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1. Stochastic Drift Counteraction Optimal Control of a Fuel Cell-Powered Small Unmanned Aerial Vehicle

   https://doi.org/10.3390/en14051304  

   Zhang, Jiadi; Kolmanovsky, Ilya; Amini, Mohammad Reza (March 2021, Energies)

   null (Ed.)

   This paper investigates optimal power management of a fuel cell hybrid small unmanned aerial vehicle (sUAV) from the perspective of endurance (time of flight) maximization in a stochastic environment. Stochastic drift counteraction optimal control is exploited to obtain an optimal policy for power management that coordinates the operation of the fuel cell and battery to maximize the expected flight time while accounting for the limits on the rate of change of fuel cell power output and the orientation dependence of fuel cell efficiency. The proposed power management strategy accounts for known statistics in transitions of propeller power and climb angle during the mission, but does not require the exact preview of their time histories. The optimal control policy is generated offline using value iterations implemented in Cython, demonstrating an order of magnitude speedup as compared to MATLAB. It is also shown that the value iterations can be further sped up using a discount factor, but at the cost of decreased performance. Simulation results for a 1.5 kg sUAV are reported that illustrate the optimal coordination between the fuel cell and the battery during aircraft maneuvers, including a turnpike in the battery state of charge (SOC) trajectory. As the fuel cell is not able to support fast changes in power output, the optimal policy is shown to charge the battery to the turnpike value if starting from a low initial SOC value. If starting from a high SOC value, the battery energy is used till a turnpike value of the SOC is reached with further discharge delayed to later in the flight. For the specific scenarios and simulated sUAV parameters considered, the results indicate the capability of up to 2.7 h of flight time. 

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2. A distributed rule‐based power management strategy in a photovoltaic/hybrid energy storage based on an active compensation filtering technique

   https://doi.org/10.1049/rpg2.12263  

   Ghorashi Khalil Abadi, Seyyed Ali; Bidram, Ali (July 2021, IET Renewable Power Generation)

   null (Ed.)

   This paper proposes a distributed rule-based power management strategy for dynamic power balancing and power smoothing in a photovoltaic (PV)/battery-supercapacitor hybrid energy storage system. The system contains a PV system, a battery-supercapacitor hybrid energy storage system (HESS), and a group of loads. Firstly, an active compensation technique is proposed which improves the efficiency of the power smoothing filter. Then, a distributed supervisory control technique is employed that prevents the BESS and SC from SOC violation while maintaining the balance between generation and load. To this end, the system components are divided into three different reactive agents including an HESS agent, a PV agent, and a load agent. These agents react to the system changes by switching their operational mode upon satisfying a predefined rule. To analyse the hybrid dynamical behaviour of the agents and design the supervisory controllers, the agents are modelled in hybrid automata frameworks. It is shown that the proposed distributed approach reduces the complexity of the supervisory control system and increases its scalability compared to its equivalent centralized method. Finally, the performance of the proposed approach is validated using a test system simulated in MATLAB/Simulink. 

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3. Hierarchical Multi-Timescale Energy Management for Hybrid-Electric Aircraft

   https://doi.org/10.1115/DSCC2020-3190  

   Wang, Wenqing; Koeln, Justin P. (October 2020, Dynamic Systems and Control Conference)

   null (Ed.)

   Hybrid-electric aircraft represent an important step in the transition from conventional fuel-based propulsion to fully-electric aircraft. For hybrid power systems, overall aircraft performance and efficiency highly depend on the coordination of the fuel and electrical systems and the ability to effectively control state and input trajectories at the limits of safe operation. In such a safety-critical application, the chosen control strategy must ensure the closed-loop system adheres to these operational limits. While hierarchical Model Predictive Control (MPC) has proven to be a computationally efficient approach to coordinated control of complex systems across multiple timescales, most formulations are not supported by theoretical guarantees of actuator and state constraint satisfaction. To provide guaranteed constraint satisfaction, this paper presents set-based hierarchical MPC of a 16 state hybrid-electric aircraft power system. Within the proposed two-level vertical hierarchy, the long-term control decisions of the upper-level controller and the short-term control decisions of the lower-level controller are coordinated through the use of waysets. Simulation results show the benefits of this coordination in the context of hybrid-electric aircraft performance and demonstrate the practicality of applying set-based hierarchical MPC to complex multi-timescale systems. 

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4. (2021). Making flying microgrids work in future aircrafts and aerospace vehicles. 52, 428-445.

   Ilić, M. D. (July 2021, Annual reviews in control)

   This paper concerns modeling, simulations and control design of turbo-electric distributed propulsion (TeDP) systems needed to power future hybrid aircraft systems. The approach taken is the one of control co-design by which the sizing and hardware selection of components and the TeDP architecture design are pursued so that potential e ects of control and automation are accounted for from the very beginning. Unique to this approach is a multi-layered modular modeling and control approach in which technology-specific modules comprising the complex dynamical system are characterized using unified interaction variables at their interfaces with the rest of the system. The dynamical performance of the interconnected system is assessed using these technology-agnostic interface variable specifications and, as such, can be applied to any candidate architecture of interest. Importantly, even the inputs to the TeDP system coming from pilot commands are modeled using such interface variables. This new multi-layered modeling captures the dynamics of energy and power as interactions. It also has a rather straightforward physical interpretation. The paper builds on our earlier results introduced for terrestrial power systems, including small micro-grids. We show how system feasibility and stability can be checked in real-time operations by modules exchanging the information about their interaction variables and adjusting in a near-autonomous manner so that, as system conditions vary, the interconnected system still functions. No such systematic control co-design exists to the best of our knowledge, but it is needed as both new technologies and more complex, often conflicting performance objectives emerge. We illustrate the approach on a representative TeDP architecture and compare it to today’s state-of-the-art. We close with a discussion on the generalization of the method for any given candidate architecture. Having such an approach dramatically reduces the R&D&D of novel candidate architectures. 

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5. A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles

   https://doi.org/10.1109/ASPDAC.2018.8297305  

   Zhao, Pu; Wang, Yanzhi; Chang, Naehyuck; Zhu, Qi; Lin, Xue (January 2018, 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC))

   Hybrid electric vehicles employ a hybrid propulsion system to combine the energy efficiency of electric motor and a long driving range of internal combustion engine, thereby achieving a higher fuel economy as well as convenience compared with conventional ICE vehicles. However, the relatively complicated powertrain structures of HEVs necessitate an effective power management policy to determine the power split between ICE and EM. In this work, we propose a deep reinforcement learning framework of the HEV power management with the aim of improving fuel economy. The DRL technique is comprised of an offline deep neural network construction phase and an online deep Q-learning phase. Unlike traditional reinforcement learning, DRL presents the capability of handling the high dimensional state and action space in the actual decision-making process, making it suitable for the HEV power management problem. Enabled by the DRL technique, the derived HEV power management policy is close to optimal, fully model-free, and independent of a prior knowledge of driving cycles. Simulation results based on actual vehicle setup over real-world and testing driving cycles demonstrate the effectiveness of the proposed framework on optimizing HEV fuel economy. 

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