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As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7 $\%$. [ABSTRACT FROM AUTHOR]

As a typical energy storage in hydraulic hybrid powertrain, the hydraulic accumulator has high power density but low energy density. There are some efforts in improving the energy density of hydraulic energy storage to achieve balanced performance. Therefore in this study an electric-hydrostatic energy storage system is proposed to replace hydraulic accumulator in a hydraulic hybrid wheel loader. Through active control of proposed energy storage, constant system pressure is possible to provide good vehicle drivability. A rule-based energy management strategy without estimating final drive power is proposed. The vehicle performance and fuel consumption with different constant system pressures are studied. Results show the vehicle fuel consumption is reduced by 5.9% with constant system pressure reduced from 33 to 27 MPa. The energy density and power density of proposed energy storage are calculated, showing a much higher energy density and slightly lower power density than gas-charged accumulator. [ABSTRACT FROM AUTHOR]

The installation of a ground energy storage system (ESS) in the substation can improve the recovery and utilization of regenerative braking energy. This paper proposes an energy management strategy (EMS) of adaptive threshold adjustment for ground ESS. In this regard, this paper analyzes the energy flow in traction power supply system (TPSS) with different headways and no-load voltage. According to the influence of charge and discharge threshold on the energy flow in TPSS, the laws of energy flow are formulated. Subsequently, an EMS based on fuzzy logic control (FLC) is designed. The proposed EMS is adaptive to the variation of headway and no-load voltage. By monitoring the charge-discharge effect of ESS and the variation of substation output energy within two adjacent periods, the charge-discharge threshold is adjusted adaptively to guarantee the recovery effect of regenerative braking energy. Finally, the simulation and field experiment of proposed EMS are carried out with ground energy storage device and actual line conditions of Beijing Batong line. The experiment results indicate the good performance of proposed EMS in terms of energy saving. [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 improves the fuel efficiency of a student-made parallel hybrid electric racing car whose internal combustion engine (ICE) either operates with peak efficiency or is turned off. The control to the ICE thus becomes a binary problem. Owing to the very limited computation resource onboard, the energy management strategy (EMS) for this car must have small time and space complexities. A computationally efficient controller that combines the advantages of dynamic programming (DP) and Pontryagin's minimum principle (PMP) is developed to run on a low-cost microprocessor. DP is employed offline to calculate the optimal speed trajectory, which is used as the reference for the online PMP to determine the real-time ICE on/off status and the electric motor (EM) torques. The normal PMP derives the optimal costate trajectory through solving partial differential equations. The proposed quasi-PMP (Q-PMP) method finds the costate from the value function obtained by DP. The fuel efficiency and computational complexity of the proposed controller are compared against several state of the art methods through both model-in-the-loop (MIL) and processor-in-the-loop (PIL) simulations. The new method reaches similar fuel efficiency as the explicit DP, but requires less than 1% onboard flash memory. The performance of the Q-PMP controller is compared between binary-controlled and continuously controlled ICEs. It achieves roughly 12% higher fuel efficiency for the binary ICE with only approximately 1/3 CPU utilization. [ABSTRACT FROM AUTHOR]

Developing energy management strategies (EMSs) for different types of hybrid electric vehicles (HEVs) is a time-consuming and laborious task for automotive engineers. Experienced engineers can reduce the developing cycle by exploiting the commonalities between different types of HEV EMSs. Aiming at improving the efficiency of HEV EMSs development automatically, this paper proposes a transfer learning based method to achieve the cross-type knowledge transfer between deep reinforcement learning (DRL) based EMSs. Specifically, knowledge transfer among four significantly different types of HEVs is studied. We first use massive driving cycles to train a DRL-based EMS for Prius. Then the parameters of its deep neural networks, wherein the common knowledge of energy management is captured, are transferred into EMSs of a power-split bus, a series vehicle and a series-parallel bus. Finally, the parameters of 3 different HEV EMSs are fine-tuned in a small dataset. Simulation results indicate that, by incorporating transfer learning (TL) into DRL-based EMS for HEVs, an average 70% gap from the baseline in respect of convergence efficiency has been achieved. Our study also shows that TL can transfer knowledge between two HEVs that have significantly different structures. Overall, TL is conducive to boost the development process for HEV EMS. [ABSTRACT FROM AUTHOR]

In order to absorb the regenerative braking energy of trains, supercapacitor energy storage systems (ESS) are widely used in subways. Although wayside ESS are widely used, because of the influence of no-load voltage, and so on, a wayside ESS cannot absorb all the regenerative braking energy in some special cases, and the brake resistor is still activated, which leading to the wasting of energy. In order to completely replace the on-board brake resistor, this paper configures a certain on-board super-capacitor, and based on a DC-side series super-capacitor topology, proposes a hierarchical optimization energy management strategy (EMS). The EMS is divided into three layers: Firstly, the strategy can increase the inverter-side voltage in a short time without changing the traction network voltage, and improve the train braking characteristic curve by utilizing the short-time overvoltage capability of the inverter and the motor; Secondly, by coordinated control with a wayside supercapacitor, the residual regenerative braking energy can be absorbed even in special cases; Finally, based on loss calculation and current prediction, this strategy can effectively reduce the system loss by adjusting the DC voltage on the inverter-side. The proposed control strategy is validated through RT-LAB experiment, and the experimental results agree well with the theory. [ABSTRACT FROM AUTHOR]

The fuel economy of hybrid electric vehicles is very closely associated with the energy management strategy (EMS). In this paper, a practicality-oriented adaptive EMS for a parallel hybrid electric bus is presented, which combines the adaptive neuro-fuzzy inference system (ANFIS) and equivalent consumption minimization strategy (ECMS). Considering the regular and fixed route of the city bus, the optimal control trajectories can be attained by the dynamic programming in advance. Using the rolling optimization method, a group of optimal equivalent factors is extracted from aforementioned control trajectories and used as the training samples. Then, a trained ANFIS model that produces the optimal equivalent factor online is constructed, showcasing striking superiority in self-learning and inference. By applying the derived equivalent factor in the framework of the ECMS, an adaptive energy management controller is available to achieve desirable power distribution online. Finally, the simulation and hardware in the loop (HIL) tests are used to validate the effectiveness and feasibility of the controller. The results demonstrate that, compared with other strategies, the fuel economy with the proposed strategy can be effectively improved. [ABSTRACT FROM AUTHOR]

In order to reduce the peak power of traction substation as much as possible and make better use of the configu-ration capacity of battery energy storage system (BESS) in urban rail transit, a BESS control strategy based on energy transfer is proposed. Based on the actual subway line data, the load characteristics of urban rail transit with different departure intervals are analyzed by using the simulation platform of urban rail transit traction power supply system. Then, based on the characteristics of urban rail traffic load and the high energy density of the battery, the SOC dynamic adjustment module and discharge threshold dynamic adjustment module were added. The discharge threshold is dynamically adjusted according to the SOC of the battery, and part of regenerative braking energy absorbed by the energy storage device is transferred from off-peak/flat peak period to peak period for release. The simulation model and 90 kW physical platform are used to verify the proposed control strategy, and the results show that the control strategy can effectively realize energy transfer. Not only can achieve energy saving and voltage stability, but also can effectively reduce the peak power of traction substation, bring more economic benefits. [ABSTRACT FROM AUTHOR]

The fuel efficiencies of Hybrid Electric Vehicles (HEVs) depend heavily on the control concepts, especially on energy management strategies, because the system efficiency of the vehicle is determined by the exploitation of the electric energy. Control concepts based on optimal control theories like Dynamic Programming (DP) or Pontryagin's Minimum Principle (PMP) have been widely studied over the last two decades, and it has been proven that these control concepts provide near-optimal solutions if future driving conditions are given prior to actual driving. However, it is difficult to apply to real vehicles unless the future driving conditions are known. In this paper, a well-known control strategy based on PMP has been developed, which needs only current driving conditions. The significance of the study is that the control concept introduced herein was evaluated in the IEEE VTS Challenge 2018 competition. It produced the best results in terms of the total energy consumption, where 52 controllers had been competing to minimize the energy consumption in unknown driving cycles. Although the control system was designed from traditional PMP-based control, it becomes a practicable and effective solution by using an adaptive concept for balancing State Of Charge (SOC) of a battery. This study is an extended version of the study presented during IEEE VPPC 2018 and provides the development process of the control concept used in the controller during the IEEE VTS Challenge 2018. [ABSTRACT FROM AUTHOR]

In this paper, an efficient energy management strategy (EMS) of a specific multi-hybrid plug-in electric bus is designed and validated using high-fidelity TruckMaker software simulation. The studied bus is equipped with a tri-hybrid powertrain in which traction torque is produced by three distinct energy sources (internal combustion engine, hydraulic accumulator, and battery). To manage the complex operation of this hybrid powertrain smoothly and efficiently, an EMS composed of two control layers combining fuzzy logic and adaptive optimal control is proposed. The main purpose of this control strategy is the coordination of these multiple energy sources while minimizing the fuel consumption and ensuring smooth torque transitions between the motors. This last-mentioned benefit is quite important, since smooth torque transitions helps to reduce power loss in the hydraulic system and ensure reliability of the powertrain. In addition, the proposed strategy is designed so that it respects the intrinsic constraints of the powertrain components and to deal with the uncertainties on the driving conditions when controlling the hybrid powertrain system. [ABSTRACT FROM AUTHOR]

The multimode power-split architecture for hybrid electric vehicle (HEV) powertrains is generally known for the complexity of its operation. This paper first addresses the challenge of developing an automated on-line near-optimal control strategy for these systems. Particularly, a machine learning logic based on supervised learning is developed for on-line selection of the HEV operating mode, thus the particular set of clutches to be engaged. An efficiency-based approach is then adopted to determine the optimal power-split between powertrain components. Later, the developed strategy finds integration in an optimal design methodology for multimode power-split HEVs considering the effectiveness and ease of on-line controllability. The obtained results are compared with the ones by the traditional HEV design methodology that only considers off-line energy management. The illustrated design methodology with on-line control reveals efficient at identifying the multimode HEV design that demonstrates optimal predicted fuel economy values and ease of on-line controllability simultaneously. Results suggest that the HEV design optimization procedure may produce different outcomes and demonstrate more effective when the evaluation of the on-line controlled operation is integrated. [ABSTRACT FROM AUTHOR]

This paper presents an on-line energy management strategy (EMS) based on trip condition prediction for commuter plug-in hybrid electric vehicles (P-HEVs). The purpose is to provide an on-line predictive control approach to minimize fuel consumption. Two pivotal contributions are provided to realize the purpose. First of all, we establish the trip condition prediction model by using back propagation neural network, to obtain the real-time vehicle-speed trajectory on-line. Particularly, both the genetic algorithm and particle swarm optimization algorithm are applied to improve the prediction accuracy of the trip condition prediction model. Next, to obtain an applicable EMS in real time, we propose a dynamic programming-based predictive control strategy. Finally, a simulation study is conducted for applying the proposed strategy to a practical trip path in the Beijing road network. The results show that the designed trip condition prediction model can effectively realize the on-line vehicle-speed prediction, and the prediction accuracy is more than 93%. In addition, compared to the offline global optimization EMS, although the proposed strategy makes the fuel consumption grow less than 5.2%, it can be implemented in real time. Moreover, compared with the existing real-time EMSs, it can further reduce the fuel consumption and emissions. It shows that the proposed EMS can provide an effective solution for commuter P-HEVs applying it on-line. [ABSTRACT FROM PUBLISHER]

Plug-in hybrid electric vehicles (PHEVs) with fuel and electricity have demonstrated the capability to reduce fuel consumption and emissions by adopting appropriate energy management strategies. In the existing energy management strategies, the dynamic programming (DP)-based energy management strategy (EMS) can realize the global optimization of the fuel consumption if the global vehicle-speed trajectory is known in advance. The global vehicle-speed trajectory can be obtained by applying GPS data of vehicles when the trip path is determined. However, for a trip path without GPS data, the global vehicle-speed trajectory is difficult to be gained. In this case, the DP-based EMS cannot be utilized to achieve the globally optimal fuel consumption, which is the issue discussed in this paper. This paper makes the following two contributions to solve this issue. First of all, the cell transmission model of the road traffic flow and the vehicle kinematics are introduced to obtain the traffic speeds of road segments and the accelerations of the PHEV. On this basis, a hybrid trip model is presented to obtain the vehicle-speed trajectory for the trip path without GPS data. Next, a DP-based EMS with prediction horizon is proposed, and moreover, in order to improve its real-time implementation, a search range optimization algorithm of the state of charge (SOC) is designed to reduce the computational load of DP. In summary, we propose a computation-optimized DP-based EMS through applying the hybrid trip model. Finally, a simulation study is conducted for applying the proposed EMS to a practical trip path in Beijing road network. The results show that the hybrid trip model can effectively construct the vehicle-speed trajectory online, and the average accuracy of the vehicle-speed trajectory is more than 78%. In addition, compared with the existing optimization algorithm for DP calculation, the SOC search range optimization algorithm can further reduce the calculation load of DP. More importantly, compared to the globally optimal DP-based EMS, although the proposed EMS makes the fuel consumption grow less than 5.36%, it can be implemented in real time. Moreover, compared with the existing real-time strategies, it can further reduce the fuel consumption and emissions. Thus, the proposed EMS can offer an effective solution for the PHEV applying it online in the trip path without GPS data. [ABSTRACT FROM PUBLISHER]

A modular cascade machine (MCM) system is composed of several electric machines instead of a unique one. The MCM system is expected for application in electric vehicles (EVs). This paper aims to compare a MCM-based EV with a classical single drive EV in normal operations. The energetic macroscopic representation formalism is used to organize the system model and control of both studied EVs. An optimal energy management strategy of the MCM-based EV is developed for minimizing its energy consumption. Simulation results demonstrate the interest of the MCM-based EV in terms of energy saving. Different driving cycles are analyzed and the comparison of both vehicles highlights the interest of this new multimachine system despite the increase of its mass and cost. [ABSTRACT FROM PUBLISHER]

The performance of a dual energy storage electric vehicle system mainly depends on the quality of its power and energy managements. A real-time management strategy supported by a model predictive control (MPC) using the nonuniform sampling time concept is developed and fully addressed in this paper. First, the overall multiple energy storage powertrain model including its inner control layer is represented with the energetic macroscopic representation and used to introduce the energy strategy level. The model of the system with its inner control layer is translated into the state-space domain in order to develop an MPC approach. The management algorithm based on mixed short- and long-term predictions is compared to rule-based and constant sampling time MPC strategies in order to assess its performance and its ability to be used in a real vehicle. The real-time simulation results indicate that, compared to other strategies, the proposed MPC strategy can balance the power and the energy of the dual energy storage system more effectively, and reduce the stress on batteries. Moreover, battery and supercapacitor key variables are kept within safety limits, increasing the lifetime of the overall system. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel energy management strategy (EMS) of an onboard supercapacitor (SC) for subway applications with a permanent-magnet (PM) traction system. The studied subway train is modeled, and its control is developed, in which the flux-weakening operation is taken into account to minimize the copper loss. The proposed EMS tries to minimize the actual flux-weakening region. Compared with the conventional EMS, the proposed EMS is realized by increasing the dc bus voltage to minimize the demagnetizing $d$-axis current, hence copper loss, in the flux-weakening region instead of reducing the dc bus voltage drop during startup. An experimental platform using the hardware-in-the-loop (HIL) simulation method is developed, and experiments are carried out to verify the effectiveness of the proposed EMS. [ABSTRACT FROM PUBLISHER]