Real-time Energy Management Strategy Research Articles

Critical review on integrated real-time energy management strategy and digital twin applied to hybrid electric UAVs

Critical review on integrated real-time energy management strategy and digital twin applied to hybrid electric UAVs

Multi-Objective Parameter Configuration Optimization of Hydrogen Fuel Cell Hybrid Power System for Locomotives

Conventional methods of parameterizing fuel cell hybrid power systems (FCHPS) often rely on engineering experience, which leads to problems such as increased economic costs and excessive weight of the system. These shortcomings limit the performance of FCHPS in real-world applications. To address these issues, this paper proposes a novel method for optimizing the parameter configuration of FCHPS. First, the power and energy requirements of the vehicle are determined through traction calculations, and a real-time energy management strategy is used to ensure efficient power distribution. On this basis, a multi-objective parameter configuration optimization model is developed, which comprehensively considers economic cost and system weight, and uses a particle swarm optimization (PSO) algorithm to determine the optimal configuration of each power source. The optimization results show that the system economic cost is reduced by 8.76% and 18.05% and the weight is reduced by 11.47% and 9.13%, respectively, compared with the initial configuration. These results verify the effectiveness of the proposed optimization strategy and demonstrate its potential to improve the overall performance of the FCHPS.

A Real-Time Energy Management Strategy of Flexible Smart Traction Power Supply System Based on Deep Q-Learning

A Real-Time Energy Management Strategy of Flexible Smart Traction Power Supply System Based on Deep Q-Learning

Optimization of intelligent charge compression ignition engine in hybrid electric powertrain by adaptive equivalent consumption management strategy

Dual fuel intelligent charge compression ignition (ICCI), taking advantage of two direct injection fuel supply systems, substituted low-carbon fuels for conventional fuels, improving fuel economy and emissions. This study focused on the application of the ICCI engine on the power-split hybrid powertrain and carried out the influence of the real-time energy management strategy on ICCI engine performance. Taking the ideal solution calculated by dynamic programming (DP) as the benchmark, equivalent fuel consumption minimum strategy (ECMS) and adaptive equivalent fuel consumption minimum strategy (AECMS) were compared in aspects of ICCI engine performance and state of charge (SOC) control robustness. The results showed that AECMS had better performance on SOC control, where the maximum deviation from the ideal control by DP was lower than 0.5 % during the charge sustaining stage, and the maximum deviation during the charging and discharging stages was no more than 1.5 %. In terms of engine performance control, the accumulative fuel consumption increased by no more than 20 %, and the average E85 (85 % ethanol in ethanol-gasoline blend) substitution ratio reduced by less than 8 %. Despite consuming a high proportion of E85, the accumulative fuel mass consumption of the hybrid powertrain was still 4 % lower than the conventional diesel vehicle.

Real-Time Energy Management Strategy for Fuel Cell Vehicles Based on DP and Rule Extraction

Energy management strategy (EMS), as a core technology in fuel cell vehicles (FCVs), profoundly influences the lifespan of fuel cells and the economy of the vehicle. Aiming at the problem of the EMS of FCVs based on a global optimization algorithm not being applicable in real-time, a rule extraction-based EMS is proposed for fuel cell commercial vehicles. Based on the results of the dynamic programming (DP) algorithm in the CLTC-C cycle, the deep learning approach is employed to extract output power rules for fuel cell, leading to the establishment of a rule library. Using this library, a real-time applicable rule-based EMS is designed. The simulated driving platform is built in a CARLA, SUMO, and MATLAB/Simulink joint simulation environment. Simulation results indicate that the proposed strategy yields savings ranging from 3.64% to 8.96% in total costs when compared to the state machine-based strategy.

Adaptive real-time energy management strategy using heuristic search for off-road hybrid electric vehicles

The energy management strategies (EMS) of hybrid electric vehicles (HEVs) are put to the test by complex off-road driving conditions. To this end, a novel model-based reinforcement learning (MBRL) algorithm, namely heuristic search, is proposed for EMS. In the MBRL framework, with an online learning Markov Chain (MC) representing the stochastic driving conditions, and a nonlinear state space model describing the deterministic powertrain, an RL model for the HEV is constructed first. Then, heuristic search is introduced to solve the energy management problem, which has two significant advantages: 1) it centers on searching for the optimal action for every current state online; 2) a heuristic function derived from previous experiences is utilized to accelerate the learning. Thus, the optimal actions in each HEV state are learned in real-time, improving the EMS's adaptability to various driving conditions. In the simulation, the proposed EMS is compared with model-free Q-learning (MFQL), model-based Q-learning (MBQL) and dynamic programming (DP) in both off-road driving cycle and standard cycles. Results show that heuristic search only costs about 30 % computing time of MBQL and can maintain better performance than MFQL in various driving conditions.

An Enhanced Extremum Seeking-Based Energy Management Strategy with Equivalent State for Hybridized-Electric Tramway-Powered by Fuel Cell–Battery–Supercapacitors

This article proposes a novel real-time optimization-based energy management strategy (EMS) for proton membrane exchange fuel cell (PEMFC)-battery-supercapacitors-driven hybridized-electric tramways (HETs). The proposed algorithm is derived based on an enhanced extremum seeking (ES) algorithm, with a new equivalent state-of-charge (SOC) and a new adaptive co-state introduced. Thereby, optimized reference power for each power source can be distributed appropriately when using three components. The workability and prominent of the proposed technique are demonstrated through comparative simulations with fuzzy-rule-based EMS (FEMS) and equivalent consumption minimization strategy (ECMS) in two case studies: with and without considering the supercapacitors, as an important factor in the EMS design to stabilize the SOC of energy storage devices (ESDs). Briefly, under the proposed ES-based method, the PEMFC power can be regulated such that high-efficiency can be performed, approximately by 46.7%. Subsequently, the hydrogen consumption is reduced about 31.2% compared to a comparative fuzzy-based EMS. Besides, the supplements’ SOCs at the end of a driving cycle are also regulated to be equal to the initial ones.

Multi-agent deep reinforcement learning-based multi-time scale energy management of urban rail traction networks with distributed photovoltaic–regenerative braking hybrid energy storage systems

The integration of photovoltaics (PVs), regenerative braking (RB) techniques, and energy storage devices has become crucial to promote energy conservation and emission reduction for a sustainable future of urban rail traction networks (URTNs). This paper proposes a tri-level multi-time scale energy management framework for the economic and low-carbon operation of URTNs with PV–RB hybrid energy storage systems (HESSs) based on multi-agent deep reinforcement learning (MADRL). A two-stage stochastic scheduling approach is developed to minimize daily operation and carbon trading costs at the upper level and correct day-ahead scheduling deviations against multi-source uncertainties at the middle level. A MADRL-based real-time energy management strategy is established to optimize the PV–RB power flow and promote its utilization by coordinating distributed HESSs at the lower level. The HESS control problem is formulated as a decentralized partially observable Markov decision process and solved by a multi-agent control algorithm based on monotonic value function factorization. A Copula-based spatio-temporal dependency model is devised to characterize the PV, passenger flow, and traction load uncertainties and generate daily URTN operation scenarios for enhancing day-ahead and intraday decisions. Comparative studies demonstrate the effectiveness of the proposed framework in terms of a cost reduction by 11.98% and a PV–RB energy utilization improvement by 13.94%.

A new real-time optimal energy management strategy for range extended electric vehicles considering battery degradation and engine start-up

A new real-time optimal energy management strategy for range extended electric vehicles considering battery degradation and engine start-up

Real-time energy management strategy of the hydrogen-coupled microgrid based on Informer model prediction results and deep reinforcement learning

Abstract Microgrids (MG) powered by emerging distributed energy resources (DERs) are increasingly pivotal in driving the decarbonization of the power system. Due to the uncertainty of renewable energy sources, electricity generated from wind and solar is typically stored, either in batteries or in the form of high-pressure hydrogen gas within hydrogen storage tanks to meet the refuelling requirements of fuel cell vehicles (FCVs). For FCVs, the substantial operational expenses associated with hydrogen refuelling stations pose a significant barrier to their widespread adoption, with a notable portion of these costs attributed to hydrogen transportation. To address this challenge, this paper employs the Informer model based on a transformer for day-ahead prediction of photovoltaic power generation, as well as load demand forecasting. Subsequently, the Twin-delayed deep deterministic (TD3) policy gradient algorithm is used for real-time energy management of the microgrid. While ensuring that the State of Charge (SOC) remains above 45% daily to meet future hydrogen demands, the MG pursues an optimal strategy to minimize the total daily cost, including electricity procurement, carbon emissions and equipment degradation. Compared to outcomes derived from rule-based approaches, the algorithm introduced in this paper minimizes solar waste and equipment degradation, facilitating hydrogen production during low-cost electricity periods and enhancing economic feasibility.

Four-leg floating interleaved converters for electric vehicle applications with rule-based energy management algorithm

This work contributes to the optimization of hybrid system coupling a fuel cell (FC) to a supercapacitor (SC) for electric vehicles. The complementarity between these two energy sources allows the improvement of the global performances of the system. Our study focuses on the implementation of control and energy management techniques. Our objective is to have a better use of the storage system. In this context, our approach is to regulate the bus current and voltage and then to develop two real-time energy management strategies, one without considering and the other with considering the state of charge of the storage system. A comparison between these two strategies allowed us to understand the importance of the state of charge in the hybrid system. MATLAB/Simulink helped us in showing the performance obtained on a given mission profile of the vehicle dynamics.

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.

Real-time three-level energy management strategy for series hybrid wheel loaders based on WG-MPC

The application of Hybrid Electric Wheel Loaders (HEWL) represents an attractive option for future industrial development. In order to reduce the equivalent fuel consumption and extend the lifetime of the battery, a real time whale optimal algorithm and general regression neural network (WOA-GRNN) based hierarchical model predictive control (WG-MPC) energy management strategy for HEWL is proposed. The structure of the WG-MPC consists of three layers, namely, the prediction layer, the economic layer and the control layer. In the prediction layer, a prediction model based on WOA-GRNN is proposed for the complex demand power prediction. In the economic layer, the equivalent diesel cost, diesel generator operation cost, the lifetime cost of the diesel generator and the battery pack are considered. The control layer is designed based on improved MPC. Through the simulation analysis, the proposed WOA-GRNN prediction model achieves the best prediction accuracy compared with the traditional prediction methods. Compared with FT-MPC and ECMS, the total economic cost of WG-MPC is reduced by 3.42% and 5.12%, respectively. The simulation results demonstrate that the proposed WG-MPC strategy has good control performance, high computational efficiency, low equivalent diesel consumption, longer battery lifetime and good use cost economy, which is of great significance for practical applications.

A real-time energy management strategy for fuel cell vehicle based on Pontryagin’s minimum principle

This paper proposes a Pontryagin's minimum principle (PMP) energy management strategy (EMS) based on driving cycle recognition for fuel cell vehicle powertrains, aiming to minimize hydrogen consumption and fuel cell degradation. Firstly, the neural network-based driving cycle recognizer is optimized using the tuna swarm optimization (TSO) algorithm and trained under four typical driving cycles. Then, the optimal co-state variables for the four driving cycles are obtained by iteration. Finally, the co-state variables are dynamically updated based on real-time driving cycle recognition results. Comparative analysis demonstrates that the PMP-DCR effectively improves fuel cell lifetime and vehicle economy under short-distance driving cycles. Based on the combined driving cycle, the proposed PMP-DCR EMS exhibits similar economy performance to optimal dynamic programming (DP) EMS, reducing equivalent hydrogen consumption by 13.8% and 9.2%, and decreasing fuel cell degradation rates by 93% and 8.7% in comparison to the conventional power-following and PMP EMS, respectively.

Optimized energy management for plug-in hybrid vehicles with predicted driving cycles1

The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study.

A Fuzzy Logic Control-Based Approach for Real-Time Energy Management of the Fuel Cell Electrical Bus Considering the Durability of the Fuel Cell System

The present study proposes a fuzzy logical control-based real-time energy management strategy (EMS) for a fuel cell electrical bus (FCEB), taking into account the durability of the fuel cell system (FCS), in order to enhance both the vehicle’s economic performance and the FCS’s service life. At first, the model of the FCEB is established whilst the power-following strategy is also formulated as a benchmark for the evaluation of the proposed strategy. Subsequently, a fuzzy logical controller is designed to improve the work efficiency of the FCS, in which the battery state-of-charge (SOC) and the vehicle’s desired power are considered the inputs, whilst the power of the FCS is the output. Then, a limitation method is integrated into the fuzzy logical controller to restrict the change rate of the FCS’s power to strengthen the FCS’s service life. At last, the evaluation is accessed based on the China city bus driving cycle (CCBC). The results indicate that the proposed fuzzy logical strategy can satisfy the dynamic performance of the FCEB well. Importantly, it also has a remarkable effectiveness in terms of promoting the FCEB’s economy. Despite a slight reduction in contrast to the fuzzy logical control, the improvements of the strategy in which the FCS’s durability is considered are still acceptable. The change rate of the FCS’s power can be confined to ±10 kW. Meanwhile, the promotion of economic performance can reach up to 8.43%, 7.69%, and 6.53% in the proposed durability consideration strategy in contrast to the power-following strategy under different battery SOCs. This will significantly benefit both the energy saving and the FCS’s durability.

Real-time energy management strategy for flexible traction power supply system

Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases.

Integrated real-time optimal energy management strategy for plug-in hybrid electric vehicles based on rule-based strategy and AECMS

Integrated real-time optimal energy management strategy for plug-in hybrid electric vehicles based on rule-based strategy and AECMS

Real-Time Global Optimal Energy Management Strategy for Connected PHEVs Based on Traffic Flow Information

Real-Time Global Optimal Energy Management Strategy for Connected PHEVs Based on Traffic Flow Information

Integrated real-time optimal energy management strategy for plug-in hybrid electric vehicles based on rule-based strategy and AECMS

Integrated real-time optimal energy management strategy for plug-in hybrid electric vehicles based on rule-based strategy and AECMS