Online Energy Management Strategy Research Articles

Energy Management of a Fuel Cell Electric Robot Based on Hydrogen Value and Battery Overcharge Control

The energy management system of a fuel cell electric robot should be highly responsive to provide the required power for various tactical operations, navigation of different routes, and acceleration. This paper presents a new multi-level online energy management strategy for a fuel cell electric robot based on the proposed functions of equivalent hydrogen fuel value evaluation, classification of the battery state of charge via the squared combined efficiency function, identification of the robot maneuver condition based on the proposed operation state of robot function, improvement of the overall energy efficiency based on the proposed function of the battery overcharge control, and separation of the functional points of the fuel cell based on the operational mode control strategy. The simulation study of the proposed online multi-level energy management strategy was carried out with MATLAB R2018b software to verify its superiority by comparing with other strategies. The results indicate a reduction in hydrogen consumption, reduction in fuel cell power fluctuations, prevention of battery overcharging, and incrementation in the total energy efficiency of energy storage systems compared to other energy management strategies.

An Online Energy Management Strategy Based on SOC Fluctuation Optimization for Fuel Cell UAV

An Online Energy Management Strategy Based on SOC Fluctuation Optimization for Fuel Cell UAV

A rule-based online energy management strategy for long-endurance stratospheric airships

Stratospheric airships are typically equipped with photovoltaic energy systems to ensure consistent and controlled long-duration flights. To maximize the utilization of energy generated by the airship's solar array for mission requirements while maintaining energy balance in a computation-efficient manner, we analyzed the characteristics of global optimal energy profiles obtained through the pseudospectral method and distilled them into a rule-based energy management strategy. The rule-based strategy simplifies solving large-scale nonlinear optimization problems posed by pseudospectral method into solving problems for optimal rule logic thresholds, which greatly enhances the speed of strategy generation. Leveraging rapid strategy generation, we developed an online energy management scheme that can achieve closed-loop control by repeatedly updating the rule logic thresholds based on the feedback states. Simulation results demonstrate that the proposed energy management strategy is more effective in utilizing energy to meet mission requirements compared to the two-phase energy management strategy from a previous study. Notably, the online energy management scheme can adapt to uncertainties such as modeling errors, rather than merely providing an open-loop plan.

Online energy management strategy for ammonia-hydrogen hybrid electric vehicles harnessing deep reinforcement learning

Powertrain electrification and fuel decarbonization play pivotal roles in the pursuit of carbon peak and carbon neutrality within the realm of transportation. Ammonia and hydrogen are essential carbon-neutral fuels suited for deployment in heavy-duty, long-haul transportation applications. The technological complementarity of ammonia and hydrogen can be realized in the current context by combining various energy conversion devices. Energy management strategy (EMS) is one of the critical technologies in hybrid systems. In this study, a novel EMS based on deep reinforcement learning (DRL) is proposed for a heavy-duty automotive hybrid system containing an ammonia-hydrogen internal combustion engine (ICE), a fuel cell system (FCS), an ammonia electrolysis cell (AEC), and Li-ion batteries (LB). To address the protracted training times and convergence challenges intrinsic to DRL, a fuzzy logic control (FLC) is introduced to provide expert demonstrations during the DRL agent's learning process. The incorporation of FLC not only facilitates the convergence speed of the DRL algorithm, but also strikes a good balance between the operation efficiency improvement and battery SOC maintenance. The simulation results show that the proposed EMS improves average efficiency by 2 % with a modest 0.75 % SOC reduction across four operational scenarios compared to the FLC-based EMS. After thorough validation of the proposed EMS, a parametric study is conducted to examine three key parameters: charge mode threshold, FCS power change rate limit, and hybrid mode threshold. The study aims to offer valuable insights for formulating the EMS of the ammonia-hydrogen hybrid system.

Real-time energy management strategy with dynamically updating equivalence factor for through-the-road (TTR) hybrid vehicles

Through-the-road–coupled hybrid electric vehicles, comprising a forward-driving internal combustion engine (ICE) and two rear-mounted hub motors, have been attracting increasing attention for their energy efficiency and excellent road passability. However, real-time energy allocation in the ICE and motors is challenging because of complex road scenarios, nonlinear characteristics of components, and the requirement of real-time executability of energy management strategies (EMSs). This study developed an online EMS with an equivalent factor (EF) which is timely updated through the offline rule extraction and online parameter feedback based on real-time driving scenarios. Initially, the utilization of a global dynamic programming approach aims at delineating the potential switching boundary, thus substantially mitigating the computational burden on the controller. Subsequently, an innovative Model Predictive Control-based Equivalent Fuel Consumption Strategy (MPC-ECMS) is introduced. This method integrates real-time velocity prediction with State of Charge (SOC) feedback to enhance fuel efficiency. Notably, the efficiency factor (EF) within ECMS undergoes optimization through a genetic algorithm, with adaptive corrections based on driving condition recognition outcomes. Consequently, the EF is continuously adjusted to ensure convergence of actual SOC towards the reference SOC within the MPC time frame. Through rigorous Hardware in Loop experimentation, the efficacy of the proposed EMS is validated. The outcomes demonstrate its superiority over traditional charge-depleting mode–charge sustaining mode strategies, yielding a notable 13.3 % reduction in total fuel consumption during high power demand scenarios. Furthermore, the method's feasibility within embedded systems is convincingly affirmed.

Parallel-Reinforcement-Learning-Based Online Energy Management Strategy for Energy Storage Traction Substations in Electrified Railroad

The traction power supply system is the only source of power for electric locomotives. The huge power fluctuations and complex operating conditions of the traction power supply system pose a challenge to the efficient operation of energy storage traction substations. The existing energy management strategies are difficult to achieve accurate charging and discharging, difficult to modify the control rules in real-time, and have poor migration capability. For comparison, the reinforcement learning algorithms can address the shortcomings of rule-based energy management strategies due to their model-free feature. Therefore, this paper proposes an energy management strategy based on parallel reinforcement learning to improve the efficiency of energy utilization while speeding up the convergence of the algorithm. More specifically, a Markov decision framework is established for capturing the energy management process. The Monte Carlo sampling process is also improved to achieve offline optimization by parallel reinforcement learning algorithms and reduce the impact of low-value power fragments on iteration speed. Meanwhile, the algorithm is modified to enable online updates. The case study shows that compared with other energy management strategies, the parallel reinforcement learning-based energy management strategy has faster convergence speed, higher energy exchange efficiency, better migration capability, and can adapt to various complex working conditions.

An Online Energy Management Strategy for Fuel Cell Vehicles Based on Fuzzy Q-Learning and Road Condition Recognition

An Online Energy Management Strategy for Fuel Cell Vehicles Based on Fuzzy Q-Learning and Road Condition Recognition

Optimal online energy management strategy of a fuel cell hybrid bus via reinforcement learning

An energy management strategy (EMS) based on reinforcement learning is proposed in this study to enhance the fuel economy and durability of a fuel cell hybrid bus (FCHB). Firstly, a comprehensive powertrain system model for the FCHB is established, mainly including the FCHB’s power balance, fuel cell system (FCS) efficiency, and aging models. Secondly, the state–action space, state transition probability matrix (TPM), and multi-objective reward function of Q-learning algorithm are designed to improve the fuel economy and the durability of power sources. The FCHB’s demand power and battery state of charge (SOC) serve as the state variables and the FCS output power is used as the action variable. Using the demonstration FCHB data, a state TPM is created to represent the overall operation. Finally, an EMS employing Q-learning is formulated to optimize the fuel economy of FCHB, maintain battery SOC, suppress FCS power fluctuations, and enhance FCS lifetime. The proposed EMS is tested and verified through hardware-in-the-loop (HIL) tests. The simulation results demonstrate the effectiveness of the proposed strategy. Compared to a rule-based EMS, the Q-learning-based EMS can improve the energy economy by 7.8%. Furthermore, it is only a 3.7% difference to the best energy economy under dynamic optimization, while effectively reducing the decline and enhancing the durability of the FCS.

A coordinated optimization method of energy management and trajectory optimization for hybrid electric UAVs with PV/Fuel Cell/Battery

There are complex coupling relationships between flight motion and energy management for hybrid electric fixed wing UAVs with photovoltaic/fuel cell/battery. Aiming at realizing high-energy-efficiency long endurance autonomous flight, this paper proposes a coordinated optimization method of trajectory optimization and energy management for hybrid electric UAVs. Numerical simulation and experiment are carried out. Separated by surplus demand energy except that produced by PV, this method includes an optimal energy flight trajectory optimization layer and an energy management layer. For the former, fuzzy neural network sequential convex optimization (FNNSCP) is proposed to handle the flight trajectory optimization problem for maximizing utilization solar energy. For the latter, an online energy management strategy based on convex quadratic programming MPC (CQPMPC) is proposed to realize the power allocation. Numerical simulation results show that FNNSCP reduced 2% energy requirement compared with Radau pseudo-spectral method (RPM) from flight trajectory optimization level. From energy management level, CQPMPC saves hydrogen consumption by 3.1% and 16.3% compared with nonlinear model predictive control (NMPC) and fuzzy logic state machine (FLSM), respectively. In the experiment, less hydrogen fuel is consumed by proposed method by 5.2% and 15.6% compared with NMPC and FLSM, respectively. Experimental results show that the proposed method has better energy-saving performance and excellent real time performance. It indicates that optimizing flight trajectory and improving energy management efficiency can realize the high-energy-efficiency flight of hybrid electric UAVs. This method is conducive to promoting the application of hydrogen energy in the field of UAVs.

Multi-parameter adaptive online energy management strategy for concrete truck mixers with a novel hybrid powertrain considering vehicle mass

Given the characteristics of dynamic vehicle mass, multi-operation scenes, and multi-energy-source and multi-energy-consumption-source, the energy optimization problem of concrete truck mixers remains a challenge. To this end, in this paper, a novel hybrid powertrain with a single-shaft parallel for the upper-part system and extended-range for the driving system is utilized and an adaptive online energy management strategy with multi parameters including the driving cycle, vehicle mass, battery state of charge (SOC), and remaining mileage is presented. First, a driven-data clustering method with fuel consumption differentiation is adopted. Then, using the parallel computing genetic algorithm-optimized rule-based method, a driving condition knowledge base, including optimal control models of the various typical driving conditions under multi-parameter combinations, is built to realize the adaptability and robustness. Parameter identifiers are designed, including a sparrow search-optimized-based deep mixed kernel extreme-learning machine recognizer and a vehicle mass recognizer based on sensor and working scene fusion for obtaining corresponding information online. Finally, simulation experiments are conducted to validate the performance and robustness of the presented control strategy. Results indicate it can reduce fuel consumption by an average value of 15.17% and performs better in the convergence level of terminal SOC in five real composite driving conditions.

Online Energy Management Strategy of the Flexible Smart Traction Power Supply System

The flexible smart traction power supply system (FSTPSS) is a fully electronic traction power supply system (TPSS), which integrates ac-dc–ac traction substations, distributed generation, and hybrid energy storage system (HESS). Compared with the traditional TPSS, the power supply of FSTPSS becomes more diverse, but the randomness of power flow becomes greater. A suitable energy management strategy is needed for FSTPSS to control the power flow of the system. At present, the day-ahead energy management strategy (DAEMS) has been proposed to make an optimal plan for the whole-day operation of FSTPSS, but this energy management strategy does not consider the impact of real-time fluctuation caused by nondeterministic loads or sources on the reliable execution of DAEMS. To solve this problem, this article proposes an online energy management strategy (OEMS) based on long short-term memory (LSTM) network and deep deterministic policy gradient (DDPG) algorithm to counteract the effects of these real-time fluctuations. The proposed OEMS has the advantages of small tracking error, model-free control, and continuous action control. To verify these advantages, a series of comparative simulations is carried out, and the simulation results verify the advantages of the proposed OEMS.

Configuration Design and Optimal Energy Management for Coupled-Split Powertrain Tractor

High-power tractors are regarded as effective operation tools in agriculture, and plugin hybrid tractors have shown potential as agricultural machinery, due to their wide application in energy conservation. However, the allocation of the output power of the motors and engine is a challenging task, given that the energy management strategy (EMS) is nonlinearly constrained. On the other hand, the structure of the continuous variable transmission (CVT) system is complicated, and affects the price of tractors. In this paper, a variable configuration of a tractor that could have the same performance as a complex CVT system is proposed. To address the EMS issues that have shown poor performance in real time, where the programming runs online, firstly a demand power prediction algorithm is proposed in a rotary tillage operation mode. Secondly, an equivalent fuel consumption minimization strategy (ECMS) is used to optimize the power distribution between the engine and the motors. In addition, the equivalent factor is optimized with an offline genetic algorithm. Thirdly, the equivalent factor is converted into a lookup table, and is used for an online power distribution with different driving mileages and state-of-charge (SOC). The simulation results indicate that the equivalent fuel consumption is reduced by 8.4% and extends the operating mileage of pure electric power. Furthermore, the error between the actual and forecasted demand power is less than 1%. The online EMS could improve the mileage of the tractor working cycle with a more feasible fuel economy based on demand power predictions.

A Comprehensive Review on Fuel Cell UAV Key Technologies: Propulsion System, Management Strategy, and Design Procedure

Unmanned aerial vehicles (UAVs) can be regarded as one of the most emerging technologies. Compared with piloted aerial vehicles, UAVs qualify for higher safety and lower cost, and they are more suitable for dangerous missions. Recently, fuel cell (FC) UAVs have obtained more and more attention due to their relatively higher energy density. However, their propulsion system still needs to be further improved to meet different requirements of performing difficult tasks. This article aims to give a comprehensive review of the key technologies of FC UAVs. First, various architectures of the propulsion system and corresponding advantages and disadvantages are introduced. Then, three kinds of management strategies for FC UAVs are reviewed. Next, some special applications and considered design procedures are summarized. Compared with other review articles, this article tries to cover more aspects of FC UAVs. Since these aspects have a tight relationship with each other, giving a comprehensive review can be more beneficial to us to understand the state-of-the-art development of propulsion systems for FC UAVs. The conclusions show that advanced propulsion architectures, efficient online energy management strategies, and professional design procedures are still in urgent demand in the commercial process of FC UAVs.

Online energy management strategy considering fuel cell fault for multi-stack fuel cell hybrid vehicle based on multi-agent reinforcement learning

Because of the high-power demand of fuel cell hybrid vehicles, a multi-stack fuel cell system (MFCS) composed of multiple low-power fuel cell stacks (FCSs) instead of a high-power one has become a satisfactory solution. This is due to the modularity of MFCS, which is more reliable and durable. However, the hybrid power system (MHPSS) of an MFCS hybrid electric vehicle possesses not only MFCS, but also batteries to improve the dynamic performance of MHPSS. Due to the difference in characteristics of MFCS and battery, the energy management strategy (EMS) of MHPSS is the key of ensuring its safe and efficient operation. However, most of the existing MHPSS EMSs are complicated in design and complex to compute online. Besides, they also do not consider the robustness of EMS, that is, EMS has the ability to guarantee the safe operation of MHPSS when the MFCS fails. To solve the above problems, this paper proposes an EMS based on independent Q-learning (IQL) which is an algorithm of multi-agent reinforcement learning to maintain battery state of charge (SOC) and minimize hydrogen consumption. The proposed EMS is not only simple in design, but also can guarantee the normal operation of the MHPSS when the MFCS fails, and can be transplanted to execute online on a microcontroller unit or a field programmable gate array. The various parts of the MHPSS model are first built, then the IQL strategy (IQLS) is trained offline in the established model environment, and finally, the IQLS is ported to the hardware-in-the-loop platform for validation. In order to verify the effectiveness of the proposed EMS, the solution of dimensionality-reduced DP is also used to calculate the fuel economy. Through the experimental verification under different initial SOC and driving cycles, it can be seen that the IQLS proposed in this paper can achieve the goal of maintaining battery SOC and minimizing hydrogen consumption, and also has good generalization ability and safe operation ability under fault conditions.

Multi-objective Online Energy Management Strategies for Fuel Cell Bus Considering Stack Efficiency

Multi-objective Online Energy Management Strategies for Fuel Cell Bus Considering Stack Efficiency

Research on operation cost minimization online energy management strategy for fuel cell hybrid passenger vehicles

The excellent fuel economy of fuel cell passenger vehicles has facilitated its applications. However, high operation costs including economic costs and degradation costs of power source make it uncompetitive. To minimize total operating costs, a new operation cost minimization online energy management strategy (OCMO-EMS) combining dynamic programming (DP) with control rule extraction is proposed in this work. Firstly, both the hydrogen consumption and degradation of power source are incorporated into multi-objective cost function, an improved DP is introduced to generate the globally optimal results. Then, for online application of globally optimal results, a new control rule extraction method that combines fixed thresholds with local working probability is raised. This method effectively extracts control rules to obtain near-optimal result and reduce calibration workload. In the end, offline and hardware in loop (HIL) simulations are carried out on China Light-duty vehicle Test Cycle-Passenger (CLTC_P). Compared with the power-following strategy, the OCMO-EMS reduces the total operating costs by 22.6%. Meanwhile, the total costs difference between DP and OCMO-EMS are within 4%. The HIL results are consistent with offline simulation. Therefore, the strategy proposed in this work can served as a theoretically reference for EMS design of fuel cell passenger vehicles.

Energy Management Based on Neural Networks for a Hydraulic Hybrid Wheel Loader

This paper presents a method to derive optimised energy management strategies for a hydraulic hybrid wheel loader. Energy efficiency is a key aspect for the sustainability of off-road mobile machines. Energy management strategies for on-road hybrid vehicles cannot be directly applied to off-road hybrid machines. One significant reason is that there are added degrees of freedom with respect to how power can be recovered, exchanged and reused in the different functions, such as drivetrain or work functions. This results in more complex energy management strategies being derived. This paper presents an analysis and preliminary conclusions for a proposed method to derive optimised online energy management strategies for a hydraulic hybrid wheel loader. Dynamic programming is used to obtain optimal offline energy management strategies for a series of drive cycles. The results are used as examples to train a neural network. The trained neural network then implements the energy management strategy and is used to make optimised control decisions. Through simulation, the neural network’s ability to learn the dynamic programming decision-making process is shown, resulting in the machine operating with fuel consumption similar to that of the offline optimal energy management strategy. Aspects of simplicity to model these machines for dynamic programming optimisation, the data necessary to train the network, the training process, variables used to learn the dynamic programming decision-making process and the robustness of the network when facing unseen operational conditions are discussed. The paper demonstrates the simplicity of the method for taking into account variables that affect the control decisions, therefore achieving optimised solutions.

Optimal sizing and learning-based energy management strategy of NCR/LTO hybrid battery system for electric taxis

This paper proposes an offline sizing method and an online energy management strategy for the electric vehicle with semi-active hybrid battery system (HBS). The semi-active HBS is composed by Nickel Cobalt Rechargeable (NCR) and lithium titanate (LTO) batteries with a bi-directional DC/DC converter. First, the vehicle dynamics and the HBS are modelled. Second, a hierarchical optimal sizing method is proposed to minimize the distance-based cost (DBC) of electric taxi in a variety of driving cycles. The lower layer optimizes the energy management strategy (EMS) with dynamic programming (DP), while the upper layer optimizes the sizes of HBS for minimum DBC. Based on the sizing results, the DBC decreases firstly and then increases with the increasing LTO size. In addition, the results of DP indicate the SOC of the LTO batteries works between 50% and 80% for optimal NCR lifespan. Third, by using the rule extracted from DP, a learning-based EMS, i.e., deep deterministic policy gradient (DDPG), is proposed with excellent real-time control potential. Finally, the simulation results show that the proposed DDPG EMS achieves the improved performance than fuzzy logic control EMS and closed result with what can be achieved through DP, yet the computation time is much less.

Optimal Control of Electrified Powertrains in Offline and Online Application Concerning Dimensioning of Li-Ion Batteries

Various energy management systems (driving strategies) have been developed to improve the efficiency of electrified vehicle drives. These include strategies from the field of offline optimization to determine the theoretical optimum for a given system, as well as online strategies designed for an on-board application in the vehicle. In this paper, investigations are performed on an SUV electrified by a 48 V hybrid system in P14 topology regarding both offline and online strategies. To calculate the global optimum, the performance of Dynamic Programming (DP) compared to an Equivalent Consumption Minimization Strategy (ECMS) with an iteratively determined equivalence factor is shown. Furthermore, with regard to online energy management strategies (EMS), it is presented how a predictive Online ECMS achieves additional fuel savings compared to a robust, non-predictive implementation. The simulation-based vehicle development allows detailed investigations regarding interactions between battery requirements and EMS. In this context, it is shown how various battery capacities are exploited by the discussed EMS.

A Q-learning fuzzy inference system based online energy management strategy for off-road hybrid electric vehicles

In this paper, a Q-learning fuzzy inference system (QLFIS)-based online control architecture is proposed and applied for the optimal control of off-road hybrid electric vehicles (HEVs) to achieve better dynamic performance, fuel economy and real-time performance. A dynamic model, including a hybrid system, vehicle dynamics and road model, is established to obtain the state feedback according to the current driving environment under command. The optimal control strategy and objective function are both constructed by an adaptive network fuzzy inference system (ANFIS) due to its strong approaching ability. The fuzzy rules and parameters are trained online through the Q-learning algorithm and gradient descent method. This control framework provides a new control idea for the control of off-road vehicles. Without knowing the driving cycle in advance, it achieves a good control effect for different driving environments through online data collection and training. The QLFIS-based control strategy is compared to dynamic programming (DP)-based and rule-based strategies based on two different off-road driving cycles through simulation. The simulation results show that the vehicle dynamic performance and fuel economy are improved with respect to the rule-based strategy, while the calculation time is greatly reduced compared to that of the DP-based strategy.