Rule-based Energy Management Strategy Research Articles

Rule-based energy management strategies for PEMFC-based micro-CHP systems: A comparative analysis

Commercial PEMFC-based micro-CHP systems are operated by rule-based energy management strategies. Each of these strategies constitutes a different way to meet the household energy demand (following the heat demand, following the electricity demand, the maximum of the two, etc.). Previous studies demonstrate that which of them is the best —i.e. the one that manages to meet the demand at the lowest operating cost— depends on the particular scenario in which the micro-CHP system works (gas and electricity prices, annual energy demands, ability to export electricity to the grid, etc.). This paper aims to explore this dependence relationship and to deepen our understanding of it. To this end, a parametric analysis is conducted and the performances achieved by four rule-based operating strategies are compared. The parameters whose influence is studied, and through which the scenario is jointly characterized, are: (1) energy prices (electricity and natural gas), (2) feed-in tariff, (3) stack degradation, (4) climate and (5) heat to power ratio of the demand. The results show this dependence relationship in a clear and more comprehensive way, and offer a better understanding of its nature. From this improved understanding it can be inferred, among other things, that adapting the strategy to the scenario can generate annual savings of up to 14.5 percentage points. Moreover, this enhanced characterization of that dependence relationship can be useful for the design of a new operating strategy, a strategy that, without falling into the complexity that an optimal energy management approach (based on linear programming) involves, manages to exploit the savings potential of micro-CHP systems, thus facilitating their future mass commercialization.

Regenerative Braking Conscious Rule-Based Energy Management Strategy for Semi-Active Hybrid Energy Storage Systems for Electric Vehicles

Hybrid Energy Storage Systems (HESS) function as a solution to extend the lifespan of battery packs by maintaining a low charge/discharge rate. The integration of HESS in electric vehicles (EVs) is facilitated by supercapacitors (SC), known for their safe operation even under harsh conditions. The widely adopted rule-based power follower energy management strategy (EMS) is employed to control and restrict battery discharge within a defined range with SC support. Additionally, it charges the SC to accommodate regenerative energy, but it often neglects exploring the SC voltage set reference. This study investigates the SC voltage reference point and introduces a method to generate the reference point by equating the SC energy with the available kinetic energy in the moving vehicle. The proposed method was simulated and compared against the benchmark method using a portion of the medium segment Worldwide Harmonized Light Vehicles Test Procedure (WLTP) driving cycle. The results demonstrate a more stable discharge power for the battery bank, accompanied by improvements in battery voltage stability. The root mean square (RMS) battery current values were found to be 43.68 A and 42.92 A for the benchmarked and proposed methods, respectively, indicating a slight improvement of about 1%. Furthermore, the proposed method reduces the voltage deviation from 2.73% to about 2.57%, showcasing an additional positive outcome. These findings suggest that dynamically adjusting the SC voltage based on kinetic energy can enhance battery life and stability in EVs, offering a promising direction for future research and development of EMS.

Energy Management Strategy of Fuel Cell Commercial Vehicles Based on Adaptive Rules

Fuel cell vehicles have been widely used in the commercial vehicle field due to their advantages of high efficiency, non-pollution and long range. In order to further improve the fuel economy of fuel cell commercial vehicles under complex working conditions, this paper proposes an adaptive rule-based energy management strategy for fuel cell commercial vehicles. First, the nine typical working conditions of commercial vehicles are classified into three categories of low speed, medium speed and high speed by principal component analysis and the K-means algorithm. Then, the crawfish optimization algorithm is used to optimize the back propagation neural network recognizer to improve the recognition accuracy and optimize the rule-based energy management strategy under the three working conditions to obtain the optimal threshold. Finally, under WTVC and combined conditions, the optimized recognizer is used to identify the conditions in real time and call the optimal rule threshold, and the sliding average filter is used to filter the fuel cell output power in real time, which finally realizes the adaptive control. The simulation results show that compared with the conventional rule-based energy management strategy, the number of fuel cell start–stops is reduced. The equivalent hydrogen consumption is reduced by 7.04% and 4.76%, respectively.

Dual-Source Cooperative Optimized Energy Management Strategy for Fuel Cell Tractor Considering Drive Efficiency and Power Allocation

To solve the problems of the low driving efficiency of a fuel cell tractor power source and the high hydrogen consumption caused by the irrational power allocation of the energy source, the power system was divided into two parts, power source and energy source, and a dual-source cooperative optimization energy management strategy was proposed. Firstly, a general energy efficiency optimization method was designed for the power source composed of a traction motor and PTO motor, and the energy source was composed of a fuel cell and power battery. Secondly, the unified objective function and constraint conditions were established, and the instantaneous optimization algorithm was used to construct the weight factor. The instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were designed, respectively. Finally, with the demand power as the transfer parameter, the instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were integrated to form a dual-source collaborative optimal energy management strategy. In order to verify the effectiveness of the proposed strategy, a rule-based energy management strategy was developed as a comparison strategy and tested in an HIL test under plowing and rotary plowing conditions. The results show that the average fuel cell efficiency of the proposed strategy increased by 7.86% and 8.17%, respectively, and the proposed strategy’s equivalent hydrogen consumption decreased by 24.21% and 9.82%, respectively, compared with the comparison strategy under the two conditions. It can significantly reduce the SOC fluctuation of the power battery and extend the service life of the power battery.

A novel EMS design framework for SPHTs based on instantaneous layer, driving event layer, and driving cycle layer

To effectively harness the energy-saving potential of series–parallel hybrid transmissions (SPHTs) with multiple gears and modes and to enhance the driving cycle adaptability of the rule-based energy management strategy (RB EMS), this study establishes a novel EMS design framework for SPHTs at three levels: the instantaneous, the driving event, and the driving cycle layers. Firstly, an equivalent fuel factor based on the principles of energy conservation and calorific value measurements is constructed, the energy consumption of different working modes and torque combinations under different instantaneous power units is determined, and the decision conditions of working modes are determined. Secondly, the energy consumption of engine and motor torque combination under constant speed, acceleration and deceleration driving events is compared, and the torque distribution feature of multi-power sources is determined. Thirdly, a driving cycle distribution factor is introduced and calculated using vehicle navigation map information, which can adjust the gear switching condition of parallel driving mode online. Based on above, an RB EMS adjusted with driving cycles (ADC-RB EMS) is proposed. The effectiveness of the proposed strategy is then verified through real-world vehicle tests. The fuel consumption performance of this strategy falls between the RB EMS and the dynamic programming (DP) strategy. According to the fuel consumption results, the RB EMS consumes an additional 0.35 L/100 km of fuel, while the DP strategy saves only 3.66% more energy compared to this strategy. The application potential of driving cycle distribution factor in instantaneous optimal control is tested. Compared with ADC-RB and CD-CS, the fuel saving rate is 1.71% and 5.67% respectively, which proves the energy saving performance of A-ECMS. This study provides a development direction for improving the energy saving effect of the RB EMS.

Real-Time Energy Management Strategy for Fuel Cell Vehicles Based on DP and Rule Extraction

Energy management strategy (EMS), as a core technology in fuel cell vehicles (FCVs), profoundly influences the lifespan of fuel cells and the economy of the vehicle. Aiming at the problem of the EMS of FCVs based on a global optimization algorithm not being applicable in real-time, a rule extraction-based EMS is proposed for fuel cell commercial vehicles. Based on the results of the dynamic programming (DP) algorithm in the CLTC-C cycle, the deep learning approach is employed to extract output power rules for fuel cell, leading to the establishment of a rule library. Using this library, a real-time applicable rule-based EMS is designed. The simulated driving platform is built in a CARLA, SUMO, and MATLAB/Simulink joint simulation environment. Simulation results indicate that the proposed strategy yields savings ranging from 3.64% to 8.96% in total costs when compared to the state machine-based strategy.

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.

A rule-based online energy management strategy for long-endurance stratospheric airships

Stratospheric airships are typically equipped with photovoltaic energy systems to ensure consistent and controlled long-duration flights. To maximize the utilization of energy generated by the airship's solar array for mission requirements while maintaining energy balance in a computation-efficient manner, we analyzed the characteristics of global optimal energy profiles obtained through the pseudospectral method and distilled them into a rule-based energy management strategy. The rule-based strategy simplifies solving large-scale nonlinear optimization problems posed by pseudospectral method into solving problems for optimal rule logic thresholds, which greatly enhances the speed of strategy generation. Leveraging rapid strategy generation, we developed an online energy management scheme that can achieve closed-loop control by repeatedly updating the rule logic thresholds based on the feedback states. Simulation results demonstrate that the proposed energy management strategy is more effective in utilizing energy to meet mission requirements compared to the two-phase energy management strategy from a previous study. Notably, the online energy management scheme can adapt to uncertainties such as modeling errors, rather than merely providing an open-loop plan.

A Study on the Performance of the Electrification of Hydraulic Implements in a Compact Non-Road Mobile Machine: A Case Applied to a Backhoe Loader

This work presents a study of the performance of prime mover and hydraulic implement electrification in a backhoe loader. The results are validated through simulation and experimental tests. The construction and agriculture sector has grown in recent years with the aid of compact non-road mobile machines. However, as is common in fossil fuel-powered vehicles, they significantly contribute to increasing emissions. Previous research has primarily relied on powertrain electrification to address the low-efficiency drawbacks. Notably, compact off-road vehicles comprise implements less discussed in the literature. A hybrid series topology is employed, where the rear implement is driven by an electrical drive and the Diesel engine is coupled to a generator. A rule-based energy management strategy is applied. The operation of the Diesel engine and electrical machines in optimal points of the efficiency maps are the basis of the analysis. The design is validated using simulations and experimental tests in a commercial backhoe loader as a benchmark. Experimental and simulation results obtained from the hybrid series backhoe loader applied to the hydraulic implement show a 33% reduction in fuel consumption, demonstrating the effectiveness of electrification in reducing emissions and fuel consumption of compact non-road mobile machines.

Tri-objective optimal design of a hybrid electric propulsion system for a polar mini-cruise ship

The environmental impact, economic considerations, and passenger comfort of hybrid electric propulsion systems (HEPSs) are significantly influenced by the design phase. However, a design method solely focused on minimizing annual fuel consumption may inadvertently lead to increased life-cycle cost and decreased annual pure-electric sailing time. To address this issue, this paper presents a tri-objective optimization design method to achieve an optimal trade-off among the objectives for a polar mini-cruise ship. First, a scalable mathematical model is established for a hybrid diesel engines/batteries/shore power propulsion system. Then, a refined, rule-based energy management strategy is developed to control the power flow among energy sources. Next, a tri-objective optimization model with detailed descriptions of objectives, variables, constraints, and method is formulated. Finally, the performances of three optimal designs, obtained through tri-objective optimization, single-objective optimization, and conventional heuristics, are compared through simulations based on a typical real-world navigation case. The results highlight the clear advantages of the proposed method over the other two methods. Furthermore, sensitivity analysis underscores that the parameters of the diesel engine, battery, and gear ratio exert a greater impact on HEPS performances than other factors.

Optimal Design of Grid-Connected Hybrid Renewable Energy System Considering Electric Vehicle Station Using Improved Multi-Objective Optimization: Techno-Economic Perspectives

Electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) have been widely integrated into distribution systems. Electric vehicles (EVs) offer advantages for distribution systems, such as increasing reliability and efficiency, reducing pollutant emissions, and decreasing dependence on non-endogenous resources. In addition, vehicle-to-grid (V2G) technology has made EVs a potential form of portable energy storage, alleviating the random fluctuation of renewable energy power. This paper simulates the optimal design of a photovoltaic/wind/battery hybrid energy system as a power system combined with an electric vehicle charging station (EVCS) using V2G technology in a grid-connected system. The rule-based energy management strategy (RB-EMS) is used to control and observe the proposed system power flow. A multi-objective improved arithmetic optimization algorithm (MOIAOA) concept is proposed to analyze the optimal sizing of the proposed system components by calculating the optimal values of the three conflicting objectives: grid contribution factor (GCF), levelled cost of electricity (LCOE), and energy sold to the grid (ESOLD). This research uses a collection of meteorological data such as solar radiation, temperature, and wind speed captured every ten minutes for one year for a government building in Al-Najaf Governorate, Iraq. The results indicated that the optimal configuration of the proposed system using the MOIAOA method consists of eight photovoltaic modules, two wind turbines, and thirty-three storage batteries, while the fitness value is equal to 0.1522, the LCOE is equal to 2.66 × 10−2 USD/kWh, the GCF is equal to 7.34 × 10−5 kWh, and the ESOLD is equal to 0.8409 kWh. The integration of RESs with an EV-based grid-connected system is considered the best choice for real applications, owing to their remarkable performance and techno-economic development.

Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy.

Optimized energy management for plug-in hybrid vehicles with predicted driving cycles1

The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study.

Parallel-Reinforcement-Learning-Based Online Energy Management Strategy for Energy Storage Traction Substations in Electrified Railroad

The traction power supply system is the only source of power for electric locomotives. The huge power fluctuations and complex operating conditions of the traction power supply system pose a challenge to the efficient operation of energy storage traction substations. The existing energy management strategies are difficult to achieve accurate charging and discharging, difficult to modify the control rules in real-time, and have poor migration capability. For comparison, the reinforcement learning algorithms can address the shortcomings of rule-based energy management strategies due to their model-free feature. Therefore, this paper proposes an energy management strategy based on parallel reinforcement learning to improve the efficiency of energy utilization while speeding up the convergence of the algorithm. More specifically, a Markov decision framework is established for capturing the energy management process. The Monte Carlo sampling process is also improved to achieve offline optimization by parallel reinforcement learning algorithms and reduce the impact of low-value power fragments on iteration speed. Meanwhile, the algorithm is modified to enable online updates. The case study shows that compared with other energy management strategies, the parallel reinforcement learning-based energy management strategy has faster convergence speed, higher energy exchange efficiency, better migration capability, and can adapt to various complex working conditions.

Configuration size optimization of gas-electric hybrid power systems on ships considering energy density and engine load response

System component sizes and their energy densities impact the long-term ship energy efficiency and economics, whereas power source load response influences system transient behavior, posing challenges to system design and energy management when clean fuels are used onboard. In this paper, a novel component sizing method is proposed for integrating reciprocating gas engines and energy storage systems (ESS) on ships, considering energy density and load response simultaneously. By specifying gas engine powers and ESS capacities as normalized optimization variables by ship scales, this method optimizes them based on the A. J. Klee multi-index evaluation and single-objective particle swarm optimization (PSO). Energy densities of engines and ESS were introduced to demonstrate the trade-off between the power-splitting and increased system mass and hull resistance. The engine load response was incorporated into the rule-based energy management strategy (EMS) to limit power ramps. This method was examined in the case study by taking four cruise ships as examples. It can be found that implementing the proposed evaluation-based optimization can improve cruise ship energy efficiency while maintaining reliability over the voyage. It is possible to reduce fuel consumption and carbon emissions by up to 17.1% and 14.8%, respectively. The minimum system reliability can be increased from 0.025 to 0.475 during transients. Furthermore, ship life-cycle costs can be reduced compared to the original design method, and the payback period for gas engines, ESS, and fuel tanks can be shortened to less than six years. Among the various cases, the definition of normalized optimization variables broadens the applicability of this method away from a specific ship, allowing it to be applied to ships of various tonnages and types.

Optimal Design of a Grid-Independent Solar-Fuel Cell-Biomass Energy System Using an Enhanced Salp Swarm Algorithm Considering Rule-Based Energy Management Strategy

Optimal Design of a Grid-Independent Solar-Fuel Cell-Biomass Energy System Using an Enhanced Salp Swarm Algorithm Considering Rule-Based Energy Management Strategy

Optimization of Fuel Consumption for Rule-Based Energy Management Strategies of Hybrid Electric Vehicles: SOC Compensation Methods

Optimization of Fuel Consumption for Rule-Based Energy Management Strategies of Hybrid Electric Vehicles: SOC Compensation Methods

Adaptive Dynamic Surface Control With Disturbance Observers for Battery/Supercapacitor-Based Hybrid Energy Sources in Electric Vehicles

The underlying control in the hybrid energy source system (HESS) of an electric vehicle plays a pivotal role. Uncertainty is unavoidable in system modeling owing to variations in electrical parameters and unknown external disturbances, which inevitably deteriorate the control performance of the HESS. In this study, an innovative adaptive dynamic surface control with disturbance observers (ADSCDOB) is adopted as a control scheme for underlying tracking in the HESS to counteract the adverse effects and improve control accuracy. First, disturbance observers are designed by employing a nonlinear disturbance observer and an extreme learning machine approximator to estimate the mismatched and matched uncertainties. Subsequently, the proposed ADSCDOB scheme integrates the adaptive dynamic surface technique and second-order differentiators to achieve robust control, in which voltage/current references are obtained through the rule-based energy management strategy. The established ADSCDOB obviates the “differential explosion” problem and ensures that the closed-loop system is semi-globally and uniformly bounded. Comprehensive simulations and prototype experiments prove the effectiveness of the ADSCDOB, confirming its satisfactory performance in terms of a fast response, reduced error, and robust stability under hybrid driving conditions.

Optimal online energy management strategy of a fuel cell hybrid bus via reinforcement learning

An energy management strategy (EMS) based on reinforcement learning is proposed in this study to enhance the fuel economy and durability of a fuel cell hybrid bus (FCHB). Firstly, a comprehensive powertrain system model for the FCHB is established, mainly including the FCHB’s power balance, fuel cell system (FCS) efficiency, and aging models. Secondly, the state–action space, state transition probability matrix (TPM), and multi-objective reward function of Q-learning algorithm are designed to improve the fuel economy and the durability of power sources. The FCHB’s demand power and battery state of charge (SOC) serve as the state variables and the FCS output power is used as the action variable. Using the demonstration FCHB data, a state TPM is created to represent the overall operation. Finally, an EMS employing Q-learning is formulated to optimize the fuel economy of FCHB, maintain battery SOC, suppress FCS power fluctuations, and enhance FCS lifetime. The proposed EMS is tested and verified through hardware-in-the-loop (HIL) tests. The simulation results demonstrate the effectiveness of the proposed strategy. Compared to a rule-based EMS, the Q-learning-based EMS can improve the energy economy by 7.8%. Furthermore, it is only a 3.7% difference to the best energy economy under dynamic optimization, while effectively reducing the decline and enhancing the durability of the FCS.

Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles

Driving pattern recognition (DPR) is widely used to improve the robustness of rule-based energy management strategies (EMS). However, the number and quality of preset patterns limit further performance improvements. This paper proposes a meta rule-based EMS to replace the role of DPR, wherein the meta rule refers to a rule that determines the parameters of the energy management rule. Firstly, the power distribution rule pattern is derived through the analysis of dynamic programming results. Then, the value of the parameters is obtained through clustering. Subsequently, a feature screening method based on mutual information is proposed to select useful features of driving conditions and system states. The seagull optimization algorithm is then employed to find the mathematical expression of the meta rule. Finally, simulation results demonstrate the effectiveness of the meta rule-based EMS. Compared with DPR-based and long short-term memory-based EMS, the meta rule-based EMS can reduce the Ampere-hour throughput of Li-ion batteries by 17.0% and 9.7% respectively under the China light-duty vehicle test cycle, and by 3.4% and 16.6% respectively under a real-world driving condition. The meta rule-based EMS requires only dozens of milliseconds for each moment and is suitable for real-time systems.