of vehicles with internal combustion engines. However, the mere presence of electric components in the powertrain is not enough - what matters is the optimal interaction of the components. For example, when driving a hybrid vehicle in mountainous terrain, the control concept should ensure that the battery's state of charge is low before a long downhill section. This ensures that the braking energy can then be used to charge the battery. This is where predictive systems come into play, based for example on information from planned routes, vehicle-to-vehicle communication (V2V) or vehicle-to-infrastructure communication (V2I), which form the basis for optimal control of the vehicle components.
Fuel Cell Vehicles
Fuel cell drives are a promising propulsion system alternative to the internal combustion engine. Passenger cars, for example, benefit from fast refueling and low weight. Due to long driving ranges and high energy densities, fuel cells are also particularly relevant for applications in heavy-duty vehicles. However, fuel cell drives are not limited to on-road applications. In large construction machinery with high energy demand and the requirement of short refueling times, even at remote locations with insufficient electric infrastructure, fuel cell drives are promising to achieve future emission targets on construction sites.
To reduce the dynamic stress on the fuel cell, a battery is usually added to the powertrain. Therefore, fuel cell vehicles are typically hybrid vehicles. This results in the requirement for an optimal energy management strategy aiming for an efficient load distribution between the fuel cell and the battery. Additionally, sufficient cooling of both power sources is challenging and calls for efficient thermal management. Regarding both topics, applying sophisticated predictive strategies considering future load demands allows for increased efficiency and prevents component degradation. Moreover, component lifetime can be further prolonged by health-conscious energy management adaptations.
Increasing the Efficiency of Conventional Powertrains
The greatest potential to meet short-term emission limits lies in the electrification of conventionally powered vehicles. This is not limited to expanding the powertrain with an electric motor. For example, e-turbos or electrically heatable catalytic converters allow efficiency increases or emission reductions, provided they are efficiently controlled. This requires again predictive information and sophisticated control concepts.
Publications
Kofler, Sandro, Zhang Peng Du, Stefan Jakubek, and Christoph Hametner. "Predictive energy management strategy for fuel cell vehicles combining long-term and short-term forecasts., opens an external URL in a new window" IEEE Transactions on Vehicular Technology (2024).
Kofler, Sandro, Stefan Jakubek, and Christoph Hametner. "Cost-to-go-based predictive equivalent consumption minimization strategy for fuel cell vehicles considering route information., opens an external URL in a new window" In 2024 IEEE Intelligent Vehicles Symposium (IV), pp. 2910-2916. IEEE, 2024.
Kofler, Sandro, Zhang Peng Du, Stefan Jakubek, and Christoph Hametner. "Adaptive Step Size Dynamic Programming for Optimal Energy Management of Fuel Cell Vehicles., opens an external URL in a new window" In 2023 IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1-6. IEEE, 2023.
Ferrara, Alessandro, Stefan Jakubek, and Christoph Hametner. "Cost-optimal design and energy management of fuel cell electric trucks., opens an external URL in a new window" International Journal of Hydrogen Energy 48, no. 43 (2023): 16420-16434.
Ferrara, Alessandro, Saeid Zendegan, Hans-Michael Koegeler, Sajin Gopi, Martin Huber, Johannes Pell, and Christoph Hametner. "Optimal calibration of an adaptive and predictive energy management strategy for fuel cell electric trucks., opens an external URL in a new window" Energies 15, no. 7 (2022): 2394.
Ferrara, Alessandro, and Christoph Hametner. "Eco-driving of fuel cell electric trucks: optimal speed planning combining dynamic programming and Pontryagin’s minimum principle., opens an external URL in a new window" In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), pp. 1-7. IEEE, 2022.
Ferrara, Alessandro, Stefan Jakubek, and Christoph Hametner. "Energy management of heavy-duty fuel cell vehicles in real-world driving scenarios: Robust design of strategies to maximize the hydrogen economy and system lifetime, opens an external URL in a new window." Energy Conversion and Management 232 (2021): 113795.
Ferrara, Alessandro, and Christoph Hametner. "Impact of Energy Management Strategies on Hydrogen Consumption and Start-up/Shut-down Cycles in Fuel Cell-Ultracapacitor-Battery Vehicles, opens an external URL in a new window." IEEE Transactions on Vehicular Technology (2021).
Zendegan, Saeid, Alessandro Ferrara, Stefan Jakubek, and Christoph Hametner. "Predictive Battery State of Charge Reference Generation Using Basic Route Information for Optimal Energy Management of Heavy-Duty Fuel Cell Vehicles, opens an external URL in a new window" IEEE Transactions on Vehicular Technology 70, no. 12 (2021): 12517-12528.
Vrlić, Martin, Daniel Ritzberger, and Stefan Jakubek. "Model-predictive-control-based reference governor for fuel cells in automotive application compared with performance from a real vehicle, opens an external URL in a new window." Energies 14, no. 8 (2021): 2206.
Vrlić, Martin, and Stefan Jakubek. "Degradation Avoiding Start Up and Shut Down of Fuel Cell Stacks for Automotive Application Using Two Plant Model Predictive Control, opens an external URL in a new window." In 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech), pp. 1-6. IEEE, 2021.
Vrlić, Martin, Daniel Ritzberger, and Stefan Jakubek. "Safe and Efficient Polymer Electrolyte Membrane Fuel Cell Control Using Successive Linearization Based Model Predictive Control Validated on Real Vehicle Data, opens an external URL in a new window." Energies 13, no. 20 (2020): 5353.
Vrlić, Martin, Daniel Ritzberger, and Stefan Jakubek. "Efficient and life preserving power tracking control of a proton exchange membrane fuel cell using model predictive control, opens an external URL in a new window." In 2020 SICE International Symposium on Control Systems (SICE ISCS), pp. 77-84. IEEE, 2020.
