A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

Power-balancing Instantaneous Optimization Energy Management for a Novel Series-parallel Hybrid Electric Bus

Energy management（EM） is a core technique of hybrid electric bus（HEB） in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization（PBIO） energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus（SPHEB） dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.

ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES

Energy management strategy (EMS) is the core of the real-time controlalgorithm of the hybrid electric vehicle (HEV). A novel EMS using the logic threshold approach withincorporation of a stand-by optimization algorithm is proposed. The aim of it is to minimize theengine fuel consumption and maintain the battery state of charge (SOC) in its operation range, whilesatisfying the vehicle performance and drivability requirements. The hybrid powertrain bench testis carried out to collect data of the engine, motor and battery pack, which are used in the EMS tocontrol the powertrain. Computer simulation model of the HEV is established in the MATLAB/Simulinkenvironment according to the bench test results. Simulation results are presented for behaviors ofthe engine, motor and battery. The proposed EMS is implemented for a real parallel hybrid carcontrol system and validated by vehicle field tests.

DEVELOPMENT OF THE ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC URBAN BUSES

A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.

Simulation research of energy management strategy for dual mode plug-in hybrid electrical vehicles

In this paper, a plug-in hybrid electrical vehicle(PHEV) is taken as the research object, and its dynamic performance and economic performance are taken as the research goals. Battery charge-sustaining(CS) period is divided into power mode and economy mode. Energy management strategy designing methods of power mode and economy mode are proposed. Maximum velocity, acceleration performance and fuel consumption are simulated during the CS period in the AVL CRUISE simulation environment. The simulation results indicate that the maximum velocity and acceleration time of the power mode are better than those in the economy mode. Fuel consumption of the economy mode is better than that in the power mode. Fuel consumption of PHEV during the CS period is further improved by using the methods proposed in this paper, and this is meaningful for research and development of PHEV.

Research on System Control and Energy Management Strategy of Flux-Modulated Compound-Structure Permanent Magnet Synchronous Machine

The flux-modulated compound-structure permanent magnet synchronous machine (CS-PMSM), composed of a brushless double rotor machine (DRM) and a conventional permanent magnet synchronous machine (PMSM), is a power split device for plug-in hybrid electric vehicles. In this paper, its operating principle and mathematical model are introduced. A modified current controller with decoupled state feedback is proposed and verified. The system control strategy is simulated in Matlab, and the feasibility of the control system is proven. To improve fuel economy, an energy management strategy based on fuzzy logic controller is proposed and evaluated by the Urban Dynamometer Driving Schedule (UDDS) drive cycle. The results show that the total energy consumption is similar to that of Prius 2012.

Energy management strategy based on dynamic programming with durability extension for fuel cell hybrid tramway

This paper proposes an energy management strategy for a fuel cell(FC)hybrid power system based on dynamic programming and state machine strategy,which takes into account the durability of the FC and the hydrogen consumption of the system.The strategy first uses the principle of dynamic programming to solve the optimal power distribution between the FC and supercapacitor(SC),and then uses the optimization results of dynamic programming to update the threshold values in each state of the finite state machine to realize real-time management of the output power of the FC and SC.An FC/SC hybrid tramway simulation platform is established based on RTLAB real-time simulator.The compared results verify that the proposed EMS can improve the durability of the FC,increase its working time in the high-efficiency range,effectively reduce the hydrogen consumption,and keep the state of charge in an ideal range.

Research on Optimal Configuration of Energy Storage in Wind-Solar Microgrid Considering Real-Time Electricity Price

Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.

Cost-Based Research on Energy Management Strategy of Electric Vehicles Using Hybird Energy Storage System

This paper uses the minimization and weighted sum of battery capacity loss and energy consumption under driving cycles as objective functions to improve the economy of Electric Vehicles(EVs)with an hybrid energy storage system composed of power batteries and ultracapacitors.Furthermore,Dynamic Programming(DP)is employed to determine the objective function values under different weight coefficients,the comprehensive cost consisting of battery aging and power consumption costs,and the relationship between the hybrid power distribution.We also evaluate the real-time fuzzy Energy Management Strategy(EMS),fuzzy control strategies,and a strategy based on DP using the World Light vehicle Test Procedure(WLTP)driving cycle and a synthesis driving cycle derived from New European Driving Cycle(NEDC),WLTP,and Urban Dynamometer Driving Schedule(UDDS)as examples.Then,the proposed strategy is compared with the fuzzy control strategy and the strategy based on DP.Compared with fuzzy energy management strategy(namely FZY-EMS),the proposed EMS reduces the battery capacity loss and system energy consumption.The results demonstrate the effectiveness of the proposed EMS in improving EV economy.

Global Optimization‑Based Energy Management Strategy for Series–Parallel Hybrid Electric Vehicles Using Multi‑objective Optimization Algorithm

The study of series–parallel plug-in hybrid electric vehicles(PHEVs)has become a research hotspot in new energy vehicles.The global optimal Pareto solutions of energy management strategy(EMS)play a crucial role in the development of PHEVs.This paper presents a multi-objective global optimization algorithm for the EMS of PHEVs.The algorithm combines the Radau Pseudospectral Knotting Method(RPKM)and the Nondominated Sorting Genetic Algorithm(NSGA)-II to optimize both energy conservation and battery lifespan under the suburban driving conditions of the New European Driving Cycle.The driving conditions are divided into stages at evident mode switching points and the optimal objectives are computed using RPKM.The RPKM results serve as the fitness values in iteration through the NSGA-II method.The results of the algorithm applied to a PHEV simulation show a 26.74%–53.87%improvement in both objectives after 20 iterations compared to the solutions obtained using only RPKM.The proposed algorithm is evaluated against the weighting dynamic programming and is found to be close to the global optimality,with the added benefits of faster and more uniform solutions.

Function approximation reinforcement learning of energy management with the fuzzy REINFORCE for fuel cell hybrid electric vehicles

In the paper,a novel self-learning energy management strategy(EMS)is proposed for fuel cell hybrid electric vehicles(FCHEV)to achieve the hydrogen saving and maintain the battery operation.In the EMS,it is proposed to approximate the EMS policy function with fuzzy inference system(FIS)and learn the policy parameters through policy gradient reinforcement learning(PGRL).Thus,a so-called Fuzzy REINFORCE algorithm is first proposed and studied for EMS problem in the paper.Fuzzy REINFORCE is a model-free method that the EMS agent can learn itself through interactions with environment,which makes it independent of model accuracy,prior knowledge,and expert experience.Meanwhile,to stabilize the training process,a fuzzy baseline function is adopted to approximate the value function based on FIS without affecting the policy gradient direction.More-over,the drawbacks of traditional reinforcement learning such as high computation burden,long convergence time,can also be overcome.The effectiveness of the proposed methods were verified by Hardware-in-Loop ex-periments.The adaptability of the proposed method to the changes of driving conditions and system states is also verified.

Hierarchical predictive energy management strategy for fuel cell buses entering bus stops scenario

This paper aims to answer how to use traffic information to design energy management strategies for fuel cell buses in a networked environment.For the buses entering the bus stops scenario,this paper proposes a hierarchical energy management strategy for fuel cell buses,which considers the traffic information near the bus stops.In the upper-level trajectory planning stage,the optimal SOC trajectory under various historical traffic conditions is solved through dynamic planning.The traffic information and the best SOC trajectory are mapped through BiLSTM,which can achieve fast,real-time long-term SOC reference.In the lower-level real-time predictive energy management strategy,the optimal SOC is used as the state reference to guide the predictive energy management of fuel cell buses when entering the bus stops.Simulation results show that compared with the strategy without SOC trajectory reference,the life cost of the proposed strategy is reduced by 13.8%,and the total cost is reduced by 3.61%.The SOC of the proposed strategy is closer to the DP optimal solution.

Energy management of hybrid electric propulsion system: Recent progress and a flying car perspective under three-dimensional transportation networks

The hybrid electric propulsion system(HEPS)holds clear potential to support the goal of sustainability in the automobile and aviation industry.As an important part of the three-dimensional transportation network,vehicles and aircraft using HEPSs have the advantages of high fuel economy,low emission,and low noise.To fulfill these advantages,the design of their energy management strategies(EMSs)is essential.This paper presents an in-depth review of EMSs for hybrid electric vehicles(HEVs)and hybrid electric aircraft.First,in view of the main challenges of current EMSs of HEVs,the referenced research is reviewed according to the solutions facing real-time implementation problems,variable driving conditions adaptability problems,and multi-objective optimization problems,respectively.Second,the existing research on the EMSs for hybrid electric aircraft is summarized according to the hybrid electric propulsion architectures.In addition,with the advance in propulsion technology and mechanical manufacturing in recent years,flying cars have gradually become a reality,further enriching the composition of the three-dimensional transportation network.And EMSs also play an essential role in the efficient operation of flying cars driven by HEPSs.Therefore,in the last part of this paper,the development status of flying cars and their future prospects are elaborated.By comprehensively summarizing the EMSs of HEPS for vehicles and aircraft,this review aims to provide guidance for the research on the EMSs for flying cars driven by HEPS and serve as the basis for knowledge transfer of relevant researchers.

A transferable energy management strategy for hybrid electric vehicles via dueling deep deterministic policy gradient

Due to the high mileage and heavy load capabilities of hybrid electric vehicles(HEVs),energy management becomes crucial in improving energy efficiency.To avoid the over-dependence on the hard-crafted models,deep reinforcement learning(DRL)is utilized to learn more precise energy management strategies(EMSs),but cannot generalize well to different driving situations in most cases.When driving cycles are changed,the neural network needs to be retrained,which is a time-consuming and laborious task.A more efficient transferable way is to combine DRL algorithms with transfer learning,which can utilize the knowledge of the driving cycles in other new driving situations,leading to better initial performance and a faster training process to convergence.In this paper,we propose a novel transferable EMS by incorporating the DRL method and dueling network architecture for HEVs.Simulation results indicate that the proposed method can generalize well to new driving cycles,with comparably initial performance and faster convergence in the training process.

Online Learning Control for Hybrid Electric Vehicle

Improvements in hybrid electric vehicle (HEV) fuel economy and emissions heavily depend on an efficient energy management strategy (EMS). However, the uncertainty of future driving conditions generally cannot be easily tackled in EMS design. Most existing EMSs act upon fixed parameters and cannot adapt to varying driving conditions. Therefore, they usually fail to fully explore the potential of these advanced vehicles. In this paper, a novel EMS design procedure based on neural dynamic programming (NDP) is proposed. The NDP is a generic online learning algorithm, which combines stochastic dynamic programming (SDP) and the temporal difference (TD) method. Instead of computing the utility function and optimal control actions through Bellman equations, the NDP algorithm uses two neural networks to approximate them. The weights of these neural networks are updated online by the TD method. It avoids the high computational cost that SDP suffers from and is suitable for real-time implementation. The main advantages of NDP EMS is that it does not rely on prior information related to future driving conditions, and can self-tune with a wide variance in operating conditions. The NDP EMS has been applied to “Qianghua-I”, a prototype of a parallel HEV, using a revolving drum test bench for verification. Experiment results illustrate the potential of the proposed EMS in terms of fuel economy and in keeping state of charge (SOC) deviations at a low level. The proposed research ensures the optimality of NDP EMS, as well as real-time applicability.

Control algorithm and energy management strategy for extended range electric tractors

It is difficult to make full use of the electrical energy of the power battery for extended-range electric tractors because the battery’s state of charge may be relatively high at the end of the running mileage.To address this situation,this paper aimed to study the control parameter adjustment in relation to the power battery’s electrical consumption and the diesel engine’s fuel consumption energy management strategy.Based on the AVL-Cruise simulation platform,the vehicle model of the tractor was established,and the control module of AVL-Cruise was used to compile the energy management strategy.In order to verify the superiority of the proposed strategy,the contrast strategy was employed in terms of the diesel engine start and stop control plus fixed point energy management strategy(FPEMS).The applicability of the proposed strategy was tested through continuous transfer operation and the small area deep loosening operation.The simulation results show that the proposed strategy was of good applicability.Compared with the FPEMS,the fuel consumption reduced significantly,and the electrical consumption of the power battery increased obviously.

Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm

Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-

Energy Management Strategy Considering Multi-time-scale Operational Modes of Batteries for the Grid-connected Microgrids Community

The advantages and promoting applications of the microgrids community(MGC)allows for a critical step being taken toward a smart grid.An energy management strategy(EMS)is essential to intelligently coordinate the operations of the MGC.This paper presents a multi-time-scale EMS consid-ering battery operational modes for grid-connected MGCs.The proposed strategy consists of two modules:day-ahead integrated optimization and realtime distributed compensation.The first module aims to minimize the operational cost of the MGC considering battery free-overcharging protecting.This problem is solved by the mixed integer linear programming(MILP)sim-ulating two charging/discharging modes:limited-current mode and constant-voltage mode.The second module is installed in local MGs to correct the optimizing deviations of the day-ahead static scheduling,which are caused by predicting errors of renewable energy and loads.The main contribution of this work is integrating the advantages of global optimization of the centralized method and the fast computing speed of the distributed method.Experimental results prove the proposed EMS is feasible and effective.The computing time at each updating step is reduced by 75%on average,which has the potential to be adopted in engineering.

Multi-mode energy management strategy for hydraulic hub-motor auxiliary system based on improved global optimization algorithm

Heavy commercial vehicles equipped with a hydraulic hub-motor auxiliary system(HHMAS)often operate under complex road conditions.Selecting appropriate operating mode and realizing reasonable energy management to match unpredictable road conditions are the keys to the driving performance and fuel economy of HHMAS.Therefore,a multi-mode energy management strategy(MM-EMS)based on improved global optimization algorithm is proposed in this study for HHMAS.First,an improved dynamic programming(DP)algorithm for HHMAS is developed.This improved DP algorithm considers the effect of SOC and vehicle speed,thereby preventing the calculation results from falling into local optimization.This algorithm also reduces the dimension of the control variable data grid,and the calculation time is reduced by 35%without affecting the accuracy.Second,a MM-EMS with hierarchical control is proposed.This strategy extracts the optimal control rules from the results of the improved DP algorithm.Then it divides the system’s operating region into two types,namely,single-mode working region and mixedmode working region.In the single-mode working region,mode switching is realized through fixed thresholds.In the mixedmode working region,a linear quadratic regulator(LQR)is adopted to determine a target mode and realize SOC tracking control.Finally,the designed MM-EMS is verified separately in offline simulation and hardware-in-the-loop(HIL)under actual vehicle test cycles.Simulation results show that the results between HIL and offline simulation are largely coincidence.Besides,in comparison with the engine optimal control strategy,the designed MM-EMS can achieve an approximate optimal control,with oil savings of 3.96%.