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In addressing challenges associated with the constrained enhancement of range performance in pure electric tractors, the difficulty in achieving genuinely eco-friendly agricultural machinery with hybrid powertrains, and the outdated approaches to energy optimization in hydrogen fuel cell tractors, a comprehensive methodology covering the entire spectrum of system design, optimization, and validation is presented. This method constructs an integrated electromechanical and hydraulic power bond graph model for an agricultural tractor, encompassing energy systems, drive systems, and lifting systems. It achieves rapid interaction between 20-sim software and Matlab/Simulink software through a script toolbox. In order to tackle the real-time applicability challenges of dynamic programming, a hierarchical dynamic programming is proposed. The dynamic programming strategy results serve as the upper-level output. At the lower layer, preprocessing of impact value variables is carried out to filter the input variables for the general regression neural network. This enables the real-time application of the dynamic programming algorithm and significantly reduces training time. Finally, validation is conducted through both model-in-the-loop and hardware-in-the-loop. The results demonstrate the correctness and superiority of the designed tractor simulation model and energy management strategy model compared to the power-following strategy. Specifically, in plowing conditions, the dynamic programming and hierarchical dynamic programming strategies exhibited reductions of 10.278% and 6.728%, respectively, in equivalent hydrogen consumption compared to power-following strategy. In transportation conditions, the reductions are 16.02% for dynamic programming strategy and 4.87% for hierarchical dynamic programming strategy, both relative to power-following strategy. This study lays the theoretical groundwork for modeling dynamic systems of tractors and optimizing energy management.

The rotary motion field of heavy equipment is a large energy-consuming field, characterized by frequent cyclical start-stop, significant load inertia, and braking energy is usually dissipated as thermal energy, for energy regeneration, no good solution has been found. This paper proposes a new energy-regenerative hybrid drive (ERHD) system and defines three operating modes. Taking the reversible rolling mill’s roll as load, and taking the energy consumption of electric motor (EM) and peak power as evaluation indexes, the influence law of key component parameters on the driving characteristics of the system is studied. Simulation models for the pure electric motor drive (PEMD) system and ERHD system were established by AMESim, along with a rule-based energy management strategy (EMS). The results show that the hydraulic pump/motor displacement has the greatest influence on braking time, peak power, and energy consumption. Under the optimal parameters, the proposed ERHD system can reduce the peak power of EM by 45.75% and energy consumption by 48.33% compared with the PEMD system. Starting and braking times were reduced by 44.54% and 1.21%, respectively. Based on the test results, the ERHD system reduces start-up time by 22.70% and peak power by 23.72%. The proposed system will significantly contribute to developing the rotary motion field of heavy equipment in terms of energy efficiency, low carbon and green.

The automobile industry is regarded as one of the primary producers of greenhouse gas emissions. Hence, one major approach for promoting global sustainability is vehicle electrification. Energy source hybridization is imperative in vehicle electrification because no alternative clean energy source can match the performance of vehicles with internal combustion engines. For a compact energy source system with minimal source degradation and low operational costs, it is crucial to determine the optimal sizing of multiple sources in a hybrid electric vehicle (HEV). The objective of this article is to conduct a thorough assessment of the optimal energy source sizing methodology for HEV alongside an analysis of energy management systems. The importance of dynamic energy source characteristics in the optimal source design is explained and the dynamic modelling of these sources is investigated in detail. Moreover, this paper discusses the dynamic source degradation models used in the literature. This analysis on HEV component sizing provides a comprehensive picture of the current state-of-the-art and highlights several shortcomings, difficulties, and knowledge gaps. The findings indicate that the establishment of an energy management system and energy source sizing are interdependent activities that can prolong the usable life of the energy sources and reduce energy consumption. The aging, temperature, depth of discharge, and charging state are found to be essential characteristics of vehicle energy sources, yet these dynamic aspects have largely been neglected in previous papers. Additionally, if the source deterioration and associated factors are considered throughout the design phase, the HEV system can be built with long-life characteristics. The assessment conclusions highlight the significance of continued analysis to overcome obstacles and improve the hybrid automobile sector.

Energy management strategies (EMSs) for hybrid vehicles have been extensively studied to achieve high system efficiency. EMSs usually focus on the torque split between the electric motor and the main power source. Other powertrain components, such as the gearbox or battery management system, are optimized individually. However, the cooperation between different powertrain components has been studied for specific hybrid architectures and demonstrated to be highly beneficial. A modular EMS that ensures the cooperation of multiple components with different characteristics, shared constraints and objectives, while taking advantage of predictive information will be highly beneficial. To address this research gap, a modular cooperative EMS is proposed using parametric controllers with parameter updates realized in the background using available predictive information. The strategy emphasizes modularity, feasibility, and systematically takes advantage of any available predictive information to improve the overall vehicle objectives, hence considering all the components playing a role in the EMS. The proposed cooperative strategy is first detailed for a generic EMS and then demonstrated for the control of the torque split and gear selection of a hybrid electric vehicle. A numerical study is presented to compare the proposed method with the optimal strategy derived from dynamic programming. The results are detailed for different available predictive information, both in terms of quantity and quality. The proposed method is revealed to be robust against incomplete predictive information and guarantees feasibility with low computational effort, making it real-time capable.

This paper presented an optimal design of a grid-independent hybrid renewable energy system (HRES) that comprises Photovoltaic, Biomass, Hydrogen Fuel Cell, and battery storage. Renewable energy-based system have been endorsed for remote off-grid communities electrification. However, it is difficult to design an optimal hybrid energy system due to the stochastic resource nature, load variation, and high cost of renewable components. The sizing of components for the proposed HRES is determined through the application of an innovative metaheuristic optimization technique called salp swarm algorithm (SSA). Addressing the limitations of the salp swarm algorithm, which include low precision, optimization dimension and convergence rate, a modified version of the salp swarm algorithm (SSA), known as the Levy and sine cosine operator-based (LSC-SSA), was introduced. The proposed algorithm is compared with standard SSA and Genetic Algorithm (GA). The primary goal of the research is to reduce the annualized cost of the hybrid system, whilst taking into account the reliability constraint. The novelty of this research lies in its approach to enhance the performance of a HRES by optimizing its size and energy management strategy (EMS). It is achieved by employing a combined framework that integrate the proposed LSC-SSA into the supervisory EMS. The potential benefits of this approach include reducing the cost of energy and the annualized system cost. The comparative results validate that the LSC-SSA surpasses the standard SSA, and GA algorithms examined, by realizing significant cost reductions amounting to ${\$}$ 82,023, and ${\$}$ 202,127 respectively. Additionally, the outcome indicates that, the LSC-SSA offers the least cost of energy (COE) of ${\$}$ 0.927/kWh, in comparison with the COE values of ${\$}$ 0.931/kWh for SSA and ${\$}$ 0.949/kWh for GA, which are higher. Furthermore, the results indicate that the applied supervisory EMS has effectively assisted the establishing an eco-friendly and economical energy system.

A charging station that integrates renewable energy sources is a promising solution to address the increasing demand for electric vehicle (EV) charging without expanding the distribution network. An efficient and flexible energy management strategy is essential for effectively integrating various energy sources and EVs. This research work aims to develop an Energy Management System (EMS) for an EV charging station (EVCS) that minimizes the operating cost of the EVCS operator while meeting the energy demands of connected EVs. The proposed approach employs a model-free method leveraging Deep Reinforcement Learning (DRL) to identify optimal schedules of connected EVs in real time. A Markov Decision Process (MDP) model is constructed from the perspective of the EVCS operator. The real-world scenarios are formulated by considering the stochastic nature of renewable energy and the commuting behavior of EVs. Various DRL algorithms for addressing MDPs are examined, and their performances are empirically compared. Notably, the Truncated Quantile Critics (TQC) algorithm emerges as the superior choice, yielding enhanced model performance. The simulation findings show that the proposed EMS can offer an enhanced control strategy, reducing the charging cost for EVCS operators compared to other benchmark methods.

This paper proposes a collaborative optimization strategy for speed planning and energy management of intelligent plug-in hybrid electric vehicles (PHEVs). In this study, a single-axis parallel PHEV with the powertrain of P2 configuration is employed as the research object. Then, dynamic programming (DP) is leveraged to ensure optimal fuel economy with the consideration of the simultaneous optimal demand torque distribution and autonomous speed selection. An adjustment coefficient that reasonably constrains the feasible domain of vehicle speed is designed to limit the range of autonomous speed selection according to driving conditions. The reasonableness of speed selection is enhanced by adding penalty functions to inhibit gear shifting and speed fluctuations. To consider the variability of the control strategies based on the constraints of travel time and distance, a time-domain and a space-domain collaborative optimization model are established respectively, and a simulation analysis of the collaborative optimization energy management strategy is conducted. The simulation result shows that the strategy achieves the collaborative optimization of speed autonomous planning and reasonable allocation for demand torque. In addition, the proposed strategy demonstrates preferable energy economy under different constraints based on time and space domains.

Voltage fluctuation and power mismatch which are caused by the decentralization, randomness, and intermittence of distributed energy resource along with load variability cause severe impacts on the security and quality of power network operation. Aiming at alleviating this issue, the structure of an AC/DC hybrid microgrid based on solid-state transformer is presented in this paper. A proper control coordination is developed to guarantee stable and reliable operation of the system. An energy management strategy is proposed to coordinate the power flow among the solid-state transformer, AC microgrid, DC microgrid and energy storage and to effectively suppress the fluctuation of the DC bus voltage. A novel adaptive droop control for the energy storage is proposed to prolong the supercapacitor life and achieve an optimum economic benefit to consumers. The droop coefficient is obtained by fuzzy logic controller of which the state of charge of the supercapacitor and unit-time electricity charge are assumed to be the input parameters. Simulation attest the feasibility of the proposed control coordination and energy management strategy.

This article proposes coordinated model predictive control (MPC) techniques for a DC-coupled hybrid microgrid system with solar photovoltaic and wind generated system. To achieve optimum power generation in the microgrid, the finite-control-set MPC (FCS-MPC) controls both PV-wind generated power using a DC-DC converter and a controlled rectifier. The mathematical formulation of the proposed hybrid microgrid system is described, and maximum power point tracking is employed to guarantee that the grid receives the maximum power. Furthermore, the 3- $\Phi $ bidirectional two-level inverter is connected between the DC-bus and AC grid which is controlled by the grid side FCS-MPC controller. The FCS-MPC is used in all system control parts, eliminating the use of four proportional controllers (PI) and giving a better dynamic response. Additionally, the outcomes are evaluated in comparison to current techniques. The proposed power management technique is also based on the relationship between the overall demand and the produced power provided by both WDG and PV sources. Due to the unpredictability of the sources, several scenarios, including (i) Fixed radiation and fixed wind speed, (ii) Wind speed variation and constant radiation, and (iii) Changing solar radiation and steady wind speed, are considered to validate the performance of the proposed scheme. The findings are then discussed.

Equivalent Consumption Management Strategy (ECMS), a representative energy management strategy for hybrid electric vehicles (HEVs) derived from Pontryagin’s minimum principle, is known to produce a near-optimal solution if the costate or equivalent factor of electric use is appropriately determined according to the driving conditions. One problem when applying the control concept to real-world scenarios is that it is difficult to precisely evaluate the performance of the control parameter before driving is complete, so the costate cannot be determined properly. To address this issue, this study proposes a practical method for estimating an appropriate costate based on Deep Q-Networks (DQNs), which is a reinforcement learning algorithm that uses a Deep Neural Network to evaluate the performances and determine the best control parameter or costate. The control concept benefits vehicle energy management by selecting the control parameter most related to stochastic conditions or future driving information based on artificial intelligence (AI), while optimal control is deterministically conducted by ECMS if the control parameter is given. Simply, only the implicit part of the optimal controller is solved via artificial intelligence. In the simulation results, not only does the proposed control concept outperform an existing ECMS that uses an adaptive technique for determining the costate, but the concept is also very feasible, in that it does not need a model for evaluating the performances.

Hybrid electric vehicles are an effective alternative to the conventional fuel engine vehicles and thus efficient and intelligent energy management is the key for establishing a significant market for the hybrid electric vehicles globally. Recent developments in the field of intelligent techniques and demand to make the energy systems intelligent have become a means to develop energy efficient hybrid electric vehicles. The energy management issue becomes vital in order to enhance the autonomy of hybrid electric vehicles and to reduce the costs. Therefore, a novel approach with intelligent techniques, to control the plug-in hybrid electric vehicles, in front of different customer profiles has been presented. This paper presents the battery performance improvement of a plug-in hybrid electric vehicle using fuzzy logic controller and neural fuzzy logic controller with battery state of charge as a deciding parameter and consequently comparing the performance of both cases. The battery state of charge and engine speed as input has been selected and based on their values the advanced controller decides the accurate torque required to be converted to energy which could be used to charge the battery and this can be achieved by controlling the forward gain value. For this, an advanced fuzzy controller and advanced adaptive nuero fuzzy inference system controller are used to decide the value of forward gain. Simulink environment is used to simulate the performance of the proposed system. This could be helpful in deciding which type of intelligent system is to be used for the power efficient operation of the hybrid electric vehicle. The results of both the control techniques are compared and the better controller is recommended for energy management of a plug-in electric vehicles. The results indicate that advanced control techniques provide the good performance and improving the fuel budget of hybrid electric vehicles.

The lack of a load power prediction function in conventional multi-wheel electric drive vehicles leads to lags in control action. To address this issue, we developed a real-time energy management strategy with improved load power prediction accuracy. Based on an assessment of the overall vehicle structure, a mathematical model for each power source was established using theoretical analysis and data fitting. A method for the joint prediction of non-stationary load power combining Kalman filter and Markov chain forecasting methods was established, and a multi-objective optimization function was constructed under the nonlinear model predictive control framework. To enable real-time optimal control command, a sequential quadratic programming method in the finite time domain was applied. Finally, the multi-power source was optimized and coordinated. Multi-road driving experiments were carried out using a hardware-in-the-loop simulation platform. Comparisons of energy management control strategies with and without power prediction revealed that applying the former enhances the predictability of future load power, significantly optimizes the coordinated control of multiple power sources, improves vehicle fuel economy, and stabilizes bus voltage and battery state of charge. Moreover, it has specific reference significance in engineering application scenarios under conventional model predictive control.

Emergency rescue vehicles are special equipment for fire, flood, earthquake and other disasters. They need to have high efficiency and high mobility under various terrain and road conditions. The fuel economy of emergency rescue vehicles is very poor in climbing and complex terrain due to the heavy weight of vehicle chassis and upper loading. The dual-motor torque increasing hybrid power system designed in this paper greatly improves the dynamic performance and mobility of emergency rescue vehicles, and the use of dual-motor drive in steep slopes and complex terrain can avoid the engine working in low efficiency area. In order to further improve the fuel economy of vehicles under various working conditions, a fuzzy logic control strategy based on improved particle swarm optimization (PSO) is proposed in this paper. The torques of engine and dual-motor are reasonably distributed under different working conditions. A novel fuzzy parameter optimization mode is designed, which reduces the workload of optimization calculation and ensures the optimization accuracy. The off-line simulation and hardware-in-the-loop experiments are carried out for the dual-motor torque increasing hybrid power system model and the proposed improved energy control strategy. The results show that the fuzzy logic control strategy optimized by simulated annealing particle swarm optimization algorithm (SimuAPSO-FLC) has the best effect and meets the requirements of high efficiency, high mobility and stability of the emergency rescue vehicle.

Electrification in the transportation industry is becoming more important to face global warming and replace fossil fuels in the future. Among the available energy sources Li-ion battery and proton exchange membrane fuel cell (PEMFC) are the most promising energy sources. Therefore, employing them in fuel cell hybrid electric vehicles (FCHEVs) to combine their advantages is one of the favorable solutions. However, they still face a major challenge residing in their aging that cause the drop of system performance. On one hand, the degradation is the result of the interaction between several aging mechanisms that react differently with various operating conditions. On the other hand, a hybrid system requires an essential energy management strategy (EMS) for fuel economy and optimal power share. At the end, this EMS has an important impact on the lifetime of sources in term of reducing or favorizing the degradation. Therefore, it is important to consider the degradation in the objectives of the designed EMS. Since the degradation is usually neglected when designing an EMS, this paper tends to review the possible methods for designing a health-conscious EMS. Hence, this paper presents a summary of the main fuel cell (FC) and Li-ion battery aging mechanisms as well as the useful degradation models for state of health estimation. In addition, the existing works that consider the degradation of on-board energy sources in their approaches for increasing their durability are classified and analysed. Remaining challenges are detailed along with a discussion and outlooks about current and future trends of health-conscious EMS.

In order to extend fuel cell lifespan and improve fuel economy of electrical hybrid vehicle with fuel cell/battery/ultracapacitor (FCHEV), a frequency decoupling-based energy management strategy (EMS) for FCHEV using fuzzy control method is proposed. In detail, firstly, according to different characteristics of energy sources, required power of FCHEV is decomposed into three frequency ranges based on Harr wavelet transform and an adaptive-fuzzy filter. Secondly, based on the proposed frequency decoupling, the obtained three frequency required power is supplied by fuel cell-battery and ultracapacitor, respectively, which can guarantee power performance of vehicle and reduce pressure and power fluctuation on fuel cell and battery. Thirdly, for improving fuel economy, one fuzzy controller is proposed to split the power between fuel cell and battery. Finally, the proposed strategy in this paper is verified by advisor-simulink and experimental bench. Simulation and experimental results show that the proposed EMS can effectively reduce impact of power fluctuations on fuel cell, extend its lifespan and reduce fuel consumption on 7.94% compared to equivalent consumption minimization strategy.

This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL). This work aims to address the defect of DRL in tedious training time. First, an optimization control modeling of a hybrid tracked vehicle is built, wherein the elaborate powertrain components are introduced. Then, a bi-level control framework is constructed to derive the energy management strategies (EMSs). The upper-level is applying the particular deep deterministic policy gradient (DDPG) algorithms for EMS training at different speed intervals. The lower-level is employing the TL method to transform the pre-trained neural networks for a novel driving cycle. Finally, a series of experiments are executed to prove the effectiveness of the presented control framework. The optimality and adaptability of the formulated EMS are illuminated. The founded DRL and TL-enabled control policy is capable of enhancing energy efficiency and improving system performance.

With the increasing demand for battery electric buses, the dual-motor coupling powertrain (DMCP) shows great advantages, but it makes the energy optimization problem more complex. To solve the hybrid system optimization problem, a stochastic model predictive control (SMPC) method is proposed to exploit the potential performance of DMCP, where the most critical issue is to improve the prediction accuracy and handle the uncertainties. After analyzing the typical velocity profiles, statistical properties are used to develop a novel Signed Markov Chain Monte Carlo (SMCMC) method that can enhance the accuracy of velocity prediction by more than 50%, compared to conventional Markov Chain methods. Next, considering the uncertainties present in various driving scenarios, the development of driving cycle recognition model based on fuzzy logic control (FLC) is introduced; this method permits to identify the current category of driving cycle rapidly. Then, dynamic programming (DP) is adopted to solve the rolling optimization problems in each finite horizon online, including necessary constraints of dynamic response. Finally, simulation results demonstrate that the proposed energy management strategy can address various daily driving cycles well, and can improve the energy performance by 6% under a generalized combination of driving conditions compared to preliminary rule-based control.

New opportunities enabled by renewable mobile power stations (RMPSs), in association with microgrids (MGs) ability to operate islanded to the main grid, present a likely solution to ensure power system novel reliability requirements in face of the increasing number of severe disruptive events. In this perspective, this paper proposes a novel intelligent RMPS allocation method to support MGs during scheduled islanded operation. In this mode, the isolated operation of a MG is beforehand planned to mitigate the effects caused by possible predictable contingencies, e.g. weather-related events, as well as programmed maintenance. For this, first a novel energy management strategy (EMS) based on dynamic programming specifically designed for MG scheduled islanded operation is proposed. Next, a particle swarm optimization (PSO) method modified to ensure a continuous search space in the face of discrete load shedding policies is developed to determine the adequate allocation of RMPS. The proposed method is simulated considering different scenarios for a MG system considering local distributed generation capacity, electric vehicles penetration, load shedding based on priority groups, as well as technical and operational limits. The obtained results showcase the proposed approach ability to meaningfully improve MG service capacity during scheduled islanded operation.

In order to protect the marine environment, the fuel cell has become a good candidate for the main energy source of marine vehicles due to its remarkable performance of high efficiency, quietness, and cleanness. However, the fuel cell of the hybrid power system has a critical drawback of short lifetime. For this reason, the development of the energy management strategy considering power sources degradation becomes significant and urgent. In addition, the power system configuration has a strong link with the control strategy. In this vein, a sizing method of the hybrid energy storage system for a fuel cell hybrid excursion ferry is proposed to prolong the fuel cell lifetime. Based on the configuration of the power system, a real-time optimization control strategy considering efficiency and durability is developed. To verify the control strategy, the mathematical model of the hybrid power system is established. Compared with the wavelet-based control strategy and rule-based control strategy, the proposed strategy has better performance in terms of the fuel economy, the system durability as well as the dynamic property.

The conversion efficiency of solar energy and the capacity of energy storage batteries are the key technologies limiting the development of solar-powered aircraft. In this paper, a mission-oriented design for the 3-dimensional (3D) path planning of solar-powered unmanned aerial vehicles (SP-UAVs) using limited solar energy to maximize the mission effectiveness is presented. Based on the solar radiation received model, the energy model, and the kinetic and kinematic model of the SP-UAV, a task planning problem oriented to multiobjective optimization is proposed. Both the pseudospectral and colony algorithms are proposed to search for the optimal mission path, and their joint optimization is employed to realize continuous flight and improve the flight mission capabilities. Explicitly, a multiobjective joint strategy is developed, including maximum power ascending, maximum range flight, maximum glide endurance, and minimum power level flight at night. Numerical and simulation results indicate that our proposed design outperforms the existing approaches not only in solar energy utilization but also in universality.

Driving cycle prediction plays a key role in energy management strategy (EMS) for hybrid electric vehicles (HEVs). This paper studies a driving cycle prediction method based on convolutional neural network (CNN). Firstly, the k-shape clustering method is used to group the driving cycle data into six different types. Moreover, this method is compared with the k-means algorithm which is often used for clustering driving cycles. Secondly, CNN is adopted to predict the different types of the driving cycles based on the results of k-Shape clustering. Some basic features are selected to construct the input of the networks with no assistance of human experience. In the process of training neural networks, some high-level features which can describe the information of a driving cycle more accurately are extracted, and the deep neural networks are built, which are different from traditional experience-based driving cycle prediction methods. And then, the better performance of the proposed method is illustrated by making a comparison with the traditional machine learning method. Finally, an adaptive energy management strategy for plug-in hybrid electric buses (PHEB) based on deep learning is given, and simulation results prove the effectiveness of the proposed method.

A primary challenge to the implementation of hybrid electric vehicles (HEVs) is the design of the energy management strategy for the vehicle. Most conventional strategies have been designed for passenger vehicles using rule-based or optimization-based control strategies that rely on navigation support; therefore, the optimal performance of heavy-duty HEVs that lack navigation support cannot be achieved using conventional strategies. In this study, we propose a nonlinear model predictive control (NMPC) for heavy-duty HEVs based on a random power prediction method. To obtain the models of multiple power sources, we analyzed the structure and powertrain of the vehicle using mathematical modeling methods. To account for the lack of navigation support, we used the data-driven prediction method by combining the grey model and Markov chain methods to obtain higher-accuracy ultra-short-term power prediction. Considering the predicted disturbance power, we established a multi-objective optimization function with explicit constraints to optimize fuel consumption, bus voltage, and battery state of charge. Under these constraints, a nonlinear programming problem based on the NMPC could be restricted to find an optimal numerical solution in real time. We validated the control strategy on a hardware-in-the-loop simulation platform and compared its results with those obtained using thermostat control, fuzzy, and dynamic programming approaches. The proposed control strategy achieved a considerably better all-round performance than rule-based control strategies; moreover, the results were considerably similar compared with those of offline global optimization strategies. Furthermore, the proposed method achieved excellent real-time operation capability, thereby providing a valuable reference for practical engineering applications.

This paper presents a prescient energy management strategy based on the model predictive control (MPC) for the parallel plug-in hybrid electric vehicles (PHEVs). In this hierarchical strategy, dynamic programming (DP), with its improved calculation speed, is chosen as the solution algorithm to calculate the optimal power distribution combinations in the predicted receding horizon and under the given terminal battery state-of-charge (SOC) terminal constraint. A synthesized velocity profile prediction (SVPP) method is adopted. The macroscopically and microcosmically predicted velocities obtained by the participatory sensing data (PSD)-based method and the Markov chain (MC), respectively, are synthesized by the linear regression method, obtaining the final velocity profile. In the linear regression step, a particle filter (PF) is implemented for the parameter estimation. According to the characteristics of the driving conditions and components, the terminal battery SOC in each control horizon is constrained by a novel method. Finally, we demonstrate the capability of the proposed scheme in terms of fuel economy improvement by comparing the value of this metric with those of other strategies through simulation.

In the trend of urgent demand of energy saving for public transportation, the series-parallel plug-in hybrid electric bus (SPPHEB) with energy saving potential is proposed. The fuel economy of hybrid power train depends to a significant degree on its control strategy, and then an energy management strategy based on nonlinear model predictive control (NMPC) is obtained for better fuel economy performance. Firstly, the quasi-static model of the plant is described and the reference curve of the state of charge (SOC) and the predictive torque are formulated and illustrated. Then the NMPC framework for SPPHEB, which explored the torque prediction method in the prediction domain and the prediction method of the reference SOC trajectory on the whole working condition, is introduced and completed by adopting dynamic programming (DP) algorithm to solve the nonlinear optimization problem. Finally, the NMPC strategy is simulated in Simulink, and its optimization performance is compared with other strategies such as DP, equivalent consumption minimization strategy (ECMS) and charge-depleting and charge-sustaining (CDCS). The simulation result is that compared with the CDCS strategy, NMPC strategy shows an economic improvement by 18.86%, and 10.36% improvement compared with the ECMS strategy. The good performance of the NMPC strategy is due in part to the consideration of the reference SOC trajectory mechanism and the prediction of the expected torque. The NMPC-based EMS considered both optimization performance and computation burden, which may provide a prospect for further practical application of real vehicles.

In this study, a multi-objective optimization method based on the Radau pseudospectral method is proposed for the energy management strategy in the hybrid energy storage system (HESS). In the proposed method, by approximating state and control variables in the system with global interpolating polynomials, the optimal control problem (OCP) is transformed into a nonlinear programming problem (NLP) and solved by a sparse nonlinear optimizer. Further, the Pareto solution set is obtained by taking the energy consumption of the HESS and the equivalent life of the battery as objective functions. Three solutions representing different tradeoffs were selected for comparative analysis: minimum system energy consumption (5819.60 kJ), with battery life 68368 cycles; maximum battery life (76227 cycles), with energy consumption 5865.68 kJ; and the balanced tradeoff optimal solution with battery life 72488 cycles and energy consumption 5841.96 kJ. The results showed that for every additional 5 kJ in system energy consumption, the battery Ah-throughput was reduced by 0.053 Ah and its equivalent life extended by 876 cycles. Further, compared with the single-cell energy source, the balanced tradeoff optimal solution increased the battery life by 29.92% and decreased the system energy consumption by 1.79%. Thus, this work provides a fast and stable multi-objective optimization method for the energy management strategy of HESS and lays the foundation for obtaining optimal system parameters.

The energy management strategy is the key to achieve fuel cell hybrid system operation with high-efficiency and low-energy cost. The widely used energy management strategies are established based on the fixed models of power sources energy consumption and efficiency. However, due to the gradual performance degradation of the fuel cell during the service period, some model parameters will change significantly and the fixed models will become increasingly inaccurate over time. Therefore, this paper established time-varying models of power sources energy consumption and efficiency by introducing the fuel cell degradation rate. On this basis, in order to reduce the energy consumption of the system, improve the fuel cell efficiency and relatively maintain the SOC level, a novel dual-mode energy management strategy, DMDEE, for hybrid vehicles was proposed. In order to improve the online operation speed of proposed energy management strategy, the optimal control rules were obtained by offline calculation first. Then through comparison experiments with other two energy management strategies, PMP and PF, it was verified that the proposed strategy can effectively reduce the system energy consumption and improve the efficiency of the fuel cell system, meanwhile could make the SOC regress after its deviating from ideal working area. In addition, the mode switching hysteresis control was also validated. Therefore, the effectiveness and superiority of the proposed energy management strategy in this paper has been verified.

In this paper, energy management strategy (EMS) model based on deep recurrent neural network (DRNN) is presented to learn optimal torque distribution for the single-axle parallel hybrid electric vehicle. The model has two distinguishing properties: 1) because the EMS is formulated as a time series prediction problem, taking historical data as input of the EMS model captures the input-and-output dynamic characteristics and enhances the prediction capability and 2) the EMS model based on end-to-end framework directly generates torque distribution results without extracting features of driving cycles and other artificial interference. The extensive simulations are conducted to demonstrate the accuracy and generalization capability of the EMS model in public platform TensorFlow. Comparing with other energy management strategies, our proposed model yields better performance in terms of fuel economy and accuracy. The simulation results show that our proposed EMS model provides a novel way to study the energy management strategy.

The energy management strategy plays a major role in hybrid platforms powered by fuel cells (FCs) and batteries. This paper presents an assessment of energy management focused on fuel economy and battery degradation. Particularly, a proposed heuristic strategy and the widely known equivalent consumption minimization strategy are compared with the optimal solution obtained offline via dynamic programming. The case study is based on a real FC hybrid vehicle. Accordingly, the powertrain model of the vehicle used for the simulations is validated experimentally, and the profile of the power demand is measured from the real application. The results show that the proposed strategy offers the same performance as the equivalent consumption minimization strategy when the battery degradation is prioritized, and in comparison with the optimal off-line solution, it can be seen that there is still margin for improvement in terms of battery degradation.

As the global environment and energy problems are becoming increasingly serious, electric vehicles are expected to play an important role in current and future transportation. In this paper, the configuration of a coaxial series dual-motor coupled propulsion system is analyzed, and the optimization objective function is obtained for a power management strategy in which the control strategy is extracted based on a dynamic programming (DP) method. The simulation model of the powertrain is tested in MATLAB with different strategies under various driving cycles. The control strategies extracted from DP show that the DP/single-mode strategy (strategy based on DP under single-mode) reduces the energy cost by 8.0% and the DP/dual-mode strategy reduces it by 8.8% compared with the traditional proportional control strategy which simply distributes torques of the two electric motors according to default ratios. In addition, the DP/dual-mode strategy performs similarly well under the urban dynamometer driving schedule and UKBUS6 with reductions of 9.1% and 8.6%, respectively. Vehicle experiments are also performed with the YuTong ZK6120BEV under Chinese typical urban bus driving cycles. As a result, the DP/dual-mode control strategy reduces the energy consumption of the 6120BEV by 2.85% on average compared with the traditional proportional control strategy.

Energy management strategies are widely used for the fuel efficiency of hybrid electric vehicle (HEV). Most of the on road hybrid cars use Atkinson cycle engine instead of Otto cycle engine, on account of better efficiency. The authors have already proposed a control oriented extended mean value engine model (EMVEM) of the Atkinson cycle engine and also developed and evaluated its control framework. The prior research was limited to evaluate the fuel efficiency of the engine only, while HEV employs both the engine and the motor. The presented work is an attempt to devise and demonstrate energy management strategy (EMS) by giving full consideration to the powertrain using Atkinson cycle engine. A novel energy management strategy based on the vehicle speed for Atkinson cycle engine for HEV is proposed. A parallel hybrid electric vehicle is modeled in Matlab/Simulink through a forward facing simulator. The proposed EMS with Atkinson cycle engine control framework exhibits the significant improvement in the fuel economy around 12.30% for standard Manhattan driving cycle and 7.22% for the modified federal urban driving schedule (FUDS) driving cycle in comparison with the Otto cycle engine.

A critical challenge in the energy management of hybrid electric vehicles is how to intelligently distribute the power between engine and electric motor with a significant reduction in fuel consumption and emissions. In this paper, iterative dynamic programming (IDP) and adaptive neurofuzzy inference system (ANFIS)-based energy management strategy was proposed for a parallel hybrid electric bus. First, IDP was used to obtain the optimal control trajectories for a specific driving cycle. During the iterative process, a modified gear-shifting strategy was introduced to achieve a good tradeoff between the fuel economy and the drivability performance. Next, multi-ANFIS networks were designed and trained to learn the control law from the available optimal trajectories. Finally, the real-time energy management controller based on the IDP-ANFIS was built to coordinate the output of the two power sources, which reduces the fuel consumption and emissions. The simulation and experimental results reveal that the IDP-ANFIS is feasible, and the performance is superior to that of the equivalent consumption minimization strategy and rule-based method.