Equivalent Consumption Minimization Strategy Research Articles

Impact of Engine Inertia on P2 Mild HEV Fuel Consumption

The energy management system (EMS) of a hybrid electric vehicle (HEV) is an algorithm that determines the power split between the electrical and thermal paths. It defines the operating state of the power sources, i.e., the electric motor (EM) and the internal combustion engine (ICE). It is therefore one of the main factors that can significantly influence the fuel consumption and performance of hybrid vehicles. In the transmission path, the power generated by the ICE is in part employed to accelerate the rotating components of the powertrain, such as the crankshaft, flywheel, gears, and shafts. The main inertial components are the crankshaft and the flywheel. This additional power is significant during high-intensity acceleration. Therefore, the actual engine operation is different from that required by the power split unit. This study focuses on exploring the influence of engine inertia on HEV fuel consumption by developing a controller based on an equivalent consumption minimisation strategy (ECMS) that considers crankshaft and flywheel inertia. The optimal solution obtained by the ECMS controller is refined by incorporating the inertia effect of the main rotating components of the engine into the cost function. This reduces the engine operation during high inertial torque transient phases, resulting in a decrease in vehicle CO2 emissions by 2.34, 2.22, and 1.13 g/km for the UDDS, US06, and WLTC driving cycles, respectively.

Energy Management Strategy for a Net Zero Emission Islanded Photovoltaic Microgrid-Based Green Hydrogen System

Investing in green hydrogen systems has become a global objective to achieve the net-zero emission goal. Therefore, it is seen as the primary force behind efforts to restructure the world’s energy, lessen our reliance on gas, attain carbon neutrality, and combat climate change. This paper proposes a power management for a net zero emission PV microgrid-based decentralized green hydrogen system. The hybrid microgrid combines a fuel cell, battery, PV, electrolyzer, and compressed hydrogen storage (CHSU) unit aimed at power sharing between the total components of the islanded DC microgrid and minimizing the equivalent hydrogen consumption (EHC) by the fuel cell and the battery. In order to minimize the EHC and maintain the battery SOC, an optimization-based approach known as the Equivalent Consumption Minimization Strategy (ECMS) is used. A rule-based management is used to manage the power consumed by the electrolyzer and the CHSU by the PV system in case of excess power. The battery is controlled by an inverse droop control to regulate the dc bus voltage and the output power of the PV system is maximized by the fuzzy logic controller-based MPPT. As the hybrid microgrid works in the islanded mode, a two-level hierarchical control is applied in order to generate the voltage and the frequency references. The suggested energy management approach establishes the operating point for each system component in order to enhance the system’s efficiency. It allows the hybrid system to use less hydrogen while managing energy more efficiently.

Hierarchical Energy Management Strategy Based On M-DQN for Fuel Cell Hybrid Electric Vehicle

For fuel cell hybrid electric vehicles equipped with a fuel cell (FC), battery (BAT), and supercapacitor (UC), this paper proposes a hierarchical energy management strategy (EMS) based on the improved M-DQN algorithm to reduce hydrogen consumption, enhance the efficiency of the FC, and maintain the state of charge (SoC) of the BAT. Firstly, an adaptive fuzzy low-pass filter is employed to achieve the frequency decoupling of power demand, enabling the UC to provide/absorb the peak power demand. Secondly, an energy management framework based on M-DQN is designed, and the concept of an equivalent consumption minimization strategy is applied to construct the reward function, which includes penalty factors related to the efficiency of the FC and SoC deviation of the BAT, aiming at optimizing the power allocation between the FC and BAT. Simulations and platform tests were conducted under various typical driving cycles. The results indicate that, compared with the traditional M-DQN-based EMS, the proposed EMS can significantly maintain the SoC of the BAT, improve the efficiency of the FC, reduce hydrogen consumption, and even achieve an average improvement of 5.2% in fuel economy.

Online Model Adaption for Energy Management in Fuel Cell Electric Vehicles (FCEVs)

The growing interest in low-impact mobility technologies has elevated the significance of fuel cell electric vehicles (FCEVs) in the automotive sector. Given the complexity of the resulting powertrain, the need for an effective energy management strategy (EMS) becomes essential to optimize efficiency and energy consumption in vehicles with diverse energy sources. Model-based control is the main approach to address the EMS in electrified vehicles. In particular, fuel cell power is commonly represented through a 1D look-up table using the current demand as input to simplify the implementation in a vehicle control unit. Uncertainties that may be implemented in maps due to simplifying hypotheses, dynamics, ageing, etc., can be propagated to powertrain control, motivating the adoption of adaptive look-up tables for FC modelling. In this study, an extended Kalman filter (EKF) is proposed to adapt the look-up table to actual FC behaviour by measuring its power and gradually correcting calibration errors, drift, and ageing. Subsequently, a standard equivalent consumption minimization strategy (ECMS) is employed to control the FCEV. The fuel cell model is calibrated with experimental data from an FCEV. The results demonstrate that the adaptive strategy outperforms the base calibration. Following an extensive simulation campaign, an improvement of 1.1% in fuel consumption was observed. Remarkably, after just one hour of operation, there was a notable 85% reduction in fuel cell power estimation error, even when the EMS was initially fed a biased look-up table.

Online battery aging calculation in plug-in hybrid electric vehicle considering driving condition

ABSTRACT Online calculation of battery capacity loss and its instantaneous remaining life for electric and hybrid electric vehicles is a challenging subject, particularly for designing and optimizing vehicle energy management in real-life driving conditions. This paper presents an online method for estimating plug-in hybrid electric vehicle battery degradation, considering the driving conditions. For this purpose, a national parallel plug-in hybrid electric vehicle is employed, where its components have been sized based on some experimental map characteristics using a genetic algorithm. Also, a real-world driving cycle is developed based on the data gathered in the actual traffic conditions in Tehran. The vehicle’s energy management system is designed using an adaptive equivalent consumption minimization strategy. In addition, an empirical battery life model combined with an online cycle counting method is used for battery aging calculation. To evaluate the effectiveness of the proposed approach, the capacity fade of the vehicle battery over various driving cycles has been calculated. The comparison of the results confirms the efficacy of the proposed algorithm compared to the Rainflow counting algorithm. Applying the Rainflow to all driving cycles yields consistent outcomes. However, when employing the suggested method, capacity losses of approximately 2.4%, 1.9%, 2.1%, and 1.4% are detected in Tehran, FTP-75, WLTP, and NEDC, respectively, following one hundred driving cycles. This means the capacity loss increases by 35%, 50%, and 71% for FTP-75, WLTP, and Tehran, respectively, compared with the NEDC driving cycle.

Optimal Energy Management Strategy for Repeat Path Operating Fuel Cell Hybrid Tram

This study focuses on minimizing fuel consumption of a fuel cell hybrid tram, operated with electric power from both the fuel cell stack and the energy storage system, by optimizing energy distribution between distinct energy sources. In the field of fuel cell hybrid system application, dealing with real-world optimal control implementation becomes more important. Some ‘online control’ strategies optimize energy management by measuring the current battery’s state and planning for future cycles. However, its dependence on stochastic processes remains a limitation for adapting ‘online control’ even when driving in the same way. In order to optimize energy distribution robustly during the tram’s repetitive cycle operation, we develop a practical control map with a fuel cell hybrid tram simulation model and conduct energy distribution. The control map is based on a mathematical equivalent consumption minimization strategy (ECMS) equation reflecting the characteristics of the fuel cell stack and electric cells. The comparison of fuel consumption with another practical control strategy optimized for a specific railway cycle shows that the suggested map-based optimal control achieves a reduction in fuel consumption while satisfying a boundary condition.

Optimized equivalent consumption minimization strategy-based artificial Hummingbird Algorithm for electric vehicles

The automotive sector is experiencing rapid evolution, with the next-generation emphasizing clean energy sources such as fuel-cell hybrid electric vehicles (FCHEVs) due to their energy efficiency, eco-friendliness, and extended driving distance. Implementing effective energy management strategies play a critical role in optimizing power flow and electrical efficiency in these vehicles. This study proposes an optimized energy management strategy (EMS) for FCHEVs. The suggested EMS introduces a hybridization between the equivalent consumption minimization strategy (ECMS) and the Artificial Hummingbird Algorithm (AHA). The Federal Test Procedure for Urban Driving (FTP-75) is employed to evaluate the performance of the proposed EMS. The results are assessed and validated through comparison with outcomes obtained by other algorithms. The findings demonstrate that the proposed EMS surpasses other optimizers in reducing fuel consumption, potentially achieving a 48.62% reduction. Moreover, the suggested EMS also yields a 15.45% increase in overall system efficiency.

Hydrogen reduction-based energy management strategy of hybrid fuel cell/PV/battery/supercapacitor renewable energy system

Fuel cells can be used in sustainable cities for a variety of purposes, including emergency backup power systems, electric vehicle recharging, and the production of clean, efficient energy for buildings. Additionally, fuel cells can support distributed energy systems, improving energy management, and lowering dependency on conventional energy sources. However, the slow time response of proton exchange membrane fuel cell (PEMFC) during high-level load variation is an issue that needs addressing. To solve this, battery storage and supercapacitor can be integrated with a hybrid generation system including fuel cell, photovoltaic (PV). It's important to have effective energy management strategies (EMSs) to ensure the photovoltaic (PV) array, PEMFC, batteries, and supercapacitors function optimally. An EMS distributes the load demand among these components while maintaining high efficiency and low hydrogen consumption. This paper proposes an effective EMS for a hybrid generating system DC microgrid (MG) having PV, FC, battery, and SC based on recent Harris hawks optimizer (HHO) to manage the energy between the equipment while minimizing the total hydrogen consumption and enhancing the system efficiency. The research work compared several algorithms, including external energy maximization strategy (EEMS), equivalent consumption minimization strategy (ECMS), frequency decoupling and state machine control (FDSMC), proportional integral (PI), Cuckoo search (CS), and grey wolf optimizer (GWO), with the proposed HHO. The EEMS-based HHO algorithm outperformed the others resulting in 18.95 %, 38.77 %, 47.34 %, 34.43 %, 33.9 %, and 20.38 % hydrogen consumption reduction compared to PI, FDSMC, ECMS, EEMS, CS, and GWO, respectively. Moreover, efficiency improved by 7.62 %, 7.77 %, 17.15 %, 11.21 %, 2.17 %, and 5.12 % compared to PI, FDSMC, ECMS, EEMS, CS, and GWO, respectively. The obtained findings demonstrated the robustness and efficacy of the suggested HHO-EMS in obtaining the lowest hydrogen consumption of the DC microgrid that was provided.

Progress in Research on Equivalent Consumption Minimization Strategy Based on Different Information Sources for Hybrid Vehicles

In recent years, hybrid electric vehicles (HEVs) have become one of the leading technologies for renewable energy and environment-friendly automobiles. Since an HEV has two or more power sources whose high-efficiency regions usually do not overlap, it is crucial to choose suitable energy management strategies (EMS) for HEVs to allocate the power outputs, improving the vehicular fuel economy and reducing emissions effectively. The equivalent consumption minimization strategy (ECMS) has become one of the most promising and widely studied EMS due to its merits of optimization potential and onboard real-time availability. This paper reviews and discusses the progress in the state-of-the-art ECMS methods utilizing different information sources. It is proposed to classify various ECMS methods into two groups, i.e., the ECMS with and without external information according to the utilization of road, vehicle, and driver information. The principles, advantages and disadvantages, and application features of those methods are analyzed and compared in detail. Furthermore, ECMS execution methods with state-of-charge correction and ECMS multi-objective optimization are also discussed. Finally, the outlook of research on the ECMS is presented.

Energy management of an eVTOL aircraft with optimization based on the Equivalent Consumption Minimization Strategy (ECMS)

This article introduces an optimization-based energy management strategy developed and validated for electric vertical take-off and landing (eVTOL) aircraft. The primary objective of this strategy is to minimize hydrogen consumption while accounting for resource constraints, such as the battery and fuel cell, to enhance energy efficiency and sustainability while adhering to performance and safety requirements. The eVTOL employed in this research is an essential component of our “Viable” research project, featuring a hybrid propulsion system that combines batteries and fuel cells.To accomplish this, mathematical and computational techniques were employed using the equivalent consumption minimization strategy method to determine the optimal operational parameters for the eVTOL aircraft, considering the available energy resources. These techniques were implemented for the first time in MATLAB, enabling simulations of the aircraft’s performance under various conditions. The results demonstrate the efficacy of the energy management strategy in significantly reducing hydrogen consumption while maintaining optimal performance and safety. Graphs and comparative analyses are presented to highlight the evolution of hydrogen consumption compared to different parameters and to compare the approach with alternative methods.Furthermore, the article explores an alternative solution that offers related results and performance as MATLAB, utilizing the open-source software OpenModelica (OM). Energy management experiments using ECMS were conducted on OM, yielding highly satisfactory outcomes in terms of simulation accuracy and cost-effectiveness. The method was also evaluated in simulations of propulsive system resources, developed on OM, validating the results concerning energy distribution, compliance with constraints, and real-time feedback.In summary, this article presents a comprehensive strategy for effectively managing energy consumption in eVTOL aircraft. By leveraging simulations conducted in MATLAB and OM, the strategy’s effectiveness was assessed, with potential implications for advancing the sustainability of electric eVTOL aircraft.

Bilevel Predictive Control for HEVs Integrating Energy and Cabin Thermal Comfort

This paper proposes an integrated energy and cabin thermal management strategy for a connected hybrid electric vehicle that utilizes a vehicle speed preview informed by traffic connectivity. The proposed strategy employs a bilevel model predictive control framework over long and short predictive horizons to improve fuel economy while ensuring the vehicle’s traction energy and cabin thermal demands are satisfied. The cabin thermal load planning is developed with sensitivity to the vehicle speed of an air conditioning (AC) system, and the proportion of traction energy supplied between the engine and battery is optimized using the Pontryagin minimum principle. Under standard and on-road driving cycles, the simulation results indicate that over 18% and 22% reduction in fuel consumption can be saved compared to a baseline strategy and an equivalent consumption minimization strategy, respectively. Specifically, the electricity consumption of the AC system is reduced by 13% via thermal load planning of the cabin.

A Rolling Convergent Equivalent Consumption Minimization Strategy for Plug-in Hybrid Electric Vehicles

A Rolling Convergent Equivalent Consumption Minimization Strategy for Plug-in Hybrid Electric Vehicles

Online Lifecycle Operating Costs Minimization Strategy for Fuel Cell Buses Considering Power Sources Degradation

The high operating costs limit the popularity and development of fuel cell buses (FCBs). To enhance the lifecycle operating economy of FCBs, this article analyzes the impact of power sources degradation on operating costs and proposes an online operating costs minimization strategy, which integrates online state of health (SOH) estimation, online multiobjective optimization, and online adaptive adjustment into the control strategy framework. Specifically, the SOH of the power sources is estimated by cycle life models in real time. The hydrogen consumption, battery energy consumption, and power sources degradation are incorporated into the objective function and adjusted in real time according to the SOH. Under different SOH, the simulation results indicate that compared with the equivalent consumption minimization strategy, the proposed strategy can reduce fuel cell and battery degradation by 13.8% and 5%, and decrease operating costs by 5.3%. Moreover, the adaptability of the control strategy is enhanced throughout the full lifecycle.

Research on Energy Management Strategy of Fuel Cell Tractor Hybrid Power System

In recent years, more and more attention has been paid to fuel cell-based hybrid tractors. In order to optimize the global power distribution of tractors and further improve the fuel economy and fuel cell durability of the system, this paper designs an energy management strategy to maximize external energy efficiency based on fuel cell/lithium battery/supercapacitor hybrid tractors. This strategy aims to reduce the real-time hydrogen consumption of the system while maximizing the external energy output so as to reduce the impact of load randomness on the output power of the fuel cell. Under the typical ploughing conditions of the tractor, the simulation is compared with the state machine strategy and the equivalent hydrogen consumption minimization strategy. The results show that the proposed strategy meets the power requirements of a given ploughing condition, and compared with the two traditional strategy systems, the performance characteristics of auxiliary energy are more fully exerted. It reduces the burden on fuel cells and improves the durability of fuel cells. The hydrogen consumption of the system was reduced by 11.03 g and 16.54 g, respectively, improving the overall economy of the hybrid system.

Equivalent Consumption Minimization Strategy for Full-Electric Ship Energy Management with Multiple Objectives

Optimal energy management is still a challenge in full-electric vessels. New degrees of flexibility in the energy management resulting from the load sharing between multiple, heterogenous power sources lead to a suboptimal solution using rule-based control. Therefore, advanced control strategies present a solution to the challenge of finding the optimal control input for a nonlinear multi-objective power and energy problem in sufficient time. As additional benefit, advanced control allows to incorporate multiple objectives in the optimization such as minimization of several emissions, operational costs, and component degradation. Equivalent Consumption Minimization Strategy (ECMS) is a strategy for instantaneous optimization, which is promising for applications in vessels with a high degree of uncertainty in the load profile. It incorporates multiple objectives by assigning equivalent cost factors in the cost function, allowing a flexible expansion of the control problem. In this paper, we present a novel ECMS-based control strategy for a full-electric vessel with the ability to react flexibly to changing mission conditions. First, we define the objectives for the control problem, in this study \ce{CO2} production, hazardous emission production, fuel consumption, energy cost, and the degradation of the battery. Second, we develop a pareto-front approach for a-posteriori definition of the equivalent cost factors. To showcase energy consumption reduction, we use a benchmark control based on state-of-the-art control strategies. A full-electric case study vessel with high uncertainty in the load profile is chosen to evaluate the proposed controller. Several different load profiles are generated and tested to evaluate the performance of the ECMS controller in dealing with different types of loads. The results will demonstrate the effectiveness of the proposed novel control strategy in reducing energy consumption while minimizing other hazardous emission outputs and preserving the health of the battery.

Hydrogen consumption and durability assessment of fuel cell vehicles in realistic driving

This study proposes a predictive equivalent consumption minimization strategy (P-ECMS) that utilizes velocity prediction and considers various dynamic constraints to mitigate fuel cell degradation assessed using a dedicated sub-model. The objective is to reduce fuel consumption in real-world conditions without prior knowledge of the driving mission. The P-ECMS incorporates a velocity prediction layer into the Energy Management System. Comparative evaluations with a conventional adaptive-ECMS (A-ECMS), a standard ECMS with a well-tuned constant equivalence factor, and a rule-based strategy (RBS) are conducted across two driving cycles and three fuel cell dynamic restrictions (|di/dt|max≤ 0.1, 0.01, and 0.001 A/cm2s). The proposed strategy achieves H2 consumption reductions ranging from 1.4% to 3.0% compared to A-ECMS, and fuel consumption reductions of up to 6.1% when compared to RBS. Increasing dynamic limitations lead to increased H2 consumption and durability by up to 200% for all tested strategies.

Advanced Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Electric Vehicles

This paper aims to develop an Advanced Equivalent Consumption Minimization Strategy (AECMS) for efficient fuel cell hybrid electric vehicles (FCHEV). The Equivalent Consumption Minimization Strategy (ECMS) is one of the most feasible solutions for hybrid electric vehicles as it has less complexity and low computational cost in making the optimal control decision. However, typical ECMS designs with fixed gain use could not enhance an optimal energy control when dealing with multi decisive factors for highly non-linear and uncertain systems like FCHEVs. To address this control challenge, the AECMS is developed as the combination of the ECMS and an adaptive equivalence factor to optimally split the energy flow between the fuel cell and battery in the FCHEV. First, a cost function is formulated based on two key objectives which are power consumptions of the fuel cell and battery, the equivalence factor and a set of soft and hard constraints, considering the number of fuel cell start/stop, fuel cell optimal operating range, state of charge (SoC) range, temperature ranges and power limits. Second, the adaptive equivalence factor is newly designed as the combination of states and dynamics of the fuel cell and battery and corresponding weighting factors. Third, fuzzy inferences are derived to adjust online the weighting factors in an adaptive manner according to the working conditions of the energy sources, subsequently improving the overall vehicle energy efficiency and maintaining the battery SoC while prolonging their lifetime. Numerical simulations using a FCHEV model have been carried out under UDDS and HWFET drive cycles to illustrate the superior performance of the AECMS against the traditional ECMS.

Variable optimization domain-based cooperative energy management strategy for connected plug-in hybrid electric vehicles

The design of energy management strategy (EMS) that can be applied in real-time with nearly optimal fuel economy for connected plug-in hybrid electric vehicles (C-PHEVs) is an urgent need at present, especially in the context where the benefits of rapid development of intelligent transportation system are increasingly evident. Equivalent consumption minimization strategy (ECMS) is able to achieve the desired performance, on the premise that the equivalent factor (EF) is adjusted reasonably. To achieve this goal, a variable optimization domain-based cooperative EMS for C-PHEVs is proposed in this paper. First, an offline sub-cycle division method is designed, which is effectively utilized to construct a fitting function to output reference EF based on the latest driving information. Second, the theoretical relationship between EF and operating efficiency of the driving components is explored, and the variable optimization domain of EF is designed to avoid inefficient operation of engine. Third, the EF optimization is then simplified into a quadratic problem within the designed domain, which is rapidly solved online with the golden section search algorithm. Finally, the comparison work is conducted under two real-world driving cycles. The results exhibit that the proposed strategy improves the fuel economy of C-PHEV by 6.440 % and 7.936 % over that using the conventional adaptive ECMS (CA-ECMS), respectively. The average engine fuel consumption rates of the proposed strategy are significantly reduced compared with that of CA-ECMS. Moreover, the effectiveness of the proposed strategy is verified on the experiment platform, demonstrating its application prospects.

Optimized energy management and control strategy of photovoltaic/PEM fuel cell/batteries/supercapacitors DC microgrid system

The slow dynamics response of a PEMFC to high-level load variation must be solved. Consequently, it is necessary to integrate the DC microgrid with battery storage banks and ultracapacitors. To guarantee the DC microgrid components: PV array, PEMFC, battery bank, and supercapacitor work effectively; energy management strategies (EMSs) are essential. The EMS distributes the load with the PV array, PEMFC, lithium-ion battery, and supercapacitor considering high efficiency and low H2 consumption. An effective EMS using a recent Beluga Whale Optimization (BWO) was developed for sharing the load between the components of the DC microgrid. Sliding mode control (SMCS), classical equivalent consumption minimization strategy (ECMS) and other optimization algorithms such as White Shark Optimizer (WSO), Manta ray foraging optimization (MRFO), Harris Hawks Optimizer (HHO), soon eagle search (BES) and Artificial Hummingbird Algorithm (AHA) were considered for comparison with the suggested BWO. The results approved the superiority of the suggested ECMS-based BWO; the hydrogen consumption was reduced by 37.67 %, 46.4 %, 25.5 %, 32.58 %, 12.94 %, 12.1 %, and 9.67 % compared to SMCS, ECMS, WSO, MRFO, HHO, BES, and AHA, respectively. In addition, efficiency increased by 4.4 %, 13.49 %, 5.24 %, 10.51 %, 0.95 %, 4.52 %, and 0.32 % compared to SMCS, ECMS, WSO, MRFO, HHO, BES, and AHA, respectively.

A Novel Approach for a Predictive Online ECMS Applied in Electrified Vehicles Using Real Driving Data

To increase the efficiency of electrified vehicles, many energy management strategies (driving strategies) have been proposed. These include both offline optimization techniques to identify a system’s theoretical optimum and online optimization techniques created for onboard use in the vehicle. In the field of online optimization, predictive approaches can achieve additional savings. However, predictions are challenging, and robust usability in all driving situations of the vehicle is not guaranteed. In this study, a new approach for a predictive energy management strategy is presented. It is demonstrated how this so-called predictive Online Equivalent Consumption Minimization Strategy (ECMS) can achieve additional fuel savings compared to a non-predictive Online ECMS by predicting recuperation events using map data. As long as the route is known, map data are available, and the current position of the global navigation satellite system (GNSS) is given, the predictive Online ECMS can be applied. If these requirements are not met, the non-predictive basic implementation can still be used to ensure robust functionality. The methodology is investigated using a backward simulation model of a D-segment vehicle powered by a 48 V hybrid electric system in a P2 topology. A dataset including real driving cycles including map data from Open Street Map (OSM) is used. However, the investigations are limited to the consideration of traffic signal (TS) positions on the upcoming route. Simulation results focus on the interaction between the energy management strategy (EMS) and usable battery energy. More than 1 % average saving potentials compared to a non-predictive implementation are shown. The highest saving potentials are found with a usable battery energy of 100 Wh.