Equivalent Consumption Minimization Strategy Research Articles

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.

Comprehensive Study of Fuel Cell Hybrid Electric Vehicles: Classification, Topologies, and Control System Comparisons

The utilization of fuel cells (FC) in automotive technology has experienced significant growth in recent years. Fuel cell hybrid electric vehicles (FCHEVs) are powered by a combination of fuel cells, batteries, and/or ultracapacitors (UCs). By integrating power converters with these power sources, the FCHEV system can overcome the limitations of using them separately. The performance of an FCHEV is influenced by the efficiency of the power electronics converter controller, as well as the technical efficiency of the power sources. FCHEVs need intricate energy management systems (EMSs) to function effectively. Poor EMS can lead to low efficiency and accelerated fuel cell and battery degradation. The literature discusses various types of EMSs such as equivalent consumption minimization strategy, classical PI controller, fuzzy logic controller, and mutative fuzzy logic controller (MFLC). It also discusses a systematic categorization of FCHEV topologies and delves into the unique characteristics of these topologies. Furthermore, it provides an in-depth comparative study of EMSs applied in FCHEVs, encompassing rule-based, optimization-based, and advanced learning-based approaches. However, comparing different EMSs can be challenging due to the varying vehicle and system parameters, which might lead to false claims being made regarding system performance. This review aims to categorize and discuss the various topologies of FCHEVs, highlighting their pros and cons, and comparing several EMSs based on performance metrics such as state of charge (SOC) and FC deterioration. This paper seeks a deeper comprehension of the recent advancements in EMSs for FCHEVs. It offers insights that can facilitate a more comprehensive grasp of the current state of research in this field, aiding researchers in staying up to date with the latest developments.

FlexNet: A warm start method for deep reinforcement learning in hybrid electric vehicle energy management applications

Deep reinforcement learning (DRL) has been widely studied in the energy management of hybrid electric vehicles (HEVs) for its remarkable energy efficiency improvement compared to conventional methods. However, how to alleviate the time consumption of training a stable reinforcement learning agent still needs to be solved in real-world implementation. This study presents a human expert knowledge encoded ‘warm start’ method with the flexibility to change the neural network architecture. The expert knowledge is encoded in a decision tree which then initializes the weights and bias of the DRL neural network. Compared with another fixed architecture warm start method, the proposed FlexNet exhibits improved learning speed by 60.8 % and 88.8 % in action space 50 and 100, respectively. The energy consumption by the proposed FlexNet EMS method is 12.2 % and 6.4 % better than rule-based and equivalent consumption minimization strategy, respectively. This proposed warm start method can reduce learning time and increase energy efficiency in various energy management applications.

A controllable neural network-based method for optimal energy management of fuel cell hybrid electric vehicles

Neural Networks (NNs) can be used for energy management of hybrid vehicles, but they are hard to tune in inference to adapt to different driving conditions. To make the NN-based energy management strategy more flexible, this paper proposes a controllable NN for optimal energy management of fuel cell hybrid electric vehicles. Inspired by the equivalent factor in the Equivalent Consumption Minimization Strategy (ECMS), we introduce an adjustable target variable for the final state as an input to the NN-based strategy. During training, classification and regression networks with single-step and multi-step inputs are considered. An efficient shooting method and an adaptive method are then introduced to realize the precise control of the final state and online parameter adaptation. Simulations of the proposed method and the benchmarking method are carried out in different battery discharge modes. Results demonstrate that the proposed shooting neural classifier can achieve 99.7% fuel optimality of dynamic programming in a similar computational time to the shooting ECMS, and the proposed adaptive neural classifier can adapt to different driving conditions and has better fuel economy than the adaptive ECMS.

Optimal energy management strategy for dual-power coupling tractor based on the adaptive control technology.

Hybrid tractors (HT) are regarded as the efficient agricultural machine due to their energy conservation performance and faster torque response to deal with load fluctuations. However, the strategy to allocate the battery and fuel energy for demand power should be discussed. In this paper, an on-line management strategy of the HT is proposed to optimize the energy consumption of engine and motor and to reduce torque ripple for power units. A new architecture for replacing power shift and continuously variable transmission technology is proposed. Then, the modified equivalent consumption minimization strategy (ECMS) is used to optimize the torque distribution in which the equivalent factor is further calculated for the real-time process. Besides, the modification of ECMS in variable working conditions can effectively analyse the torque distribution between the motor and engine. The numerical test is implemented that the effectiveness of the proposed energy strategy is validated in plowing conditions. The consequences indicated that the proposed power distribution strategy can adaptively allocate the torque demand according to the fluctuation load. Comparing with the traditional rule-based strategy, the proposed strategy can reduce 6.2% of the energy, and decrease torque ripple with the proposed tractor architecture.

Predictive cruise control for hybrid electric vehicles based on hierarchical convex optimization

Predictive Cruise Control (PCC) achieves automated driving with improved vehicle economy by incorporating road gradient information from high-precision maps. However, existing research on PCC has primarily centered around conventional gasoline vehicles. Hybrid Electric Vehicle (HEV) offer superior fuel efficiency compared to their traditional counterparts. To further enhance vehicle economy by integrating the PCC system with HEVs, this paper proposes a hierarchical control architecture for HEV-PCC based on convex optimization. Firstly, considering spatial slope information from the road ahead, this paper introduces a Time-Space Integration Model Predictive Control (TSI-MPC) based on the principles of kinetic energy. Secondly, the paper combines a current regression model with the Pontryagin Minimum Principle (PMP) to present a Regression Analysis - Pontryagin Minimum Principle (RA-PMP) approach for the direct analytical solution of the energy management problem. Finally, the strategy is validated through simulation analysis and Hardware-in-Loop (HIL) testing on various real road scenarios. The result indicate that, in comparison to the Equivalent Consumption Minimization Strategy (ECMS) with constant speed cruising (CC), this method achieves an average fuel economy improvement of 9.45%.

Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle

Energy management strategies play an essential role in improving fuel economy and extending battery lifetime for fuel cell hybrid electric vehicles. However, the traditional energy management strategy ignores the lifetime of the battery for good fuel economy. To overcome this drawback, a battery longevity-conscious energy management predictive control strategy is proposed based on the deep reinforcement learning algorithm predictive equivalent consumption minimization strategy (DRL-PECMS) in this study. To begin with, the back-propagation neural network is devised for predicting demand power, and the predictive equivalent consumption minimum strategy (PECMS) is proposed to improve the hydrogen consumption. Then, in order to improve the battery durability performance, the deep reinforcement learning algorithm is utilized to optimize the battery power and improve battery lifetime. Finally, numerical verification and hard-ware in the loop experiments are conducted to validate hydrogen consumption and battery durability performance of the proposed strategy. The validation results show that, compared with CD/CS and SQP(Sequential Quadratic Programming), the PECMS combined can achieve better fuel economy with the fuel consumption reduction by 55.6 % and 5.27 %, which effectively improves the fuel economy. The DRL-PECMS can reduce the effective Ah-throughput by 3.1 % compared with the PECMS. The numerous validations and comparisons demonstrate that the proposed strategy effectively accomplishes the trade-off optimization between energy consumption and battery durability performance.

Energy management algorithm based on average power demand prediction for plug-in hybrid electric trucks

This paper examines potential fuel consumption savings through the implementation of a predictive equivalent consumption minimisation strategy, as a control algorithm in the energy management system of hybrid heavy-duty vehicles. A plug-in hybrid electric truck simulation model was developed as a demonstrator and was validated with experimental data. The baseline vehicle model was based on the same algorithm as VECTO, which is further extended in the current study by novel factors that contain the average predicted power demand. The potential benefits of the proposed algorithm are examined for different heavy-duty categories over regulatory mission profiles in both charge-sustain and charge-depletion modes. The results show that the fuel consumption can be reduced by up to 5.9 % and 4.4% in charge sustaining and charge depleting modes, respectively, depending on the mission profile. If such an algorithm is adopted by commercial vehicles, this could lead to substantial CO2 and fuel consumption savings on the road.

Improvised energy management control through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in non-plugin parallel hybrid electric vehicle

This work proposed to integrate intelligent techniques in an adaptive equivalent consumption minimization strategy (AECMS) for a robust equivalence factor correction based on the demand torque and battery state of charge (SOC). The two intelligent approaches of fuzzy logic and genetic algorithmically optimized adaptive neuro-fuzzy inference system (ANFIS) have been employed. The entire performance is validated using the prepared three hybrid standard driving cycles and also for training the fuzzy inference system (FIS). The proposed ANFIS-ECMS delivers a narrow battery charging and discharging profile within the optimal battery utilization zone. Also, it delivers the higher fuel economy for the three driving cycles in the D1 4.20%, D2 1.175%, and D3 0.90% improvisation is occurred compared to fuzzy-PI AECMS. Even in the reliability assessment of ANFIS-AECMS, the performance has been tested on the self-developed real-world driving cycle. The fuel economy increases by 1.26% and 0.80% are compared to rule-based and fuzzy-PI AECMS. The entire results of this work evidence that the proposed strategy achieves significant improvement in battery and fuel energy utilization and also the reduction in emissions compared with rule-based, conventional fixed PI ECMS and fuzzy-PI based ECMS.

Bi‐objective Trade‐Off Optimization Control Strategy Based on the Equivalent Consumption Minimization Strategy–Pareto Algorithm for a Multimode Hybrid Electric Vehicle

Considering the trade‐off on the improvement of fuel economy performance and mode transition comfort for multimode hybrid electric vehicles (MMHEV), a bi‐objective trade‐off control strategy is designed based on the equivalent consumption minimization strategy (ECMS) with Pareto method. Aiming at the problem of optimal economy of multimode working area of vehicles, the equivalent fuel consumption cost and torque distribution MAP in different modes are determined based on ECMS. In order to solve the problem of torque instability caused by engine response lag and demand torque fluctuation, the motor torque optimization coefficient (MTOC) is introduced as the control variable by taking advantage of the motor's fast and accurate response to torque, and the genetic algorithm is used to optimize the MTOC, and a smoother torque distribution is obtained. Because there is a coupling relationship between the economy and ride comfort of the system, the trade‐off optimization is carried out using the NSGA‐II algorithm based on Pareto principle. The simulation verification conducted in nonslope and slope conditions, as well as hardware‐in‐the‐loop experiments, demonstrates the effectiveness of the proposed control strategy in managing the trade‐off between economy and ride comfort for the MMHEV.

Reinforcement Learning‐Based Co‐Optimization of Adaptive Cruise Speed Control and Energy Management for Fuel Cell Vehicles

With the development of intelligent autodriving vehicles, the co‐optimization of speed control and energy management under the insurance of safe and comfortable driving has become a vital issue. Herein, the adaptive cruise control scenario is discussed. A co‐optimization method for speed control and energy management for fuel cell vehicles is suggested to delay the degradation of energy sources while preserving fuel cell efficiency. A reward function based on a reinforcement learning (RL) algorithm is developed to optimize the safety coefficient, comfortability, car‐following efficiency, and economy at the speed control level. The RL agent learns to control vehicle speed while avoiding collisions and maximizing the cumulative rewards. To handle the problem of energy management, an adaptive equivalent consumption minimization strategy, which takes into account the deterioration of energy sources, is implemented at the energy management level. The results indicate that the suggested method reduces the demand power by 1.7%, increases the lifetime of power sources, and reduces equivalent hydrogen consumption by 9.4% compared to the model predictive control.

A novel multi-stack fuel cell hybrid system energy management strategy for improving the fuel cell durability of the hydrogen electric Multiple Units

ABSTRACT To improve the fuel cell durability of the hydrogen Electric Multiple Units, this paper proposes a novel multi-stack fuel cell hybrid system energy management strategy in consideration of fuel cell degradation. The top layer of the method distributes the power of fuel cells and lithium batteries reasonably according to the Equivalent Consumption Minimization Strategy, and adjusts the fuel cell power through the feedback power regulation module. The bottom layer distributes the power of different stacks according to the degradation degree. The proposed layering method improves the durability of fuel cells by reducing the fuel cell degradation degree. The hardware-in-the-loop (HIL) experiments results show that, compared with the traditional equivalent hydrogen consumption minimum energy management strategy, the proposed method is effective in lowering the degradation degree and operating pressure of the fuel cell by 25.77% and 35.73% respectively.

Investigation and Optimization of Energy Consumption for Hybrid Hydraulic Excavator with an Innovative Powertrain

This paper presents an innovative powertrain design and an energy regeneration system for hybrid hydraulic excavators to reduce energy consumption and emissions. The proposed system is designed to maximize engine efficiency and make full use of the energy gained from boom and arm retraction. The powertrain features an innovative design that incorporates a continuously variable transmission (CVT), which drives the main pump. It enables precise control of both the engine’s speed and torque, ensuring that the engine operates within the high-efficiency range. The energy regeneration system is applied to regenerate the potential energy of the boom and arm, which can be used to either charge the battery or directly supply power to the main pump. Moreover, an energy management strategy based on an equivalent consumption minimization strategy is used to distribute the power while offering maximum engine efficiency. When compared with the existing hybrid system and conventional system, the simulation results indicated that the proposed approach achieves energy-saving efficiencies of 16.9% and 77.1%, respectively, at high velocities and 22.25% and 53.5%, respectively, at medium velocities. This research signifies a promising advancement for sustainable and efficient hydraulic excavator operations.

Predictive hierarchical eco-driving control involving speed planning and energy management for connected plug-in hybrid electric vehicles

The connected vehicle technique has offered great opportunities to improve further plug-in hybrid electric vehicles (PHEVs) fuel economy. In this context, a predictive hierarchical eco-driving control scheme is proposed for connected PHEVs under a car-following scenario containing a cloud-layer speed planner and vehicle-layer energy management. In the cloud layer, based on the traffic data separately processed by the dynamic programming (DP) algorithm and k-means clustering method, the reference state of charge (SOC) model, the energy consumption model, the equivalent factor model and the variable-horizon speed predictor can be constructed, respectively. And for ensuring safety, comfort and fuel economy in the car-following process, the energy and SOC models are used separately as the index and state equation in the data-driven speed planning problem. Then, with the driving pattern recognition technique, the power allocation under the planned speed can be conducted by integrating the adaptive equivalent consumption minimization strategy (ECMS) with the model predictive control (MPC), thus guaranteeing fuel economy, adaptability and near-global optimality with high computational efficiency. Compared with other strategies, the effectiveness and advantages of the proposed scheme are validated in the joint simulation platform of MATLAB/Simulink and GT-SUITE.

Cuboid equivalent consumption minimization strategy for energy management of multi-mode plug-in hybrid vehicles considering diverse time scale objectives

As a result of global actions for decarbonization, the rapid development of electrified vehicles (EV), including plug-in hybrid vehicles (PHEV) leads to increasing demands for maximizing battery useful life since recycling EV batteries would bring new environmental issues. PHEVs can have a higher fuel economy while maintaining the battery state-of-charge (SoC) through the equivalent consumption minimization strategy (ECMS); however, it is still a great challenge to maximize battery life through PHEV control since it is hard to balance diverse time scale control objectives (fuel economy, SoC control and the battery life). To this end, this paper proposes a Cuboid Equivalent Consumption Minimization Strategy (C-ECMS) for multimode PHEVs. A new concept of the “Hamiltonian matrix” is introduced by adding an additional control degree-of-freedom to the conventional Hamiltonian vector to enable optimal dual motor control in the multi-mode PHEVs. Then, an aging factor (AF) is introduced in associated with the equivalent factor (EF) to generate three Hamiltonian matrices that establish a cuboid knowledge base for the optimal control considering diverse time scale objectives. Experiments under five different driving cycles are conducted to study 1) the impact of Hamiltonian matrix dimensions on SoC control accuracy and 2) the impact of EF and AF settings on the Pareto Frontier considering fuel economy, SoC accuracy, and battery aging. The unified setting for C-ECMS is obtained based on the experimental study, and the result is demonstrated by a comparison study with the rule-based strategy and the standard ECMS implemented in the same PHEV. The results show that the proposed C-ECMS outperforms the standard ECMS control and achieves more accurate SoC sustaining (0.4% of SoC error), less fuel consumption (8.4% improvement), and less battery capacity loss (10.4% improvement).

An improved A-ECMS energy management for plug-in hybrid electric vehicles considering transient characteristics of engine

The plug-in hybrid electric vehicles (PHEVs) are playing an increasingly important role in urban public transportation systems for their unique potential for energy saving and emission reduction. However, as an enabling technology for the cost-efficient operation of PHEVs, the current energy management strategies (EMSs) rarely consider the transient characteristics of the engine, especially the limit of engine transient performance and the extra fuel consumption due to engine state changes. To improve the energy-saving effect of EMSs, an optimization study on energy management considering engine transient characteristics for PHEV is carried out in this paper. Firstly, a high-precision transient fuel consumption model is established based on artificial neural network (ANN) to accurately evaluate the real fuel consumption of engine under steady and unsteady states. Secondly, an adaptive equivalent consumption minimization strategy (A-ECMS) is constructed for PHEV, and the engine transient performance boundary is defined in the strategy to avoid unreasonable engine power surge decision. Thirdly, the transient fuel consumption model is incorporated into the equivalent fuel consumption cost function of A-ECMS to fully consider the impact of engine transient fuel consumption on the real-time power allocation of PHEV. The results show that the improved strategy weakens the state fluctuation of the engine, and makes the engine run more smoothly, resulting in a 99.16% reduction in the extra fuel consumption due to engine state changes. Finally, the fuel economy of the PHEV under the combined driving cycle based on the C-WTVC improved by 3.37%.

A GRNN informed ECMS-ALPF operational management strategy for reducing fuel consumption of diesel/battery/supercapacitor hybrid vehicles

The fuel consumption and pollution emission of ships have become the main focuses in ocean environment management. This paper proposes a new marine vehicular hybrid propulsion system (VHPS), which is composed of a diesel generator and a hybrid energy storage system consisted of batteries and supercapacitors. However, the distinct power and energy characteristics of the three power sources result in complex operational management and a negative impact on energy conservation and emission reduction. Therefore, a general regression neural network informed equivalent consumption minimization strategy and adaptive low pass filter (GRNN informed ECMS-ALPF) operational management strategy (OMS) is proposed to optimize the fuel consumption and energy loss. Finally, a hardware in the loop experimental platform is utilized to verify the performance of proposed OMS. From experimental results, the GRNN informed ECMS-ALPF strategy has accurate load predictive capability, the predictive average root mean square error value is only 2.1633 kW, whose fuel consumption is 14.9% lower compared with a conventional rule-based strategy. And the energy loss of the given hybrid energy storage system can reduce 0.0178 kWh (about 14.55%) compared with a conventional low pass filter strategy. The proposed VHPS and OMS offer insights for the study of ocean and coastal management.

An Adaptive ECMS Based on Traffic Information for Plug-in Hybrid Electric Buses

Nowadays, a growing interest is in improving the efficiency of a parallel hybrid electric bus (PHEB). This paper proposes an adaptive energy management strategy based on an equivalent consumption minimization strategy (ECMS) by using real-time traffic information described by the average speed, average acceleration, and standard deviation of speed for different road sections. The whole adaptive energy management system consists of offline and online components. Three velocity characteristic parameters are derived by utilizing the data from the vehicle. Firstly, the road segments are classified through the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means. Then three Markov transition matrices on different types of roads are constructed with the velocity feature parameters which can be used to predict the speed in the following time. On the other hand, the online component implements the energy management strategy (EMS) involved. The EMS incorporates predicted vehicle speed and road information into the ECMS. Based on this, the predicted vehicle speed and traffic information are applied to make adaptive changes to the equivalent factors in the ECMS. Finally, the performance of the proposed strategy is evaluated through a comparative simulation study and the results show the superiority of the approach in improving fuel economy. Comparative outcomes from HIL tests additionally illustrate that the proposed strategy's fuel efficiency is compatible with the original design intent.

Fuzzy-based optimal energy management strategy of series hybrid-electric propulsion system for UAVs

Hybrid-electric propulsion system (HEPS) has gained significant attention in unmanned aerial vehicles (UAVs) due to its potential to significantly reduce fuel consumption and pollutant emissions. Effective energy management strategies are crucial in ensuring the efficient operation of HEPS in UAVs, as they involve multiple power and energy sources. While conventional equivalent consumption minimum strategy (ECMS) is effective in distributing engine and battery output power, it struggles to maintain the battery’s state of charge (SOC) within desired levels in case of engine failure. The present study introduced the fuzzy logic control-equivalent consumption minimum strategy (FLC-ECMS), a fuzzy-based composite energy management strategy that incorporates the robustness and adaptability of fuzzy logic control. Through simulation tests under general aviation cruise flight and cargo transport flight scenarios, the results indicated that hybrid UAV equipped with the HEPS can decrease fuel consumption and CO2 emissions by at least 18.6% and NOx emissions by 10.1% in the first profile and by 23.6% and 11.2% in the second profile, compared to conventional oil-powered UAV. The FLC-ECMS method not only reduces fuel consumption and emissions, but also maintains the battery SOC within permitted range and minimizes the difference between front and back SOC. This study provides new insights into the optimal energy management of HEPS for complex profiles in UAVs, highlighting the significance of UAVs in reducing environmental impacts, and has the potential for broader application in other electric propulsion systems.

Lifetime-Optimized Energy Management Strategy for Fuel Cell Unmanned Aircraft Vehicle Hybrid Power System

To improve the durability of the fuel cell unmanned aircraft vehicle (UAV) hybrid power system, a model predictive control based energy management strategy (EMS) with minimum energy loss is presented in this paper. The proposed algorithm can effectively reduce the internal loss and power stress of the hybrid power system, which can help to extend fuel cell lifespan. Specifically, a calculation formula with observable parameters is proposed to predict the internal loss of the battery in both the charging and discharging mode. Besides, to increase the hybrid UAV adaptability under complex flying conditions, the fuel cell operating trajectory is also defined in the algorithm to increase the system dynamic performance and efficiency. To verify the effectiveness of the strategy, the hardware-in-the-loop (HIL) experimental test platform is built. Compared to the conventional equivalent consumption minimum strategy (ECMS), the proposed strategy can effectively alleviate the voltage degradation of the fuel cell and decrease the energy loss of the battery. The proposed strategy can help to contribute to the applications of fuel cell hybrid power system in the aviation fields.