Rule-based Energy Management Strategy Research Articles

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

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.

Energy-optimization design and management strategy for hybrid electric non-road mobile machinery: A case study of snowblower

Reduced reliance on fossil fuels is a critical issue, and electrification of agricultural machinery is a solution for lowering greenhouse gas emissions in non-road transportation. By separating the load and drive from the engine, electrification the implement allows the engine to operate at higher efficiency. This study suggests a series hybrid design that combines a snow blower with battery support and a tractor with a reduced engine size to meet the fuel consumption reduction target while maintaining its performance. Accordingly, the Particle Swarm Optimization (PSO) algorithm is employed to size the battery that meets the constraints. Additionally, the motors of actuators are designed based on efficiency maps. Moreover, the rule-based energy management strategy is also proposed to assess the solution. This suggestion is analyzed using simulation and compared to a traditional tractor as a benchmark. The simulation results demonstrate the effectiveness of the strategy in lowering emissions from non-road machinery. Furthermore, this approach can be applied to various different types of implements attached to the tractor.

Optimal design of an adaptive energy management strategy for a fuel cell tractor operating in ports

As World trade is growing rapidly, the reduction of the environmental impact of in-port operations towards a low or zero-emission scenario is becoming a paramount issue. To this aim, replacing Diesel engines of cargo handling equipment used in port logistics (e.g. reach stackers, forklifts, yard tractors, etc.) with cleaner propulsion alternatives may play a major role. In this study, a robust rule-based energy management strategy is proposed for a newly developed fuel cell/battery hybrid powertrain of a yard tractor used for roll-on and roll-off in-port operations. As typical port operations are characterized by mission profiles that can vary significantly in terms of driving and duty cycles during the same work-shift, the proposed strategy, built upon the observation of the powertrain behavior under the application of an optimal controller, dynamically adapts the operation of fuel cell and battery in order to track a predefined battery state of charge trajectory, while minimizing the hydrogen consumption. The use of an optimal model-based approach as a reference for the design of an online implementable energy management strategy is indeed particularly suitable in the present case: despite their inherent high variability, the yard tractor mission profiles can be regarded as the combination of a set of predictable parameters. Results show that the application of the proposed control strategy allows the hybrid powertrain to achieve excellent performance, by leading its components to run efficiently and across suitable operative conditions. The achieved hydrogen consumption, for the considered missions, is only 2%–3% higher than that of the optimal controller, despite a quite different evolution of the battery state of charge, that is the feedback control variable. By means of this strategy, the transient loading of the fuel cell is prevented, while the battery pack ensures the fulfillment of the peak power requests, with beneficial effects in terms of on-board stored energy exploitation. The key advantage of the developed rule-based approach lies in its robustness, reliability and online applicability in real-time powertrain control.

Energy Management Strategy Optimization for a Novel Electric‐hydraulic Power Coupling Vehicle Considering the Influence of Multiple Driving Cycles

Hydraulic hybrid technology can further improve the economy of electric vehicles (EV). In this article, a novel electric‐hydraulic power coupling vehicle (EHPCV), which is endowed with multiple drive modes and energy regenerative braking modes, is investigated. The electric‐hydraulic power coupler (EHPC) is an innovative device for realizing power coupling and conversion. The design and optimization of the energy management strategy (EMS) are key to ensuring the efficient and stable operation of the hybrid electric vehicle. Based on the analysis of energy flow, a rule‐based EMS (RB‐EMS) is built. To achieve more reasonable energy management of the EHPCV in random driving environments, the article proposes an optimization framework for the EMS based on multiple driving cycles. More precisely, a multiobjective optimization mathematical model is established with the goal of maximizing the battery state of charge and minimizing velocity error. Moreover, the optimal Latin hypercube design is used to select the design variables that have a significant impact on the optimization objective. Definitively, the nondominated sorting genetic algorithm II algorithm combines with RB‐EMS to optimize the control parameters. The verification results show that the optimized EMS enables the EHPCV to have superior economic advantages.

Hierarchical Model-Predictive-Control-Based Energy Management Strategy for Fuel Cell Hybrid Commercial Vehicles Incorporating Traffic Information

With the development of intelligent transportation systems, access to diverse transportation information has become possible. Integrating this information into an energy management strategy will make the energy allocation prospective and thus improve the overall performance of the energy management program. For this reason, this paper proposes a hierarchical model predictive control (MPC) energy management strategy that incorporates traffic information, where the upper layer plans the vehicle’s velocity based on the traffic information and the lower layer optimizes the energy distribution of the vehicle based on the planned velocity. In order to improve the accuracy of the planning speed of the upper strategy, a dung beetle optimization-radial basis function (DBO-RBF) prediction model is constructed, artfully optimizing the RBF neural network using the dung beetle optimization algorithm. The results show that the prediction accuracy is improved by 13.96% at a prediction length of 5 s. Further, when the vehicle passes through a traffic light intersection, the traffic light information is also considered in the upper strategy to plan a more economical speed and improve the traffic efficiency of the vehicle and traffic utilization. Finally, a dynamic programming (DP)-based solver is designed in the lower layer of the strategy, which optimizes the energy distribution of the vehicle according to the velocity planned by the upper layer to improve the economy of the vehicle. The results demonstrate achieving a noteworthy 3.97% improvement in fuel economy compared to the conventional rule-based energy management strategy and allowing drivers to proceed through red light intersections without stopping. This proves a substantial performance enhancement in energy management strategies resulting from the integration of transportation information.

Research on energy management strategy of fuel-cell vehicles based on nonlinear model predictive control

Fuel cell hybrid electric vehicles (FCHEV) are one of the most promising new energy vehicles. The cost and lifetime of its powertrain have limited its commercial development. This paper proposed an energy management strategy based on nonlinear model predictive control (NMPC) technology to solve the economy and durability problem of FCHEVs. Based on Markov Monte Carlo(MCMC) method, a prediction model of multi-scale operating conditions is established, and dynamic programming(DP) is used to realize the optimal control in the predicted time domain. The “constant speed prediction” is innovatively adopted in the transition stage to improve the prediction accuracy and enable the model to be realized online. The ways to reduce calculating amount of NMPC are also discussed in this paper. This simplification leads to suboptimal fuel economy and durability of control system but can have obvious reduction in calculating time. The simulation results show that, compared with the thermostat strategy and the power following strategy, the degradation cost decrease of 11.1% and 23.9% and the total operation cost of NMPC decrease of 11.0% and 23.5% respectively. The NMPC strategy has better economy and durability than the rule-based energy management strategy, is close to the global optimal result obtained by dynamic programming and can meet the requirements of real-time control.

Optimization of hybrid energy management system based on high-energy solid-state lithium batteries and reversible fuel cells

Integrating a high power source, like a super capacitor (SCAP), and a lithium-ion battery (LIB) for electric vehicle (EV) applications yields achievement improvements, including maximum reliability, long lifetime (LT), small size, and competitive pricing for the overall source. A hybrid energy storage system (ESS) controlled by an intelligent energy management strategy (EMS) may be substantially included in multi-source EV design and development. Therefore, this paper proposes a hybrid chimp optimization algorithm (ChOA) and Levy walk technique to create an optimum EMS. The proposed technique reduces battery power (BP) stress and increases the LT, which is accomplished by using a hybrid ChOA-Levy walk (ChOA-LW) optimization algorithm with a rule-based approach in accordance with understanding the performance of LIB and SCAP. In order to optimize the rule-based EMS's control settings, the latter strategy is suggested. The control approach can be implemented online once the offline optimization procedure is finished. The presented technique is evaluated via simulation and on an experimental platform by means of a power emulator testbed of a LIB/SCAP hybrid ESS. In terms of BP stress and LT, the findings are compared with a conventional rule-based approach and a mono-source containing a regular high-power LIB. Results obtained demonstrate the effectiveness of the suggested technique, which enables the requested performance to be satisfied with better energy utilization. The assessment results also show notable LT improvements for the LIB, an improvement of up to 19% over the mono-source in reference to a conventional single cell LIB.

A Grid-Connected Optimal Hybrid PV-BES System Sizing for Malaysian Commercial Buildings

In this article, the optimal sizing of hybrid solar photovoltaic and battery energy storage systems is evaluated with respect to rooftop space and feed-in tariff rates. The battery scheduling is performed using a proposed rule-based energy management strategy. The rules are formulated based on the demand limit, PV export power limit, and state of charge of the battery. Furthermore, optimization modeling with initial choices of parameters and constraints in terms of solar photovoltaic and battery energy storage capabilities is developed to minimize the total net present cost. The hourly values of solar irradiance, air temperature, electrical loads, and electricity rates are considered the inputs of the optimization process. The optimization results are achieved using particle swarm optimization and validated through an uncertainty analysis. It is observed that an optimal photovoltaic and battery energy storage system can reduce the cost of electricity by 12.33%, including the sale of 5944.029 kWh of electricity to the grid. Furthermore, energy consumption, peak demand, and greenhouse gas emissions are reduced by 13.71%, 5.85%, and 62.59%, respectively. A comprehensive analysis between the variable and fixed data for the load, energy from PV, batteries, and the grid, and costs demonstrates that the optimal sizing of photovoltaic and battery energy storage systems with the best mix of energy from PV, batteries, and the grid provides the optimal solution for the proposed configuration.

Research on an Improved Rule-Based Energy Management Strategy Enlightened by the DP Optimization Results

Plug-in hybrid electric vehicles (PHEVs) have gradually become an important member of new energy vehicles because of the advantages of both electric and hybrid electric vehicles. A fast and effective energy management strategy can significantly improve the fuel-saving performance of vehicles. By observing the dynamic programming (DP) simulation results, it was found that the vehicle is in the charge-depleting mode, the state of charge (SOC) drops to the minimum at the end of the journey, and the SOC decreases linearly with the mileage. As such, this study proposed an improved rule-based (IRB) strategy enlightened by the DP strategy, which is different from previous rule-based (RB) strategies. Introducing the reference SOC curve and SOC adaptive adjustment, the IRB strategy ensures that the SOC decreases linearly with the driving distance, and the SOC drops to the minimum at the end of the journal, similar to the result of the DP strategy. The fuel economy of PHEV in the RB and DP energy management strategies can be considered as their worst-case and best-case scenarios, respectively. The simulation results show that the fuel consumption of the IRB strategy under the China Light-duty Vehicle Test Cycle is 3.16 L/100 km, which is 7.87% less than that of the RB strategy (3.43 L/100 km), and has reached 44.41% of the fuel-saving effect of the DP strategy (2.84 L/100 km).

A dynamic programming-optimized two-layer adaptive energy management strategy for electric vehicles considering driving pattern recognition

To achieve optimal real-time power allocation in electric vehicles, a two-layer adaptive dynamic programming (DP) optimization energy management strategy (EMS) has been proposed. The upper layer uses learning vector quantization (LVQ) to produce real-time driving pattern recognition (DPR) results. The method determines 10 characteristic parameters for training the recognition network and the length of the sampling window is 120 s. The typical driving cycles are divided into different levels of blocks to identify the real-time DPR level. The lower layer adopts the optimization strategy of DP extraction to adjust the power distribution between the battery pack and the supercapacitor pack according to the recognition results. DP is used to define a cost function to minimize the energy loss of the hybrid energy storage system (HESS) and optimize the battery usage range in the system. The near-optimal real-time EMS is extracted by analyzing the DP control behavior of the battery under the layered state of charge (SOC). The simulation results indicate that the proposed new rule control based on DP optimization (NRB) EMS improves the system efficiency by 10 % compared with the original rule-based (RB) EMS under different temperatures and DPR levels. In addition, the system efficiency gap is controlled at 3 % compared with DP.