Real-time Energy Management Strategy Research Articles

Real-time cost-minimization power-allocating strategy via model predictive control for fuel cell hybrid electric vehicles

Fuel cell electric vehicles are widely deemed as the promising technology in sustainable transportation field, yet the high ownership cost makes them far from competitive in contemporary auto market. To maximize the economic potential of fuel cell/battery-based hybrid electric vehicles, this paper proposes a real-time cost-minimization energy management strategy to mitigate the vehicle’s operating cost. Specifically, the proposed strategy is realized via model predictive control, wherein both hydrogen consumption and energy source degradations are incorporated in the multi-objective cost function. Assisted by the forecasted speed, dynamic programming is leveraged to derive the optimal power-splitting decision over each receding horizon. Thereafter, the performance discrepancy of the proposed strategy is analyzed under different affecting factors, including battery state-of-charge regulation coefficient, discrete resolution of optimization solver, speed prediction approaches and length of prediction horizon. Lastly, a comparative study is conducted to validate the effectiveness of the proposed strategy, where the proposed strategy can respectively reduce the operating cost and prolong the fuel cell lifetime by 14.17% and 8.48% in average versus a rule-based benchmark. Moreover, the online computation time per step of the proposed strategy is averaged at 266.26 ms, less than the sampling time interval 1 s, thereby verifying its real-time practicality.

Real-Time Energy Management Strategy Based on Driver-Action-Impact MPC for Series Hybrid Electric Vehicles

Precise prediction of future vehicle information can improve the control efficiency of hybrid electric vehicles. Nowadays, most prediction models use previous information of vehicles to predict future driving velocity, which cannot reflect the impact of the driver and the environment. In this paper, a real-time energy management strategy (EMS) based on driver-action-impact MPC is proposed for series hybrid electric vehicles. The proposed EMS consists of two modules: the velocity prediction module and the real-time MPC module. In the velocity prediction module, a long short-term memory (LSTM) neural network model, which is trained by the traffic data derived from a VR-based driving simulator, is adopted to predict the future driving information by using driver action information and current vehicle’s velocity. The obtained future driving velocity is treated as the inputs of the real-time MPC module, which outputs the control variables to act on the underlying controllers of power components by solving a standard quadratic programming (QP) problem. Compared with the rule-based strategy, a 5.6% average reduction of fuel consumption is obtained. The effectiveness of real-time computation of the EMS is validated and verified through a hardware-in-the-loop test platform.

Adaptive real-time optimal energy management strategy based on equivalent factors optimization for hybrid fuel cell system

Fuel cell, a new kind of energy supply equipment, has several advantages such as high efficiency, low noise, and no emission. Proton exchange membrane fuel cell (PEMFC) is considered to have the potential to take the place of the conventional engine on unmanned underwater vehicle (UUV). Besides the power sources in the hybrid power system, the energy management system (EMS) is crucial to operating performance. In this paper, an on-line adaptive equivalent hydrogen consumption minimization strategy (ECMS) is proposed to solve the problem of prior knowledge demand and poor adaptability of current energy management algorithms. In this presented method, a battery state of charge (SOC) constituted penalty term is designed to calculate the equivalent factor (EF), and then the equivalent factor obtained by optimization is substituted into the original objective equation to realize the real-time energy regulation. In this paper, a typical UUV load curve is used to verify the control effect under different working conditions, and the performance is compared with three conventional algorithms’. Simulation results show that the hydrogen consumption of proposed algorithm is close to the optimal solution obtained in offline environment, and it is reduced by more than 3.79% compared with the traditional online methods.

Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach

Significant dependence on fossil fuels and freshwater shortage are common problems in remote and arid regions. In this context, the operation of a wind-solar-diesel-battery-reverse osmosis hybrid energy system has become a suitable option to solve this problem. However, owing to the uncertainties of renewable energy availability and load demand, it is a challenge for operators to develop an energy management scheme for such a system. This study aims to determine a real-time dynamic energy management strategy considering the uncertainties of the system. To this end, the energy management of a hybrid energy system is presented as an optimal control objective, and multi-targets are considered along with constraints. The information entropy theory is introduced to calculate the weight factor for the trade-off between different targets. Then, a deep reinforcement learning algorithm is adopted to solve this problem and obtain the optimal control policy. Finally, the proposed method is applied to a typical hybrid energy system, and numerous data are applied to train an agent to obtain the optimal energy management policy. Simulation results demonstrate that a well-trained agent can provide a better control policy and reduce costs by up to 14.17% in comparison with other methods.

Cloud computing-based real-time global optimization of battery aging and energy consumption for plug-in hybrid electric vehicles

This paper addresses two conflicts in the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). One is the conflict between fuel economy optimization and battery state of health preservation, and the other is the conflict between global optimality and real-time capability. Inspired by the hierarchy structure of a computer, a two-layer internet-distributed EMS (ID-EMS) is developed using cloud computing and the internet of vehicles. The top layer in the cloud, which possesses powerful calculating capability, focuses on global optimality by utilizing machine learning technology and stochastic dynamic programming. The bottom layer on board, with limited computing power, employs a fuzzy controller to respond to real-time conditions while trying not to deviate too far from the global solution. Thus, a real-time global optimal EMS can be achieved. The ID-EMS is implemented on an internet-distributed vehicle-in-the-loop simulation platform whose in-loop vehicle makes it possible to test the ID-EMS in an on-road driving experiment. The ID-EMS outperforms a rule-based EMS in terms of overall cost by 6.8%, but it is surpassed by an acausal EMS with dynamic programming by 7%. These results suggest directions for the future development of EMS for PHEVs using cloud computing.

Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses

The Plug-in hybrid vehicle (PHEV) has been progressively penetrated in the urban public transport system and seen a foreseeable fast growth in the future. Within this horizon, energy management is an enabling technique for the cost-efficient operation of the PHEV. In this paper, a model predictive control (MPC)-based real-time energy management strategy (EMS) combining a cloud-enabled velocity profile optimizer (VPO) and vehicle-side velocity predictor is proposed for the Plug-in hybrid bus (PHEB) under the intelligent transportation systems (ITS). Particularly, the velocity profile and the state of charge (SOC) sequences are optimized by incorporating the genetic algorithm (GA) with the dynamic programming (DP), giving rise to a novel GA-DP-based VPO. In the case that the vehicle can be hardly decoupled from the traffic flow, a multi-feature predictor based on Long Short Term Memory (LSTM) Network is triggered to replace the cloud-enabled VPO to predict the short-term velocity. Results show that the prediction accuracy can be improved by 5.4% by employing the multi-feature training. The equivalent fuel consumption with the mode-switching EMS in the optimized UDDS cycle can be reduced by 14.9% compared with the state of the art. The proposed strategy is validated with a real-time performance by performing the hardware in the loop (HIL) experiment.

Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation

This paper proposes a real-time predictive energy management strategy (PEMS) of plug-in hybrid electric vehicles for coordination control of fuel economy and battery lifetime, including velocity predictor, state-of-charge (SOC) reference generator, and online optimization. In velocity predictor, the radial basis function neural network algorithm is adopted to accurately estimate the future drive velocity. Based on predictive velocity and current driven distance, the SOC reference in predictive horizon can be determined online by reference generator. To coordinate fuel consumption and battery degradation, a model predictive control problem of cost minimization including fuel consumption cost, electricity cost of battery charging/discharging, and equivalent cost of battery degradation, is formulated. To mitigate the huge calculation burden in optimization, the continuation/generalized minimal residual (C/GMRES) algorithm is delegated to find the expected engine power command in real time. Since original C/GMRES algorithm cannot directly handle inequality constraints, the external penalty method is employed to meet physical inequality limits of powertrain. Numerical simulations are carried out and yield the desirable performance of the proposed PEMS in fuel consumption minimization and battery aging restriction. More importantly, the proposed C/GMRES algorithm shows great solving quality and real-time applicability in PEMS by comparing with sequence quadratic programming and genetic algorithms.

A real-time energy management strategy for pumped hydro storage systems in farmhouses

This paper proposes a real-time energy management strategy for pumped hydro storage systems in farmhouses to manage surplus renewable energy. The proposed system meets both electricity and water demand in a farm. The novelty of this paper is its combination of a scheduling method and a real-time controller to take into account both present and future conditions of the microgrid. The scheduling part determines irrigation times, required stored water, and pumped hydro storage schedule. The real-time controller receives the schedule and current condition of the microgrid in order to adjust the pump power and turbine flow rate efficiently. Two methods of fuzzy logic and artificial neural network are tested to investigate which can address the forecast error problem more economically. An innovative approach is presented to produce target data for artificial neural network training. The designed system is simulated for 365 days to investigate the effect of real-time management on the performance of the microgrid on both sunny and cloudy days. The proposed energy management system is applied in an experimental setup, tested with a real pump and turbine. Results show that a real-time management system could keep the stored water level the same as the scheduling method; however, the pump and turbine can be controlled more cost-effectively. Finally, an economic study is conducted to determine the payback period of the system.

Energy Management of a Multi-Source Vehicle by λ-Control

This paper deals with the real-time energy management of a fuel cell/battery/supercapacitors energy storage system for electric vehicles. The association of the battery and the supercapacitors with the fuel cell aims to reduce the hydrogen consumption while limiting the constraints on the fuel cell and the battery. In this paper, a real-time optimization-based energy management strategy by λ-control is proposed. Simulation results on a standard driving cycle show that the hydrogen consumption is reduced by 7% in comparison with a fuel-cell-based electric vehicle without any secondary energy storage source. Moreover, the energy management strategy ensures the system safety while preserving the fuel cell and the battery. Experimental results show that the developed energy management strategy is well-suited for the real-time requirements, applicability, and safety.

A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method

The universal adaptive equivalent consumption minimization strategy (A-ECMS) has the potential of being implemented in real-time for plug-in hybrid electric vehicles (PHEVs). However, the imprecise prediction of a long-term future driving cycle and biggish computation burdens remain the barriers for further real vehicle application. Thus, it is of great significance to develop a real-time optimal energy management strategy for PHEVs by weakening the influence of future driving cycle to the control accuracy and improving its computation efficiency. In this paper, a novel real-time energy management strategy for PHEVs based on equivalence factor (EF) dynamic optimization method is proposed. Firstly, a novel proportional plus integral adaption law for calculating the dynamic optimal EF is established for A-ECMS using only instantaneous information of current vehicle speed and battery state of charge. Second, three key coefficients are obtained and converted into a three-dimensional look up tables, so as to determine the dynamic optimal EF. Finally, the method of fast searching the optimal engine torque is proposed, which significantly enhances the computational efficiency. Compared with A-ECMS, the computational time of A-ECMS2 is decreased near 94.8% and the deviation of fuel consumption is controlled within 4.4%. Both the numerical results and hardware-in-loop results prove that the proposed novel energy management strategy A-ECMS2 has better real-time performance and less computing burden than the general A-ECMS.

A comparison of real-time energy management strategies of FC/SC hybrid power source: Statistical analysis using random cycles

This paper presents a comparative study of two promising real-time energy management strategies for fuel cell electric vehicle applications: adaptive equivalent consumption minimization strategy (A-ECMS), and stochastic dynamic programming (SDP). An off-line algorithm –classic dynamic programming - provides reference results. On-line and off-line strategies are tested both in simulation and using a dedicated test bench completely consistent with an electric scooter powertrain.The hybrid power source combines a fuel cell, a supercapacitor pack and two related power converters. The system model is carefully calibrated using experimental data. This allows meaningful identification of parameters of the various strategies. The model data is determined using a motorcycle certification driving cycle.The robustness of each strategy is then analyzed using a large number of random driving cycles. Experimental and simulation results show that a specific SDP approach, based on Markov chain modeling, has the best overall performance in real-world driving conditions. It achieves the minimum average hydrogen consumption while respecting the state-sustaining constraint. Conversely, the A-ECMS results lack robustness and show poor performance indexes when facing unknown real world power demand profile. In conclusion, the present results indicate SDP is an interesting approach for future hybrid source energy allocation.

Model-Free Real-Time Autonomous Control for a Residential Multi-Energy System Using Deep Reinforcement Learning

Multi-energy systems (MES) are attracting increasing attention driven by its potential to offer significant flexibility in future smart grids. At the residential level, the roll-out of smart meters and rapid deployment of smart energy devices call for autonomous multi-energy management systems which can exploit real-time information to optimally schedule the usage of different devices with the aim of minimizing end-users' energy costs. This paper proposes a novel real-time autonomous energy management strategy for a residential MES using a model-free deep reinforcement learning (DRL) based approach, combining state-of-the-art deep deterministic policy gradient (DDPG) method with an innovative prioritized experience replay strategy. This approach is tailored to align with the nature of the problem by posing it in multi-dimensional continuous state and action spaces, facilitating more cost-effective control strategies to be devised. The superior performance of the proposed approach in reducing end-user's energy cost while coping with the MES uncertainties is demonstrated by comparing it against state-of-the-art DRL methods as well as conventional stochastic programming and robust optimization methods in numerous case studies in a real-world scenario.

Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability

Abstract This paper presents a real-time and approximately optimal energy management strategy (EMS) based on Pontryagin’s minimum principle (PMP), considering both fuel economy and power source durability. To develop the target strategy, performance degradation models are built for two power sources: a proton exchange membrane fuel cell and a lithium ion battery. This study provides an online co-state updating method for uncertain driving cycles. The method allows the battery’s state of charge to be controlled within a certain range and determines the nearly optimal hydrogen consumption. Furthermore, by incorporating a fuel cell power variation limiting factor with a weight coefficient into the PMP to suppress power changes, the durability of the fuel cell can be improved. The average daily operating cost (calculated based on the fuel consumption and power source degradation) is used to evaluate the trade-off between fuel economy and power source durability. Simulation results show that the fuel cell durability is improved with a slight increase in fuel consumption and battery degradation, and that the average daily operating cost is effectively reduced. A comparison with the results obtained by adopting a rule-based EMS and a dynamic programming-based EMS indicate the superiority of the proposed EMS.

Coordinated management of connected plug-in hybrid electric buses for energy saving, inter-vehicle safety, and battery health

For plug-in hybrid electric vehicles, deeper battery discharge provides more electrical energy at a lower cost than fossil fuel, which reduces the overall energy consumption cost. However, it also accelerates battery degradation and increases the equivalent cost of battery life loss. Therefore, the battery depth of discharge (DOD) needs to be optimized to minimize the overall cost. For connected plug-in hybrid electric vehicles, the longitudinal velocity planning determines the energy demands, which directly affects the charging or discharging current to the battery and therefore affects DOD, aging, and fuel consumption as well. For connected plug-in hybrid electric buses running on fixed routes, in order to coordinate the velocity planning and battery health protection, this paper proposes a real-time energy management strategy aimed at achieving the minimum overall cost by optimizing the DOD and velocity planning. The proposed method is evaluated in an urban traffic scenario, and the goal of achieving optimal DOD is divided into a co-optimization problem over each moving horizon, where the velocity planning and energy management are traded off by minimizing the sum of driving safety cost, energy consumption cost, and equivalent cost of battery life loss. The results show that the proposed far-sighted economy-oriented methodology is superior to a short-sighted velocity planning and energy management method, and has an obvious advantage in the total cost compared with other conventional methods using a preset DOD. Moreover, the impacts of possible communication delays and prediction horizon lengths on the optimization performance and computational cost are investigated. The proposed method provides a promising management strategy for future connected and autonomous mobility design, which can mitigate battery capacity degradation and improve the fuel economy.

Research on Multi-Scenario Variable Parameter Energy Management Strategy of Rural Community Microgrid

Many rural communities in western China use renewable energy-based clean energy supply methods, and the community microgrid system of “photovoltaic + energy storage + electric heating” has been widely used. However, the energy management effect of such a typical rural community microgrid system is not very satisfactory. Aiming at the problem of the comprehensive economic operation of the microgrid system in rural communities, a method for optimizing the operation strategy parameters of typical operating scenarios is proposed, and critical parameters of the real-time energy management strategy of the microgrid system are optimized. Through the evaluation of the annual time-scale simulation results, it is proved that the proposed strategy can improve the comprehensive economic benefits of the operation of the rural community microgrid system (comprehensive economic benefits include electricity bills and energy storage system (ESS) life loss). Relying on the actual community microgrid demonstration project system in western China, a community-level microgrid energy management monitoring system is built. The control strategy proposed in this paper is applied to the demonstration system for experimental verification. The operational data of the demonstration system shows that the proposed method is feasible and effective.

Optimal energy management with balanced fuel economy and battery life for large hybrid electric mining truck

With the addition of an energy storage system (ESS) and advanced controls, a hybrid electric propulsion system can considerably improve the fuel economy over a pure mechanical powertrain. However, the high cost and relatively short operating life of the battery ESS constitute a significant portion of the total operation cost (TOC) of an electrified vehicle, particularly for heavy-duty vehicles with a larger ESS. In this work, a new method for generating the optimal energy management strategy (EMS), considering the TOC of a hybrid electric mining truck (HEMT), is introduced. The cost associated with battery performance degradation and operation life-shortening under different battery use patterns is added to form the globally optimal, TOC-based EMS. The optimal EMS under different vehicle operation profiles are identified using dynamic programming (DP) to serve as benchmarks. An intelligent optimal ESS energy management method for achieving the minimum TOC during real-time, open-pit HEMT operations is introduced by combining an artificial neural network (ANN) model and a fuzzy-logic controller (FLC). The new, real-time intelligent optimal EMS led to twenty-one percent TOC reduction of the HEMT over the traditional, pure fuel economy-oriented optimal EMS, and formed the foundation of TOC-based, optimal EMS development for hybrid electric vehicles (HEVs).

Adaptive real-time optimal energy management strategy for extender range electric vehicle

The extender range electric vehicle (EREV) is an effective way to solve the “mileage anxiety” of pure electric vehicles, and the fuel economy of EREV is the key point of energy optimization. This paper designed an adaptive real-time optimal energy management strategy for EREV. Firstly, an improved shooting algorithm is proposed, which can determine the range of the equivalent factor (EF) according to the power configuration parameters of the vehicle, and then the secant method is used to quickly calculate the initial value of the EF. Secondly, from the perspective of energy flow, the intrinsic operation mechanism of equivalent consumption minimization strategy (ECMS) control strategy is revealed, and the working relationship between the five working modes of EREV is clarified. Thirdly, based on the car navigation and geographic location information system, the EF is periodically updated to achieve effective maintenance of the battery state of charge (SOC), so as to obtain the optimal power allocation. Finally, The fuel economy and real-time performance of the proposed energy management strategy are simulated and compared. To verify fuel economy, the rule-based control strategy and the power following control strategy were used as comparison. The results show that the proposed control strategy has better fuel economy and adaptability. To verify real-time performance, the proportional integral derivative ECMS (PID-ECMS) and shooting method ECMS (S-ECMS) were used as comparison. The results show that the proposed strategy is better in both fuel economy and real-time performance.

Real-time Energy Management Strategy for Oil-Electric-Liquid Hybrid System based on Lowest Instantaneous Energy Consumption Cost

For the oil–electric–hydraulic hybrid power system, a logic threshold energy management strategy based on the optimal working curve is proposed, and the optimal working curve in each mode is determined. A genetic algorithm is used to determine the optimal parameters. For driving conditions, a real-time energy management strategy based on the lowest instantaneous energy cost is proposed. For braking conditions and subject to the European Commission for Europe (ECE) regulations, a braking force distribution strategy based on hydraulic pumps/motors and supplemented by motors is proposed. A global optimization energy management strategy is used to evaluate the strategy. Simulation results show that the strategy can achieve the expected control target and save about 32.14% compared with the fuel consumption cost of the original model 100 km 8 L. Under the New European Driving Cycle (NEDC) working conditions, the energy-saving effect of this strategy is close to that of the global optimization energy management strategy and has obvious cost advantages. The system design and control strategy are validated.

Adaptive Equivalent Consumption Minimization Strategy With Rule-Based Gear Selection for the Energy Management of Hybrid Electric Vehicles Equipped With Dual Clutch Transmissions

Based on observations of the behaviour of the optimal solution to the problem of energy management for plug-in hybrid electric vehicles, a novel real-time Energy Management Strategy (EMS) is proposed. In particular, dynamic programming results are used to derive a set of rules aiming at reproducing the optimal gearshift schedule in electric mode while the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) is employed to decide the powertrain operating mode and the current gear when power from the internal combustion engine is needed. In terms of total fuel consumption, simulations show that the proposed approach yields results that are close to the optimal solution and also outperforms those of the A-ECMS, a well-known EMS. One of the main aspects that differentiates the strategy here proposed from previous works is the introduction of a model to use physical considerations to estimate the energy consumption during gearshifts in dual-clutch transmissions. This, together with a series of properly tuned fuel penalties allows the controller to yield results in which there is no gear hunting behaviour.

Real-Time Optimization Energy Management Strategy for Fuel Cell Hybrid Ships Considering Power Sources Degradation

In order to protect the marine environment, the fuel cell has become a good candidate for the main energy source of marine vehicles due to its remarkable performance of high efficiency, quietness, and cleanness. However, the fuel cell of the hybrid power system has a critical drawback of short lifetime. For this reason, the development of the energy management strategy considering power sources degradation becomes significant and urgent. In addition, the power system configuration has a strong link with the control strategy. In this vein, a sizing method of the hybrid energy storage system for a fuel cell hybrid excursion ferry is proposed to prolong the fuel cell lifetime. Based on the configuration of the power system, a real-time optimization control strategy considering efficiency and durability is developed. To verify the control strategy, the mathematical model of the hybrid power system is established. Compared with the wavelet-based control strategy and rule-based control strategy, the proposed strategy has better performance in terms of the fuel economy, the system durability as well as the dynamic property.