Rule-based Energy Management Research Articles

An Electric-Hydrostatic Energy Storage System for Hydraulic Hybrid Wheel Loader

As a typical energy storage in hydraulic hybrid powertrain, the hydraulic accumulator has high power density but low energy density. There are some efforts in improving the energy density of hydraulic energy storage to achieve balanced performance. Therefore in this study an electric-hydrostatic energy storage system is proposed to replace hydraulic accumulator in a hydraulic hybrid wheel loader. Through active control of proposed energy storage, constant system pressure is possible to provide good vehicle drivability. A rule-based energy management strategy without estimating final drive power is proposed. The vehicle performance and fuel consumption with different constant system pressures are studied. Results show the vehicle fuel consumption is reduced by 5.9% with constant system pressure reduced from 33 to 27 MPa. The energy density and power density of proposed energy storage are calculated, showing a much higher energy density and slightly lower power density than gas-charged accumulator.

Energy Management Strategy in 12-Volt Electrical System Based on Deep Reinforcement Learning

The increasing electrification in motor vehicles in recent decades can be attributed to higher comfort and safety demands. Strong steering and braking maneuvers reduce the vehicle’s electrical system voltage, which causes the vehicle electrical system voltage to drop below a critical voltage level. A sophisticated electrical energy management system (EEMS) is needed to coordinate the power flows within a 12-volt electrical system. To prevent the voltage supply from being insufficient for safety-critical consumers in such a case, the power consumption of several comfort consumers can be reduced or switched off completely. Rule-based (RB) energy management strategies are often used for this purpose, as they are easy to implement. However, this approach is subject to the limitation that it is vehicle-model-specific. For this reason, deep reinforcement learning (DRL) is used in the present work, which can intervene in a 12-volt electrical system, regardless of the type of vehicle, to ensure safety functions. A simulation-based study with a comprehensive model of a vehicle electric power system is conducted to show that the DRL-based strategy satisfies the main requirements of an actual vehicle. This method is tested in a simulation environment during driving scenarios that are critical for the system’s voltage stability. Finally, this is compared with the rule-based energy management system using actual vehicle measurements. Concluding measurements reveal that this method is able to increase the voltage at the most critical position of the 12-volt electrical system by approximately 0.6 V.

Taguchi robust design for adaptive energy management of plug-in fuel cell electric bus

This paper presents an adaptive energy management strategy (EMS) based on Pontryagin's Minimum Principle (PMP), considering both historical driving cycles and stochastic vehicle mass. First, an improved battery state of charge (SOC) feedback control method based on Proportion-Integration (PI) controller is proposed for adaptive energy management. Second, a Taguchi robust design (TRD) method is proposed by considering the noises of historical driving cycles and stochastic vehicle mass. The main purpose of the TRD is to find the robust parameters (P and I) that can counter to the above noise disturbance, thereby neglecting the influence of noise disturbance in adaptive energy management control and minimizing the mean hydrogen consumption. The results show that the TRD is reasonable and applicable in the robust design of plug-in fuel cell electric buses (PFCEBs); the fuel economy of the proposed strategy can be averagely improved by 18.36%, compared to the rule-based energy management strategy.

Energy management strategy of hybrid energy system for a multi-lobes hybrid air vehicle

Due to the combination of aerodynamic and buoyant lift, a multi-lobes hybrid air vehicle (HAV) is very suitable for cargo transportation, communications and surveillance in the stratosphere. Solar cells can be used as an ideal energy source for the HAV because of its flat upper surface and sufficient solar radiation in the stratosphere. A hybrid energy system that combines solar cells, fuel cells and lithium batteries is considered to power the HAV in this paper. To better manage the various energy subsystems and achieve optimal flight performance, a power-following management strategy, which is a typical rule-based energy management strategy(EMS), is constructed for the hybrid energy system of the HAV. The EMS takes into account the high specific power of lithium batteries and the high specific energy of fuel cells to improve the endurance of HAV while ensuring the maneuverability during takeoff and landing. The simulation results show that output of each energy subsystem not only meets the power demand of the HAV, but also conforms to output characteristics of each subsystem. Furthermore, the simulation analysis is also carried out to investigate the impact of one of the energy failures on energy management and corresponding strategies are given. The results show the advantages of EMS proposed in this paper, which can provide a reference for the design of HAV.

Energy management strategy for hydraulic hybrid system with compound accumulator based on dynamic programming

The compound accumulator is an energy storage device composed of one large accumulator and one small accumulator. Compared with traditional single-accumulator hydraulic hybrid vehicles, hydraulic hybrid vehicles based on compound accumulator can switch the working timing of large and small accumulators according to the characteristics of different working conditions, more braking energy can be recovered on the premise of providing enough braking strength. This article is aimed at hydraulic hybrid vehicles based on compound accumulator. First, the key components of the hydraulic system were matched, then according to the engine fuel consumption curve, the engine’s high-efficiency work area is divided, and formulated a rule-based energy management strategy based on the principle of adjusting the engine to work in a high-efficiency area. The internal pressure of the compound accumulator is selected as the state variable, the ratio of the torque of the hydraulic pump/motor to the required torque of the vehicle is the control variable, and the optimal fuel economy of the entire cycle is the objective function, and the mathematical model is established using the dynamic programming algorithm, and compiled a program in MATLAB to obtain the optimal torque distribution relationship between the engine and the hydraulic pump/motor and the optimal working timing of the large and small accumulators. Simulation results show that compared with the traditional hydraulic hybrid vehicle with single accumulator based on initial rules and the hydraulic hybrid vehicle with compound accumulator based on initial rules, the hybrid vehicle with compound accumulator based on improved rules can save fuel by 11.84[Formula: see text] and 5.96[Formula: see text] respectively. The fuel saving potential of the compound accumulator structure and the effectiveness of the improved rule control strategy based on dynamic programming algorithm are verified.

Research on the energy management strategy of extended range electric vehicles based on a hybrid energy storage system

Extended range electric vehicles (EREVs) are an effective solution to solve the lack of driving range of pure electric vehicles. Reducing the fuel consumption of EREVs and prolonging the service lifetime of battery play a positive role in solving environmental pollution and energy crisis. In this paper, hybrid energy storage system (HESS) is introduced into EREVs, and two energy management strategies based on HESS are proposed to reduce fuel consumption and prolonging the service life of battery. This paper proposes two energy management strategies for EREVs based on a HESS to reduce fuel consumption and the amount of energy flowing through the battery. HESS adopts the rule-based energy management strategy, whereas Auxiliary power unit (APU) adopts the equivalent fuel consumption minimization strategy (ECMS) or the rule-based energy management strategy as its first and second hierarchical energy management strategies respectively, both of which are embedded into the charge depleting/charge sustaining (CD/CS) control strategy. A simulation model for an EREV is built in AVL Cruise to verify the effectiveness of these strategies. The battery charge and discharge C-rate and the energy consumption of EREVs without HESS under the ECMS and rule-based energy management strategy are then compared. Results show that under the first hierarchical energy management strategy, the average discharge C-rate and cumulative ampere hours of the battery are the lowest, thereby reducing its operating load and extending its service lifetime. Meanwhile, for those EREVs without HESS, using the rule-based energy management strategy can reduce fuel consumption by 11.33%, whereas using ECMS increases fuel consumption by no more than 1.61%. Therefore, the first hierarchical energy management strategy proposed in this study can effectively extend the service lifetime of the battery and reduce the fuel economy of EREVs.

Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria

Hybrid Renewable Energy Sources (HRES) integrated into a microgrid (MG) are a cost-effective and convenient solution to supply energy to off-grid and rural areas in developing countries. This research paper focuses on the optimization of an HRES connected to a stand-alone microgrid system consisting of photovoltaics (PV), wind turbines (WT), batteries (BT), diesel generators (DG), and inverters to meet the energy demand of fifteen residential housing units in the city of Djelfa, Algeria. In this context, the multiobjective salp swarm algorithm (MOSSA), which is among the latest nature-inspired metaheuristic algorithms recently introduced for hybrid microgrid system (HMS) optimization, has been proposed in this paper for solving the optimization of an isolated HRES. The proposed multiobjective optimization problem takes into account the cost of energy (COE) and loss of power supply probability (LPSP) as objective functions. The proposed approach is applied to determine three design variables, which are the nominal power of photovoltaic, the number of wind turbines, and the number of battery autonomy days considering higher reliability and minimum COE. In order to perform the optimum size of HMG, MOSSA is combined with a rule-based energy management strategy (EMS). The role of EMS is the coordination of the energy flow between different system components. The effectiveness of using MOSSA in addressing the optimization issue is investigated by comparing its performance with that of the multiobjective dragonfly algorithm (MODA), multiobjective grasshopper optimization algorithm (MOGOA), and multiobjective ant lion optimizer (MOALO). The MATLAB environment is used to simulate HMS. Simulation results confirm that MOSSA achieves the optimum system size as it contributed 0.255 USD/kW h of COE and LPSP of 27.079% compared to MODA, MOGOA, and MOALO. In addition, the optimization results obtained using the proposed method provided a set of design solutions for the HMS, which will help designers select the optimal solution for the HMS.

Design optimization of a stand-alone green energy system of university campus based on Jaya-Harmony Search and Ant Colony Optimization algorithms approaches

The use of renewable energy resources in the production of electrical energy is becoming prevalent due to the decreasing installation costs of these resources and increasing environmental concerns. A Hybrid Renewable Energy System (HRES) is beneficial for meeting load demands, but optimal sizing is the main problem in the process of obtaining a cost-efficient system based on certain load demands and techno-economic parameters. In this article, the Harmony Search (HS) algorithm was used for the optimal sizing of components and compared to other methods. The HRESs consisted of photovoltaic (PV), wind turbine, battery, diesel generator and inverter components. A powerful rule-based energy management scheme was introduced to manage the power flow between system parts which constitute the microgrid that will minimize the annual system cost and reliably meet the energy demand. The decision variables for this optimization were the PV panel power, wind power, and the number of batteries. Simulation results revealed that HS provided the optimum sizing among the other methods including HOMER, Ant Colony Optimizer and Jaya. The time performances of the algorithms were also examined, and the HS algorithm had better performance and convergence properties. The optimization process was programmed using the MATLAB simulation package.

Fuzzy Logic Controller Based Modified Energy Management Strategy for Battery and UC with Improved Battery Performance

The ultracapacitor (UC) acts as a stress reducer for the battery. It is also considered as a fruitful companion for a battery in battery-UC hybrid energy storage system (HESS). Management of power between battery, UC, and drive motor, along with a suitable topology, decides the performance of the battery and drive system. Rule based energy management strategy (EMS) is simple, real-time implementable, ease in control, and based on engineer’s experience. But it lacks in controlling the state-of-charge (SOC) of battery and UC, battery current rate, and its magnitude, dc link voltage control, and UC voltage regulation. In this paper, low pass filter based conventional rule-based EMS is studied and implemented in MATLAB. Then fuzzy logic based modified rule-based EMS is proposed and simulated for the same load profile. Comparative study between conventional and modified EMS shows the proposed EMS is capable to provide constant dc link voltage, stable UC SOC operation, fast response, and reduces the large variation in speed. Proposed EMS saves the 10% of the battery energy for 20 sec load profiles as compared with battery only system. So, by use of UC, shared bus topology, and effective EMS, driving range of the vehicle and battery life can be improved and range anxiety can be reduced.

Fuel economy optimization of diesel engine for plug-in hybrid electric vehicle based on equivalent operating points

The plug-in hybrid electric vehicle (PHEV) has been progressively penetrated in the urban public transport system for its special advantages of energy saving and emission reduction potential. In order to improve the fuel economy of PHEV engine, an optimization study on the engine control parameters is carried out and applied on a PHEV with a rule-based energy management strategy (EMS) in this paper. Firstly, the mathematical model of diesel engine is established on GT-Power platform to accurately reflect the fuel consumption characteristics of engine. Secondly, a control parameter optimization method based on engine equivalent operating point is proposed and four key engine control parameters are optimized by genetic algorithm (GA). Thirdly, the optimization and calibration of the PHEV engine is performed under ChinaCity driving cycle. The results show that the maximum reduction of engine brake specific fuel consumption (BSFC) on the optimal operation line is 3.56%, and the equivalent fuel consumption of PHEV under ChinaCity, WVUCITY and WVUSUB driving cycles is reduced by 2.62%, 2.24% and 2.05%, respectively.

AI agents envisioning the future: Forecast-based operation of renewable energy storage systems using hydrogen with Deep Reinforcement Learning

Hydrogen-based energy storage has the potential to compensate for the volatility of renewable power generation in energy systems with a high renewable penetration. The operation of these storage facilities can be optimized using automated energy management systems. This work presents a Reinforcement Learning-based energy management approach in the context of CO2-neutral hydrogen production and storage for an industrial combined heat and power application. The economic performance of the presented approach is compared to a rule-based energy management strategy as a lower benchmark and a Dynamic Programming-based unit commitment as an upper benchmark. The comparative analysis highlights both the potential benefits and drawbacks of the implemented Reinforcement Learning approach. The simulation results indicate a promising potential of Reinforcement Learning-based algorithms for hydrogen production planning, outperforming the lower benchmark. Furthermore, a novel approach in the scientific literature demonstrates that including energy and price forecasts in the Reinforcement Learning observation space significantly improves optimization results and allows the algorithm to take variable prices into account. An unresolved challenge, however, is balancing multiple conflicting objectives in a setting with few degrees of freedom. As a result, no parameterization of the reward function could be found that fully satisfied all predefined targets, highlighting one of the major challenges for Reinforcement Learning -based energy management algorithms to overcome.

Optimal planning of solar PV and battery storage with energy management systems for Time‐of‐Use and flat electricity tariffs

This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) with novel rule-based energy management systems (EMSs) under flat and time-of-use (ToU) tariffs. Four schemes are investigated based on the combinations of flat and ToU tariffs for buying and selling the electricity: (1) Flat-Flat, (2) ToU-Flat, (3) Flat-ToU, and (4) ToU-ToU. For each scheme, two configurations are evaluated: (i) PV only, and (ii) PV-BES. The optimization of the grid-connected household is evaluated based on one-year realistic data. An uncertainty analysis is presented based on the variations of insolation, temperature, and load. Sensitivity analyses are implemented based on the average daily load, the grid constraint, and the costs of PV and BES. The operational analyses for 48 h in summer and winter are carried out to evaluate the dynamic performance of the systems for high and low solar insolation. The effectiveness of the proposed model is verified by comparing the results with that of common EMS based on the net metering scheme. It is found that the COE of the proposed EMS for a PV-BES house with ToU-Flat scheme (as the best option) is 2 ¢/kWh lower than that of the net metering scheme.

A Three-Stage Multitimescale Framework for Online Dispatch in a Microgrid With EVs and Renewable Energy

To accomplish more practical charging dispatch for electric vehicles (EVs) and effectively consider the multiuncertainties of renewable energy and load demand, this article presents a three-stage multitimescale framework for online dispatch of a grid-connected microgrid. In the first stage, a reference scheduling model is proposed to obtain the referral plans with a sampling period of 30 min. Considering these uncertainties, the second stage proposes a real-time operation method based on stochastic model predictive control, with a sampling period of 10 min, to update the referral plans by minimizing the deviation of the real results from the referral results. To sufficiently capture the operations of EV charging, a rule-based energy management model for the EV charging station is designed in the third stage. The controller of the third stage runs every minute and allocates the charging station power to the available EVs while meeting the charging demand and technical constraints. Numerical simulations are used to investigate the effectiveness and superiority of the proposed dispatch framework. The simulation results show that the proposed framework reaches 98% of ideal performance achieved by off-line optimization.

A modified rule-based energy management scheme for optimal operation of a hybrid PV-wind-Stirling engine integrated multi-carrier energy system

In this study, rule-based energy management strategies (EMS) based on the modifications of the traditional load following (LF) and circuit charging (CC) have been proposed and developed to effectively coordinate the operation of an integrated multi-carrier hybrid energy system. The proposed EMS aim to overcome some of the challenges of the traditional rule-based EMS and broaden their application to the management of complex energy systems. The study deploys a bi-level optimisation scheme to obtain the optimal number of system components that simultaneously minimises the cost, reliability and emissions, in the outer-loop and implements the rule-based EMS in the inner-loop. Also, the results of the optimal system have been simulated for a 48 h timespan, to investigate the effects of the proposed EMS on the Stirling back-up start-ups, battery storage limits, and generation of other energy vectors. The results indicate the deployment of split back-up and batteries minimise the commitment of the back-up, dumped power and emissions. However, the number of start-ups of the back-up increases appreciably by 15.34% and 36%, with the deployment of 2-split and 4-split Stirling, respectively in CC with battery storage. Correspondingly, the operational cost of the system rises as the number of splits increases, but only a slight change in the energy cost is observed, because of the significant reductions in the capacity of the green generators. Interestingly, the batteries record many duty cycles, store less energy and attain lower discharge limits as many small capacity ST back-ups are deployed. Other results demonstrate the additional capabilities of the proposed EMS in handling complex energy systems by the substantial increase in the generation of heating and cooling with increasing splits of the back-up and inclusion of batteries in the optimal system.

An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle

With the development of high-altitude and long-endurance unmanned aerial vehicles (UAVs), optimization of the coordinated energy dispatch of UAVs’ energy management systems has become a key target in the research of electric UAVs. Several different energy management strategies are proposed herein for improving the overall efficiency and fuel economy of fuel cell/battery hybrid electric power systems (HEPS) of UAVs. A rule-based (RB) energy management strategy is designed as a baseline for comparison with other strategies. An energy management strategy (EMS) based on fuzzy logic (FL) for HEPS is presented. Compared with classical rule-based strategies, the fuzzy logic control has better robustness to power fluctuations in the UAV. However, the proposed FL strategy has an inherent defect: the optimization performances will be determined by the heuristic method and the past experiences of designers to a great extent rather than a specific cost function of the algorithm itself. Thus, the paper puts forward an improved fuzzy logic-based strategy that uses particle swarm optimization (PSO) to track the optimal thresholds of membership functions, and the equivalent hydrogen consumption minimization is considered as the objective function. Using a typical 30 min UAV mission profile, all the proposed EMS were verified by simulations and rapid controller prototype (RCP) experiments. Comprehensive comparisons and analysis are presented by evaluating hydrogen consumption, system efficiency and voltage bus stability. The results show that the PSO-FL algorithm can further improve fuel economy and achieve superior overall dynamic performance when controlling a UAV’s fuel-cell powertrain.

Composite Non-Linear Control of Hybrid Energy-Storage System in Electric Vehicle

The underlying circuit control is a key problem of the hybrid energy-storage system (HESS) in electric vehicles (EV). In this paper, a composite non-linear control strategy (CNC) is proposed for the accurate tracking current/voltage of the fully-active HESS by combining the exact feedback linearization method and the sliding mode variable structure control technology. Firstly, by analyzing the circuit characteristics of HESS, the affine non-linear model of fully-active HESS is derived. Then, a rule-based energy management strategy (EMS) is designed to generate the reference current value. Finally, the HESS is linearized by the exact feedback linearization method, and the proposed CNC strategy is developed combined with sliding mode variable structure control technology to ensure fast response, high performance, and robustness. At the same time, the stability proof based on the Lyapunov method is given. Moreover, the performance of the CNC strategy is thoroughly investigated and compared with simulation studies with the traditional PI control and a modified sliding mode control, and its effectiveness under different driving conditions is fully verified.

A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle

To achieve the optimal energy allocation for the auxiliary power unit (APU) and battery of a range-extended electric vehicle, a novel hybrid-point-line energy management strategy (H–P-LEMS) has been proposed from a multi-scale view. First, a multi-objective optimization (MOO) model is established which takes into account energy consumption, emissions and battery life. The barebones multi-objective particle swarm optimization is applied for solving the MOO problem. And a dynamic programming-optimized algorithm is applied to obtain the optimal curve/area of APU to establish objective function of MOO. Then, an adaptive approach uses a fuzzy logic controller with the battery consideration to adjust parameters in real time. Simulation results show that there is a clear conflict that three optimization objectives cannot be optimal at the same time and the final optimization solution with optimal comprehensive evaluation index is selected to evaluate the performance of the proposed methodology. Finally, the simulation and experimental results thoroughly indicate that the proposed H–P-LEMS has better balance than conventional rule-based energy management strategy (EMS). As expected, economy improvement, emission reduction and prolonging the battery service life are kept in balance effectively. And this result can be used to develop EMS to improve comprehensive performance levels.

A Robust Design Method for Optimal Engine Operating Zone Design of Plug-in Hybrid Electric Bus

The optimal engine operating zone of energy management plays an important role in the fuel economy improvement of Plug-in hybrid electric buses. However, the existing investigations usually design the engine operating zone by experience. This paper proposes a robust design method for the robust and optimal design of the engine operating zone. Firstly, a nonlinear model predictive control (NMPC)-based energy management together with a single-point preview SOC (state of charge) plan method is designed. Then, a Taguchi robust design model is designed to find the optimal engine operating zone by taking the energy management as underlying solving module. Particularly, the noises of driving cycles and stochastic vehicle mass are considered to improve the robust performance of the engine operating zone. Finally, The Monte Carlo Simulation is deployed to verify the robustness and optimal performances of the designed engine operating zone. Simulation results demonstrate that the proposed method is beneficial to the fuel economy improvement, where the fuel economy can be averagely improved by 9.10% compared with the experienced designed engine operating zone, and can be averagely improved by 16.34% compared with the rule-based energy management strategy.

Optimized rule-based energy management for a polymer electrolyte membrane fuel cell/battery hybrid power system using a genetic algorithm

The polymer electrolyte membrane fuel cell (PEMFC) coupled with the battery is a promising hybrid power system for future energy supply application. Fuel cell durability, battery charge sustenance, and fuel consumption strongly rely on the energy management strategy (EMS). This paper puts forward an optimized rule-based EMS using genetic algorithm (GA) to optimally allocate the power between the fuel cell and the battery system. Control variables in real-time rule-based EMS are optimally adjusted with single objective of battery charge sustenance considering the fuel cell durability and efficiency. The proposed optimized rule-based EMS is simulated and experimentally verified via MATLAB/Simulink and LabVIEW-based experimental rig, respectively. The conventional rule-based EMS, fuzzy logic EMS, and dynamic programming (DP) EMS are also examined for comparison. The comparison results elucidate that the optimized rule-based EMS realizes a large performance improvement over the conventional rule-based and fuzzy logic EMSs. Near optimal performance is verified compared with DP EMS in terms of fuel economy, battery charge sustenance, fuel cell efficiency, and system durability. The combination of rule-based EMS and GA optimization algorithm has the advantage of having expert experience and global optimization properties, realizing optimal power allocation in real-time application with lower computation burden, which could be applied easily to other EMS system without loss of validity.

A Real-Time Rule-Based Energy Management Strategy With Multi-Objective Optimization for a Fuel Cell Hybrid Electric Vehicle

Energy management strategy (EMS) has a great impact on securing fuel cell durability, battery charge sustenance, and fuel saving in fuel cell hybrid electric vehicles (FCHEVs). This study aims to develop EMS that can be applied in real-time to satisfy above conditions. Real time power separation was performed using rule-based EMS. A genetic algorithm (GA) was implemented to calculate the optimal battery charge/discharge criterion that simultaneously satisfies the minimum hydrogen consumption rate, battery charge rate preservation, and high fuel cell efficiency. The battery charge/discharge parameter values vary according to driving patterns, and in this paper, typical suburban, urban, and highway driving conditions are considered. For the real-time application of this research method, the effectiveness was demonstrated by applying the driving conditions of unknown patterns. The effect on the initial battery SOC on EMS was analyzed, and in order to verify the superiority of this method, it was compared and analyzed with EMS results using dynamic programming and fuzzy logic under the same driving cycles. The effectiveness of this research method was verified through simulation, and it was confirmed through experiments for real-time application. Since there is a limit to the experiment using an actual fuel cell vehicle, the experiment was performed using a fuel cell and battery. This method can be applied to real fuel cell vehicles in the same way.