Equivalent Consumption Minimization Strategy Research Articles

Multi-objective energy management strategy for a dual-stack fuel cell heavy-duty truck via deep reinforcement learning

The Proton Exchange Membrane Fuel Cell (PEMFC) system, as an efficient, zero-pollution emission, and high-efficiency energy conversion device, is particularly suitable for heavy-duty transportation. Considering the limited power of the current PEMFC system, this study focuses on a hybrid fuel cell heavy-duty truck with dual stacks. Considering the optimal operating range of the PEMFC system and the health status of the lithium-ion battery pack, an energy management strategy (EMS) based on the Deep Deterministic Policy Gradient (DDPG) algorithm is established. This strategy optimizes the hydrogen consumption cost, degradation costs of lithium-ion battery and fuel cells. Additionally, a dynamic programming (DP) and an equivalent consumption minimization strategy (ECMS) based EMSs are established. The power allocation and overall transportation costs under the CSC driving cycle are compared and verified for the three EMSs. The results show that the DDPG algorithm provides a more optimal power allocation for the dual-stack fuel cells, improving the overall system efficiency, enhancing the lifecycle of heavy-duty trucks, and significantly reducing overall travel costs.

A multi-objective hierarchical deep reinforcement learning algorithm for connected and automated HEVs energy management

Connected and autonomous vehicles have offered unprecedented opportunities to improve fuel economy and reduce emissions of hybrid electric vehicle (HEV) in vehicular platoons. In this context, a hierarchical control strategy is put forward for connected HEVs. Firstly, we consider a deep deterministic policy gradient (DDPG) algorithm to compute the optimized vehicle speed using a trained optimal policy via vehicle-to-vehicle communication in the upper level. A multi-objective reward function is introduced, integrating vehicle fuel consumption, battery state-of-the-charge, emissions, and vehicle car-following objectives. Secondly, an adaptive equivalent consumption minimization strategy is devised to implement vehicle-level torque allocation in the platoon. Two drive cycles, HWFET and human-in-the-loop simulator driving cycles are utilized for realistic testing of the considered platoon energy management. It is shown that DDPG runs the engine more efficiently than the widely-implemented Q-learning and deep Q-network, thus showing enhanced fuel savings. Further, the contribution of this paper is to speed up the higher-level vehicular control application of deep learning algorithms in the connected and automated HEV platoon energy management applications.

Modeling and control system optimization for electrified vehicles: A data-driven approach

Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. However, their application faces system reliability challenges in the real world, which prevents widespread acceptance by original equipment manufacturers (OEMs). This paper begins by establishing a PHEV model based on physical and data-driven models, focusing on the high-fidelity training environment. It then proposes a real-vehicle application-oriented control framework, combining horizon-extended reinforcement learning (RL)-based energy management with the equivalent consumption minimization strategy (ECMS) to enhance practical applicability, and improves the flawed method of equivalent factor evaluation based on instantaneous driving cycle and powertrain states found in existing research. Finally, comprehensive simulation and hardware-in-the-loop validation are carried out which demonstrates the advantages of the proposed control framework in fuel economy over adaptive-ECMS and rule-based strategies. Compared to conventional RL architectures that directly control powertrain components, the proposed control method not only achieves similar optimality but also significantly enhances the disturbance resistance of the energy management system, providing an effective control framework for RL-based energy management strategies aimed at real-vehicle applications by OEMs.

AC loss optimization of high temperature superconducting magnetic energy storage considering energy management strategies in a hydrogen-battery system

Hydrogen-battery systems have great potential to be used in the propulsion system of electric ships. High temperature superconducting magnetic energy storage (HTS-SMES) has the advantages of high-power density, fast response, and high efficiency, which greatly reduce the dynamic power response of hydrogen-battery systems. Although a superconductor has zero-resistance during direct current operation, considerable alternating current (AC) loss is generated in superconducting coils during charging and discharging operations, which reduces the overall efficiency of the system and increases the quench risk of superconducting coils. Therefore, it is essential to analyze and optimize the AC loss of SMES during the design and optimization of a hydrogen-battery-SMES system. In this work, the AC losses of SMES in a hydrogen-battery-SMES system is studied under three energy management strategies, proportional-integral (PI) control, fuzzy logic, and the equivalent hydrogen consumption minimization strategy. The results show that the high fluctuation of load power can cause significant increases of AC losses in SMES. The fuzzy logic management method has the lowest AC loss among the three strategies. An orthogonal experimental method is presented to optimize the SMES structure. A control strategy based on a neural network is proposed to minimize the AC loss in SMES. Compared with PI control, the SMES AC loss can be reduced by up to 64.2 % using the proposed control scheme.

Data-driven energy utilization for plug-in hybrid electric bus with driving patten application and battery health considerations

The efficient utilization of energy is a crucial consideration for plug-in hybrid electric buses (PHEB). However, achieving the optimal energy management strategy (EMS) for PHEB necessitates the harmonious optimization of both driving conditions and battery status. Consequently, an innovative EMS that integrates the data-driven prediction of the driving cycles and battery health status. Explicitly, the bi-directional long short-term memory algorithm is employed to reconstruct and train the health status data of power batteries, enabling the accurate estimation of their State of Health (SOH) values. Moreover, a database of historical driving behavior is meticulously collected through a real-world PHEB platform, and the driving cycles for a certain while are predicted with the clustering algorithm. Then, this invaluable information on the battery status and driving cycles is seamlessly introduced into an adaptive-network-based fuzzy inference system (ANFIS) to regulate of the energy conversion factor in the following equivalent consumption minimization strategy (ECMS). Besides, the offline global optimization of equivalent fuel consumption with the Pontryagin's minimum principle (PMP) is preliminarily conducted to generate the pre-established energy conversion factors, facilitating convenient access to the engine and motor's reasonable power distribution range. Finally, simulations and experiments have validated the effectiveness of the proposed EMS, demonstrating a 23.57 % reduction in battery SOC depletion and a 10.53 % decrease in fuel consumption compared to traditional ECMS methods. More importantly, the proposed method has been validated for its executability in embedded devices through real-world bench experiments. This research holds significant reference value for energy utilization and practical implementation in the PHEB, thereby contributing to the advancement of knowledge in this field.

A multi-layer predictive energy management strategy for intelligent hybrid electric trucks collaborated with eco-driving control

Nowadays, hybrid electric trucks (HETs) are also urgently expected to further enhance energy efficiency with the development of intelligent driving system. A multi-layer predictive energy management strategy is designed for intelligent parallel HET to enhance fuel economy and computational efficiency. Depending on intelligently implementation of traffic prediction with navigation data, an online smart battery SOC generation is proposed to obtain an approximately global optimal SOC reference trajectory in the upper layer. In the bottom controller, a novel method, the tolerant sequential model predictive control (MPC) collaborated with sampling strategy, could achieve eco-driving in consideration of both driving comfort and safety with less computation time in autonomous driving scenario. Moreover, to simultaneously accomplish fuel economy and decrease computational burden, the integrated equivalent consumption minimization strategy with dynamic programming (ECMS-DP) solver for MPC scheme is presented to frozen SOC state and jointly control torque distribution and gear action. Numerical simulations demonstrate that the proposed multi-layer strategy has improved over 20 % fuel economy and decreased almost 50 % computational burden relative to traditional MPC benchmark strategy, which could exhibit great effectiveness and potential for practical implementation in real driving routes.

Parasitism-predation optimization algorithm-based energy management system for hybrid electric power system

Efficient management of hybrid energy sources, including fuel cells, batteries, and supercapacitors, is crucial for optimizing fuel consumption during emergency aircraft landings. Researchers are increasingly recognizing metaheuristic optimization algorithms for their effective convergence properties. This paper introduces a Parasitism-Predation Optimization Algorithm (PPOA)-based Energy Management System (EMS) hybridized with an Equivalent Consumption Minimization Strategy (ECMS) and an External Energy Maximization Strategy (EEMS) to reduce fuel consumption in Hybrid Energy Sources (HES). The proposed PPOA-EMS is developed using MATLAB/SIMULINK and validated on the Opal-RT 5700 real-time Hardware-in-the-Loop (HIL) simulator. The PPOA-ECMS configuration achieved fuel consumption of 17.5 g in simulations and 17.44 g in real-time simulation, with an efficiency of 88.61%. Similarly, the PPOA-EEMS configuration further reduces the fuel consumption to 15.2 g in simulations and 15.36 g in real-time simulations, with an efficiency of 93.17%. This work highlights the PPOA-EMS optimization in hydrogen fuel consumption when compared to conventional and metaheuristic algorithms, including State Machine Control (SMC), External Energy Maximization Strategy (EEMS), Equivalent Consumption Minimization Strategy (ECMS), Classical PI Controller (CPI), Gray Wolf Algorithm (GWO), Mine Blast Algorithm (MBA), and Slaps Swarm Algorithm (SSA). Finally, the findings demonstrate the effectiveness of PPOA-EMS in enhancing fuel efficiency and ensuring robust energy management.

Optimal Rule-Interposing Reinforcement Learning-Based Energy Management of Series—Parallel-Connected Hybrid Electric Vehicles

P2–P3 series–parallel hybrid electric vehicles exhibit complex configurations with multiple power sources and operational modes, presenting a difficulty in developing efficient energy management strategies. This paper takes a P2–P3 series–parallel hybrid power system-KunTye 2DHT system as the research object and proposes a deep reinforcement learning framework based on pre-optimized energy management to improve the energy consumption performance of the hybrid electric vehicles. Firstly, a control-oriented model is established based on its system configuration and characteristics. Then, the optimal distribution of the motor energy under different operating modes is pre-optimized, which aims to reduce the energy management task’s dimensionality by equating two motors as an equivalent motor. Subsequently, based on real-time traffic information under connected conditions, deep reinforcement learning is utilized to optimize the optimal operating modes of the hybrid system and the optimal distribution between the engine and equivalent motors. Combining the pre-optimized results, the optimal energy distribution between the engine and the two motors in the system is achieved. Finally, performance comparisons are made between the predictive control and the traditional Dynamic Programming and Adaptive Equivalent Consumption Minimization Strategy, revealing the proposed optimization algorithm’s promising potential in reducing fuel consumption.

Energy-Oriented Hybrid Cooperative Adaptive Cruise Control for Fuel Cell Electric Vehicle Platoons.

Given the complex powertrain of fuel cell electric vehicles (FCEVs) and diversified vehicle platooning synergy constraints, a control strategy that simultaneously considers inter-vehicle synergy control and energy economy is one of the key technologies to improve transportation efficiency and release the energy-saving potential of platooning vehicles. In this paper, an energy-oriented hybrid cooperative adaptive cruise control (eHCACC) strategy is proposed for an FCEV platoon, aiming to enhance energy-saving potential while ensuring stable car-following performance. The eHCACC employs a hybrid cooperative control architecture, consisting of a top-level centralized controller (TCC) and bottom-level distributed controllers (BDCs). The TCC integrates an eco-driving CACC (eCACC) strategy based on the minimum principle and random forest, which generates optimal reference velocity datasets by aligning the comprehensive control objectives of the platoon and addressing the car-following performance and economic efficiency of the platoon. Concurrently, to further unleash energy-saving potential, the BDCs utilize the equivalent consumption minimization strategy (ECMS) to determine optimal powertrain control inputs by combining the reference datasets with detailed optimization information and system states of the powertrain components. A series of simulation evaluations highlight the improved car-following stability and energy efficiency of the FCEV platoon.

Enhancing Efficiency in Hybrid Marine Vessels through a Multi-Layer Optimization Energy Management System

Abstract: Configuring green power transmissions for heavy-industry marines is treated as a crucial request in an era of global energy and pollution crises. Following up on this hotspot trend, this paper examines the effectiveness of a modified optimization-based energy management strategy (OpEMS) for a dual proton exchange membrane fuel cells (dPEMFCs)-battery-ultra-capacitors (UCs)-driven hybrid electric vessels (HEVs). At first, the summed power of the dual PEMFCs is defined by using the equivalent consumption minimum strategy (ECMS). Accordingly, a map search engine (MSE) is proposed to appropriately split power for each FC stack and maximize its total efficiency. The remaining power is then distributed to each battery and UC using an adaptive co-state, timely determined based on the state of charge (SOC) of each device. Due to the strict constraint of the energy storage devices’ (ESDs) SOC, one fine-corrected layer is suggested to enhance the SOC regulations. With the comparative simulations with a specific rule-based EMS and other approaches for splitting power to each PEMFC unit, the effectiveness of the proposed topology is eventually verified with the highest efficiency, approximately about 0.505, and well-regulated ESDs’ SOCs are obtained.

A multi-objective energy management optimization for a hybrid electric bus covering an urban route

The development of electrified vehicles is a promising step toward energy savings, emissions reduction, environmental protection, and more sustainable economic growth. In the case of hybrid electric vehicles (HEVs), the energy management strategy (EMS) is essential for their efficiency and energy consumption. Typically, EMS employs rule-based strategies calibrated to general driving conditions. So, this paper proposes to calibrate the EMS of an urban hybrid electric bus that covers a particular route by taking advantage of past driving information. The EMS computes the percentage of the vehicle power demand that must be supplied by each of the sources (fuel and battery) and also controls the heating, ventilating and air conditioning (HVAC) system to achieve cabin thermal comfort. The proposed approach is based on employing an optimal solution by dynamic programing in a previous loop covered by the bus in the considered route. Then, the cost-to-go matrix is stored and used in the following trips by applying the one-step look-ahead rollout, taking profit from the similarities of the loops in the route. To compare and evaluate the performance of the proposed algorithm, a benchmark was carried out by employing the widespread equivalent consumption minimization strategy (ECMS) approach, combined with rule-based strategies in the HVAC control system. Finally, the pareto front presents the trade-off between cabin temperature control performance and total fuel consumption, allowing to compare and evaluate the different EMS calibrations.

Gradient-aware trade-off control strategy dynamic optimization for a fuel cell hybrid electric vehicle

Road gradients not only affect the actual performance of control strategies but also impact battery life due to the drastic changes in power demands. To balance battery degradation with fuel economy using gradient information, this study proposes a gradient-aware trade-off control strategy. Initially, a vehicle dynamics model and a battery degradation model are established. Based on the characteristics of known road information and remaining driving distance, state of charge planning of the battery is conducted. Subsequently, the Non-dominated Sorting Genetic Algorithm-II is applied for bi-objective optimization, yielding a set of Pareto solutions that represent different levels of energy consumption and battery degradation. Thereafter, by introducing a real-time battery degradation severity factor, an optimized bias coefficient is obtained, which adjusts in accordance with the gradient changes. Through the optimization of the bias line, the optimal bias solution set under different working conditions is determined, achieving the optimal control for power system. The fuel economy of the proposed strategy is improved by 6.8% relative to the mileage adaptive Equivalent Consumption Minimization Strategy, and the battery degradation inhibition is improved by 9.3%. After real-world conditions validation, the proposed strategy demonstrates good performance in both economic efficiency and battery life.

Deep reinforcement learning based adaptive energy management for plug-in hybrid electric vehicle with double deep Q-network

The equivalent consumption minimization strategy (ECMS) with pre-calibrated constant equivalence factor (EF) can ensure near global optimal solution for certain driving cycle and enable good real-time capability, but it is difficult to adapt to a wide range of driving conditions. To this end, aiming at the optimal energy management problem of a plug-in hybrid electric vehicle (PHEV), this paper proposes a deep reinforcement learning (DRL) based adaptive ECMS by combing the double deep Q-network (DDQN) and the driving cycle information. The DDQN is applied to correct the EF of the ECMS in a feed-forward manner with the battery state-of-charge (SOC) and the periodic predicted driving cycle information as inputs, and the ECMS is utilized to calculate the engine torque and gear ratio of the powertrain. The driving cycle information is represented by the average velocity, which is predicted by the historical velocity sequence based on the back-propagation (BP) neural network, and the difference of the average velocity between two continuous time windows. The hardware-in-the-loop (HIL) platform is constructed to test the performance of the proposed strategy. It is shown that the future average velocity can be well predicted by the historic velocity sequence. Both simulation and HIL test results demonstrate that the proposed adaptive ECMS based on DDQN exhibits superior performance in improving the vehicle fuel economy.

Research on the adaptability of energy management to thermal management of PEMFCs

Abstract The stable output of power and the effective control of stack temperature play a very important role in the durability and stability of fuel cell hybrid vehicles. In actual working conditions, energy management of power system and thermal management of Proton Exchange Membrane Fuel Cells (PEMFCs) need to be coordinated with each other to jointly ensure the efficient and stable operation of vehicles, however, most of the research in these two directions is independent. In response to the research gaps mentioned above, the concept of adaptability has been proposed for the first time with the aim of combining these two systems for research. This paper takes the fuel cell hybrid vehicle as the research object and establishes the energy management system and thermal management system model based on Matlab/Simulink software. In order to investigate the adaptability of energy management strategies to the temperature control system, two representative types of rule-based and optimization-based energy management strategies (power-following strategy, PFS, and adaptive equivalent hydrogen consumption minimization strategy, A-EHMS) are designed to be numerically simulated in the driving cycle condition of the UDDS, and then combined with the temperature control system of the fuel cell to comparatively analyze the above two types of energy management strategies from the perspectives of power allocation and the impact on the thermal management system. The results show that the PFS provides better protection for the battery under the same operating conditions. And from the perspective of fuel economy and adaptability to the temperature control system, the fuel consumption of A-EHMS is reduced by 12.5% and the adaptability to the temperature control system is improved by about 13.5% compared to PFS.

Comparative study of real-time A-ECMS and rule-based energy management strategies in long haul heavy-duty PHEVs

Road transport is a significant contributor to Green House Gas (GHG) emissions in the European Union (EU) and is responsible for approximately 25% of total GHG emissions in the EU13. The European Commission has proposed stricter CO2 emissions targets for heavy-duty vehicles that require manufacturers to achieve a reduction of 90% in average fleet emissions from new vehicles by 2040 compared to a 2019 baseline. To meet these ambitious targets, there is an urgent need to explore alternative pathways away from conventional fossil fuel-based powertrain systems like electrified powertrains and carbon–neutral fuels. One promising option is the plug-in hybrid electric powertrain concept, which combines the advantages of both power sources, enabling improved fuel efficiency and reduced CO2 emissions. However, the potential of such a plug-in hybrid powertrain needs to be evaluated for heavy-duty trucks.This study focuses on the development of control strategies for the Energy Management System (EMS) of the above powertrain concept. First, a control strategy for the non-predictive EMS’s start/stop functionality and optimization of the torque split between the combustion engine and electric machine is developed and calibrated for efficient engine operation depending on the battery state of charge. In addition, a predictive EMS’s control strategy is developed that uses horizon information of the driving route for optimal utilization of electrical energy within the prediction horizon, thereby further enhancing fuel consumption reduction. The predictive EMS uses Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with Pontryagin’s Minimization Principle (PMP) for online equivalence factor adaptation, providing local optimal solutions in real-time. The study concludes with the energy savings achieved through the implementation of these strategies using real-world driving cycles in the heavy-duty transport sector.

Optimal EMS Design for a 4-MW-Class Hydrogen Tugboat: A Comparative Analysis Using DP-Based Performance Evaluation

In the current trend of hydrogen fuel cell-powered ships, batteries are used together with fuel cells to overcome the limitations of fuel cell technology. However, performance differences arise depending on fuel cell and battery configurations, load profiles, and energy management system (EMS) algorithms. We designed four hybrid controllers to optimize EMS algorithms for achieving maximum performance based on target profiles and hardware. The selected EMS is based on a State Machine, an Equivalent Consumption Minimization Strategy (ECMS), Economic Model Predictive Control (EMPC), and Dynamic Programming (DP). We used DP to evaluate the optimal design state and fuel efficiency of each controller. To evaluate controller performance, we obtained a 4-MW-class tug load profile as a reference and performed simulations based on Nedstack’s fuel cells and a lithium-ion battery model. The constraints were set according to the description of each equipment manual, and the optimal controller was derived based on the amount of hydrogen consumed by each EMS under the condition of completely tracking the load profile. As a result of simulating the hybrid fuel cell–battery system by applying the load profile of the tugboat, we found that the 4-MW EMPC, which requires more state variables and control inputs, is the most fuel-efficient controller.

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.

Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses

—The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS.

Scenario-oriented adaptive ECMS using speed prediction for fuel cell vehicles in real-world driving

To exploit the energy-saving potential and optimize the battery state of charge (SOC) maintaining capability of energy management strategies for fuel cell hybrid vehicles in specific driving scenarios, this study proposes a scenario-oriented adaptive equivalent consumption minimization strategy (SA-ECMS) based on a Nanjing-oriented driving cycle (NODC) and future speeds predicted via a hybrid neural network model. The proposed strategy determines the initial value of the equivalent factor (EF) and the proportional coefficient of the adaptive increment based on the NODC. Then, it periodically adjusts the EF via local optimization process according to the predicted speed to enhance scenario-specific adaptability and energy efficiency performance. Simulation results show that the hybrid neural network model achieves an average calculation time of 0.0033 s with a root-mean-square error of 0.85 m/s for 10 s prediction horizon, outperforming existing speed prediction models. Compared with the existing SOC feedback-based ECMS, the proposed SA-ECMS effectively suppresses the battery SOC within a narrower fluctuation range of −0.12% to 0.33%, achieves a deviation of only 0.0026 from the SOC reference value, and reduces the equivalent hydrogen-fuel consumption by 2.49% to 7.06 g/km.

Equivalent consumption minimization strategy based on global optimization of equivalent factor for hybrid tractor

Due to the increase in emission requirements for non-road vehicles in many countries and the reduction of agricultural personnel, tractors are developing towards high horsepower and electrification. According to the working conditions of high-horsepower tractors, a hydromechanical continuously variable transmission (HMCVT) is designed for hybrid tractors. Taking a tractor equipped with this transmission as the research object, an equivalent factor global optimization model was established and a genetic algorithm was used to optimize the equivalent factor S offline to obtain the optimal equivalent factor of the tractor under different operating mileage and the initial state of charge (SOC) of battery. By using the optimized equivalent factor, the tractor can be in the charge depleting (CD) mode for a longer time on the premise of making full use of the energy in the battery, so as to improve the auxiliary ability of the motor in the whole operation cycle to reduce the fuel consumption of the tractor. The effectiveness of the control strategy is verified by MATLAB/Simulink and hardware in the loop (HIL) tests, and the fuel economy of tractors is improved by 2.939% and 3.909% respectively in the two tests.