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This paper presents a novel active power control scheme for SRMs that are used as S/G units at MEAs; the proposed method uses a vector-controlled SAF, facilitating the acceleration of the SRM during starter mode and the efficient power control of the SRG at various operational points during generator mode. Moreover, an energy storage unit is incorporated in the SAF, aiming at enhancing the SRM's starting torque production and taking full advantage of the SRG's energy generation capability, through the appropriate phases’ magnetization regulation that is imposed by the developed adaptive SAF vector control. In addition, the proposed scheme provides enhanced FRTC during both modes of operation. The mathematical analysis is verified through MATLAB/Simulink simulations, while the performance of the S/G concept under study is evaluated via real-time CHIL tests, with the use of the dSPACE 1202 (MicroLabBox) platform and the 16-bit dsPIC30F4011 microcontroller. [ABSTRACT FROM AUTHOR]

Ferry contributing a significant amount of greenhouse gas is one of the critical vessels to be electrified. Designing a power system for a ferry with hybrid-shaft generators is different from designing a power system for other vessels because of its fixed route. More clearly, ferries repeatedly travel between their port of origin and port of destination, and before the next voyage, the battery must be recharged to the initial state so that the optimal energy management scheme can be repeatedly applied. Furthermore, the flexibility of hybrid-shaft generators, which allow more fuel saving, increases design complexity. In this paper, a mixed-integer non-linear programming problem is first formulated, and a power management algorithm with an initialization step for fulfilling the battery recharging requirement and a refinement step for minimizing the fuel consumption is proposed. The simulation results obtained from data of an actual ferry show that the proposed power management algorithm can fully recharge the battery and consumes less fuel than a rule-based power management scheme. Simulations also reveal that fuel consumption depends on available shore power, highlighting the necessity to develop charging infrastructure for practical electrification. Because of its speed, the algorithm can support hardware sizing, e.g., battery sizing. [ABSTRACT FROM AUTHOR]

Recent improvements in vehicle-to-everything (V2X) communication and onboard computing power have enabled the development of control algorithms that jointly optimize the vehicle velocity and powertrain control in Connected and Automated Vehicles (CAVs), commonly referred to as the Eco-Driving problem. This paper presents a novel and computationally efficient algorithm to optimize the velocity planning and energy management in a CAV with a hybrid electric powertrain. The Eco-Driving problem is formulated as a dynamic, constrained optimization problem in the spatial domain, where information about the upcoming speed limits and road topography is assumed known. This problem is solved by embedding an Equivalent Consumption Minimization Strategy (ECMS) into a Dynamic Programming (DP) optimization to obtain a sub-optimal solution that provides results close to the global optimum at a fraction of the computational cost. Further, a multi-layer hierarchical control architecture is proposed as a path to a causal, real-time implementation. The DP-ECMS algorithm is converted into a Model Predictive Control (MPC) framework by using principles of Approximate Dynamic Programming (ADP). This causal implementation is finally benchmarked to a global optimal solution obtained with DP for different scenarios. [ABSTRACT FROM AUTHOR]

Advanced algorithms can promote the development of energy management strategies (EMSs) as a key technology in hybrid electric vehicles (HEVs). Reinforcement learning (RL) with distributed structure can significantly improve training efficiency in complex environments, and multi-threaded parallel computing provides a reliable algorithm basis for promoting adaptability. Dedicated to trying more efficient deep reinforcement learning (DRL) algorithms, this paper proposed a deep q-network (DQN)-based energy and emission management strategy (E&EMS) at first. Then, two distributed DRL algorithms, namely asynchronous advantage actor-critic (A3C) and distributed proximal policy optimization (DPPO), were adopted to propose EMSs, respectively. Finally, emission optimization was taken into account and then distributed DRL-based E&EMSs were proposed. Regarding dynamic programming (DP) as the optimal benchmark, simulation results show that three DRL-based control strategies can achieve near-optimal fuel economy and outstanding computational efficiency, and compared with DQN, two distributed DRL algorithms have improved the learning efficiency by four times. [ABSTRACT FROM AUTHOR]

This paper presents predictive energy management of hybrid electric vehicles (HEVs) via computationally efficient multi-layer control. First, we formulate an optimization problem by considering driveability and a penalty for using service brakes in the objective function to optimize gear, engine on/off, engine clutch state, and power-split decisions subject to constraints on the battery state of charge (SOC) and charge sustenance. Then, we split it into two control layers, including a supervisory control in a higher layer and a local power-split control in a lower layer. In the supervisory layer, a gear and powertrain mode manager (PM) is designed, and optimal gear, engine on/off and clutch states are obtained by using a combination of dynamic programming (DP) and Pontryagin's minimum principle (PMP). Moreover, a real-time iteration Secant method is proposed to calculate optimal battery costate such that the constraint on charge sustenance is satisfied. In the local controller layer, a linear quadratic tracking method (LQT) is used to optimally split power between the engine and the electric machine and keep battery SOC within its bounds. [ABSTRACT FROM AUTHOR]