Rule-based Control Strategy Research Articles

Multi-Objective Optimization-Based Real-Time Control Strategy for Battery/Ultracapacitor Hybrid Energy Management Systems

Battery and ultracapacitor (UC) have complementary advantages, which meet the requirements of energy storage systems (ESSs) for plug-in electric vehicles (PEVs). In this paper, a novel control strategy is proposed to manage the power distribution between the battery and UC for a hybrid energy management system (HEMS) in PEVs. This control strategy aims at realizing less power loss, longer battery lifecycle, as well as UC's stable terminal voltage and ability of quick charge/discharge. Based on these three optimization targets, we define three sets of loss functions and formulate a multi-objective optimization (MOO) problem. In particular, two different methods, weighted method and no-preference method, are implemented to transform the MOO problem into a uni-objective convex optimization problem. The final problem is solved using the Karush-Kuhn-Tucker conditions. Simulation is conducted to verify the proposed control strategy, while a battery-only ESS and a HEMS utilizing rule-based control strategy are implemented as a comparative study. A scaled-down laboratory prototype is built to validate the theoretical analysis and simulation results. The results indicate that the proposed control strategy brings the benefits of minimized battery current magnitudes and ripples, enhanced system efficiency, stabilized dc-link voltage, and improved dynamic response. Moreover, this strategy exhibits fast computation speed and requires no pre-information of future load demand. Therefore, it can be easily deployed in real-time.

Self-Learning Enhanced Energy Management for Plug-in Hybrid Electric Bus With a Target Preview Based SOC Plan Method

This paper proposes a self-learning enhanced energy management for plug-in hybrid electric bus (PHEB). Two innovations are made to distinguish our contributions from others. First, a target preview-based reference SOC plan method is proposed, where a linear reference SOC trajectory will be re-planed at fixed time steps (every 60 s) by taking the expected SOC at the destination as the target, and a feasible zone is exclusively defined to provide a margin for the fuel economy improvement. Second, a reinforcement learning-Pontryagin's minimum principle (RL-PMP)-based energy management constituted by the RL and PMP algorithms is proposed, where the average velocity with time moving, the SOC, and the normalized trip distance are taken as states and the co-state is taken as the action. Different from the conventional RL methods, the co-sate is only updated at the fixed time step, and the reward will be evaluated in the next 60 s. This can greatly accelerate the training process of the RL-PMP. In addition, the driving conditions in the next 60 s are predicted by a designed Markov chain. The simulation results demonstrate that the fuel economy of the RL-PMP is close to the dynamic programming (DP) and can be averagely improved by 14.155% compared to the rule-based control strategy.

Demand-Side Management of Air-Source Heat Pump and Photovoltaic Systems for Heating Applications in the Italian Context

Matching demand profile and solar irradiance availability is necessary to meet space heating and domestic hot water needs by means of an air-source heat pump and photovoltaic system in a single-family house. Demand-side management, with smart control of the water storage set-point, is a simple but effective technique. Several studies in the literature pursue demand-side matching and self-consumption goals through system adjustments based on the model predictive control. This study proposes a rule-based control strategy, based on instantaneous photovoltaic (PV) power production, with the purpose of enhancing the self-consumption. This strategy exploits the building’s thermal capacitance as a virtual battery, and the thermal storage capacity of the system by running the heat pump to its limit when PV surplus power is available, and by eventually using an electric heater in order to reach higher temperatures. Results of annual dynamic simulations of a building and its heating system show that the proposed rule-based control strategy is able to reduce significantly the energy exchanges between the system and the grid. Despite the enlarged renewable energy share, economic analysis points out the pursuit of the self-consumption goal may lead to a diminution of the economic advantage in the Italian context (Italian weather data and the electric power pricing scheme).

Optimal Operation of an Energy Management System Using Model Predictive Control and Gaussian Process Time-Series Modeling

This paper describes an optimal operation scheme for energy management systems using Gaussian process forecasting and model predictive control (MPC) in the context of grid-connected microgrids with local generation, loads, and storage. The main objective of the control is to minimize the cost of energy taken from the grid. The microgrid consists of a photovoltaic (PV) panel and a battery energy storage system, which are connected to a power grid and a local load via a dc bus. At each sampling time, the predictions for PV output power and load demand power are calculated, and an MPC algorithm is executed based on these predictions and a physical battery model to decide the set point of the battery. Simulations of two case studies, namely, a labscale microgrid and a commercial microgrid, are presented. We compare the performance of MPC with various horizon lengths to a rule-based control strategy to demonstrate a cost reduction of more than 2%.

Model predictive control of a hybrid heat pump system and impact of the prediction horizon on cost-saving potential and optimal storage capacity

The present paper analyses the cost-optimal sizing and hourly control strategy of a hybrid heat pump system for heating application, composed by an electrically-driven air source heat pump and a gas boiler. These hybrid systems represent a promising solution for the energy retrofit of existing buildings and new installations, being able to increase the efficiency of monovalent systems, especially at low external temperatures. The use of thermal storage can furtherly minimize both the operating cost and the primary energy consumption, shifting the operation of the heat pump to the most profitable periods. In this work, the optimal control problem has been investigated by means of mixed-integer linear programming, considering an ideal forecast of external temperature and thermal load on given horizon periods (i.e. model predictive control). Achievable cost savings with respect to a traditional rule-based control strategy with no storage are presented as a function of both prediction horizon and storage capacity in a dimensionless form. A relation between prediction horizon length and optimal storage capacity is shown. An example of application of the method is illustrated, showing cost savings up to 8%. A sensitivity analysis on the storage tank losses, climatic conditions, generators efficiency, and energy prices is also presented, showing the cost saving potential in all these different conditions.

A Control Strategy for Driving Mode Switches of Plug-in Hybrid Electric Vehicles

Driving mode switches of hybrid vehicles are significant events. Due to the different dynamic characteristics of the engine, motor, and wet clutch, it is difficult to coordinate torque fluctuations caused by mode switches. This paper focused on a control strategy for driving mode switches of plug-in hybrid electric vehicles (PHEVs) with a multi-disk wet clutch. First, the dynamic model of the PHEV was established, and a rule-based control strategy was proposed to divide the working mode regions and distribute the torque between engine and motor. Second, the dual fuzzy control strategy for a wet clutch and the coordinated torque control strategy for driving mode switches were proposed. The dual fuzzy logic control system consisted of the initial pulse-width modulation (PWM)’s duty cycle control and the changing rate of the PWM’s duty cycle control. Considering the difference in the dynamic characteristics between engine, motor, and wet clutch, a coordinated control strategy for the driving mode switches of PHEVs was put forward. Third, simulations of driving mode switches between pure electric driving mode and only engine driving mode were conducted. The results showed that the proposed control strategy could reduce the torque ripple and the jerk of the vehicle, completely satisfying the requirements of China. Finally, the control strategy for the motor-assisted engine starting process was tested on the bench. The experiment results indicated that the proposed control strategy was effective.

Threshold-changing control strategy for series hybrid electric vehicles

This paper proposes a new set of design principles to classify and design rule-based control strategies for the powertrain energy management of series hybrid electric vehicles. The design principles proposed consider the two most established rule-based control strategies for series hybrid electric vehicles, the Thermostat and the Power follower control strategies, and also an optimization-based control strategy, the Equivalent consumption minimization strategy, in terms of the mechanisms they employ to ensure charge sustaining operation and fuel efficient driving. Thus, the work then reflects upon the most effective design principles and derives a novel and superior rule-based control strategy for series hybrid electric vehicles that is claimed to outperform all the existing rule-based schemes in terms of fuel economy: the optimal primary source strategy (OPSS). The OPSS is implemented and then compared on a high fidelity hybrid electric vehicle model to Thermostat, Power follower and Equivalent consumption minimization strategies, as well as to a recently developed rule-based control strategy, the Exclusive operation strategy. As compared to conventional rule-based control strategies, the OPSS is found to deliver significantly improved fuel economy and which is remarkably close to that achieved by the optimization-based Equivalent consumption minimization strategy, while the design of the OPSS is simple and robust as compared to optimization-based strategies. The impressive performance is partly attributed to the recent improvements in engine start stop system technology. It is also shown that the battery is operated in a more steady manner, with a lower depth of discharge, consequently reducing battery degradation.

Research on Speed Optimization Strategy of Hybrid Electric Vehicle Queue Based on Particle Swarm Optimization

Traffic lights intersections are common in cities and have an impact on the energy consumption of vehicles, so it is significant to optimize the velocities of vehicles in urban road conditions. The novel speed optimization strategy for hybrid electric vehicle (HEV) queue that helps reduce fuel consumption and improve traffic efficiency is presented in this paper, where real-world traffic signal information is used to construct the research scenario. The initial values of the target velocities are obtained based on the signal phase and timing (SPAT). Then the particle swarm optimization (PSO) algorithm is used to solve the nonlinear constrained problem and obtain the optimal target velocities based on vehicle to vehicle communication (V2V) and vehicle to infrastructure communication (V2I). The lower controller, which applies rule based control strategy, is designed to split the power of the engine and two electric motors in a power split HEV, which is quite promising because of its advantages in fuel economy. Simulation results demonstrate the superior performance of the proposed strategy in reducing fuel consumption of the HEV queue and improving traffic smoothness.

Modeling and computer simulation of a novel hybrid transmission

This work presents the modeling and computer simulation of a novel hybrid transmission with a mechanical reverse driving mode, including an engine, a motor, a simple planetary gear train, and a Ravigneaux planetary gear train. Based on the given teeth number, the reduction ratios of all the clutching condition are acquired. The feasibilities of mode shifts among the clutching conditions are analyzed. Then, a modified rule-based control strategy is introduced. Subject to the vehicle condition, speed command, and predicted equivalent fuel consumptions, the most fuel economy clutching condition is selected by the control strategy. And, a computer model is developed using SIMULINK. Two popular driving cycles are applied to the simulation model, and the simulation results of the novel hybrid transmission are competitive with the existing hybrid electric vehicle models.

Urban Global Driving Cycle Construction Method and Global Optimal Energy Management in Plug-in Hybrid Electric Vehicle

This paper proposes a method of constructing global urban driving cycle based on real-time traffic information and applied in energy control strategy. Urban district driving cycles are collected in substantial in California through performance measure system (PeMS) to establish local speed fragment database. A real driving cycle (RDC) is selected in an urban district and established the corresponding time traffic information tensor model by using the data in PeMS. The real-time traffic information incomplete situation is solved by tensor completion algorithm. According to the average speed of road sections at different times, the kinematic fragments are stochastically selected in the speed fragment database to construct a real-time driving cycle of each section. The comparison between global urban construction driving cycle (CDC) and RDCs show that the CDCs well reflect the real road driving characteristic parameters. Compared to its application in the plug-in hybrid electric vehicle (PHEV) global energy management, CDCs, and RDCs fuel consumption are similarity within approximately 7% and the fuel consumption improved up to 17% in peak time compared with the rule-based control strategy.

Data mining based framework to identify rule based operation strategies for buildings with power metering system

Operation strategies influence the building energy efficiency. In order to enhance the building energy efficiency, it’s necessary to adopt proper operation strategies on building equipment. Thus, the identification of existing operation strategies is necessary for the improvement of operation strategies. A data mining (DM) based framework is proposed in this paper to automatically identify the building operation strategies. The framework includes classification and regression tree (CART), and weighted association rule mining (WARM) method, targeting at three types of rule based control strategies: on/off control, sequencing control (for equipment of the same type), and coordinated control (for equipment of different types). The performance of this framework is validated with power metering system data and manual identification results based on on-site survey of three buildings in Shanghai. The validation results suggest that the proposed framework is capable of identifying building operation strategies accurately and automatically. Implemented on the original software named BOSA (Building Operation Strategy Analysis), this framework is promising to be used in engineering field to enhance the efficiency of building operation strategy identification work.

Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability

Plug-in hybrid electric buses (PHEBs) is some of the most promising products to address air pollution and the energy crisis. Considering the switching between different working modes often bring about sudden changes of the torque and the speed of different power sources, which may lead to the instability of the power output and affect the driving performance and ride comfort, it is of great significance to develop a real-time optimal energy management strategy for PHEBs to achieve the optimization of fuel economy and drivability. In this study, the proposed strategy includes an offline part and an online part. In the offline part, firstly, the energy conversion coefficient s(t) is optimized by linear weight particle swarm optimization algorithm (LinWPSO), then, the optimization results of s(t) are converted into a 2-dimensional look-up table. Secondly, combined with three typical driving cycle conditions, the gear-shifting correction and mode switching boundary parameters that affect the drivability of the vehicle are extracted by dynamic programming (DP) algorithm. In the online part, combined with the s(t), the gear-shifting correction and mode switching boundary parameters which are obtained through offline optimization, the real-time energy management strategy is proposed to solve the trade-off problem between minimizing the fuel consumption and improving the drivability and riding comfort. Finally, the proposed strategy is verified with simulation, the results show that the proposed strategy can guarantee the engine and the electric motor (EM) work in the high-efficiency area with optimal energy distribution while keeping drivability in the variation of driving circle. The overall performance is improved by 18.54% compared with the rule-based control strategy. The proposed strategy may provide theoretical support for the optimal control of PHEB.

Operational Strategy of Hybrid Heavy-Duty Trucks by Utilizing a Genetic Algorithm to Optimize the Fuel Economy Multiobjective Criteria

An optimum operating strategy is needed for early amortization of expensive hybrid electric vehicle powertrain parts. Especially in the operation of long-haul trucks, fuel costs have a large impact on the total cost of ownership, which is the key entrepreneurial figure in the transportation business. Combined with route information, a predictive powertrain control (PPC) system increases the fuel-saving. In a MATLAB/Simulink model-based generic approach, the operating strategy and the PPC are optimized using a genetic algorithm. The tradeoff between minimizing the fuel consumption and simultaneously maximizing the vehicle speed and gradeability to decrease time-related fixed costs has to be solved. This leads to a multi-optimization problem. The operating strategy is developed for a parallel hybrid topology that includes the fuel-saving functions of regenerative braking, boosting, shifting the load point, and electric drive only operation. The methodology developed answers the search for an optimum control parameter setup combining the operational strategy and the PPC system in long-haul operations. This paper describes the model building, simulation, and optimization of a rule-based control strategy. The route profile and fuel consumption of an internal combustion engine long haul truck were measured in a real-life test run. The recorded data are used for model building and to validate the simulation tool. With an optimized parameter setup, fuel-saving of up to 11% were achieved in theory with acceptable vehicle speed and gradeability.

A novel equivalent consumption minimization strategy for hybrid electric vehicle powered by fuel cell, battery and supercapacitor

The aim of this paper is to present a sequential quadratic programming (SQP) based equivalent consumption minimum strategy (ECMS) (SECMS) for fuel cell hybrid electric vehicle (FCHEV) powered by fuel cell, battery and supercapacitor. In order to decrease hydrogen consumption and increase the durability of power sources, fuel cell is chosen as the main power source and supplies steady current, battery is designed as the main energy buffer and the replacement of fuel cell failure and supercapacitor is operated to supply peak power. Low energy density of supercpacitor lets its equivalent hydrogen consumption be taken as zero for many ECMS researches. This simplification leads to suboptimal fuel economy and complex of control system. SECMS considers hydrogen consumption of three power sources into objective function to solve this problem. A rule based control strategy (RBCS) and an hybrid ECMS operating mode control strategy (OMCS) (HEOS) are also designed to compare with SECMS. An experimental test bench is built to validate the comparative study of three strategies. The results show that compared with RBCS and HEOS, hydrogen consumption of SECMS decreases of 2.16% and 1,47% respectively and it also has the most smooth fuel cell current, which means a lowest fuel cell degradation.

Rule-based energy management for dual-source electric buses extracted by wavelet transform

Abstract This paper aims at studying the energy management of the dual-source electric bus. Based on the characteristics of the power demand and the energy storage system of the dual-source electric bus, a rule-based control strategy based on wavelet transform is proposed to allocate the energy flow among the energy units to obtain efficient energy transfer. The high and low frequency component power demand are decomposed and reconstructed by wavelet transform theory. The high frequency component power is assigned to the super capacitor, and the low frequency one is assigned to the battery pack or power grid, which can obviously reduce the damage, caused by the power rapid change and surge load, to the battery pack and power grid. The effectiveness of the proposed method is evaluated by the benchmark solved by dynamic programming under both a real driving cycle and the standard China bus driving cycle. Simulation experiments are carried out and the results show that the average power consumption is 0.9335 kWh/km, which has only a difference of 8.25% compared with the benchmark under a real driving cycle. In addition, a 9.02% difference is realized under the standard China bus driving cycle. Finally, the hardware-in-loop experiment is performed and the results show that the proposed strategy is reasonable.

Rule-corrected energy management strategy for hybrid electric vehicles based on operation-mode prediction

Abstract The energy crisis and exhaust emissions are serious problems that are largely related to road traffic. One solution to these threats is to switch from traditional gasoline-based vehicles to hybrid electric vehicles (HEVs) or electric vehicles (EVs), which also has the benefit of promoting a more sustainable economy. Energy management strategies (EMS) for HEVs or EVs play an important role in improving fuel economy. As a stochastic prediction method, a Markov chain, has been widely used in the prediction of driving conditions, but the application of a Markov chain in the prediction of HEV-operating modes in a rule-based EMS has rarely been presented in the literature. In addition, the threshold selection of rule-based EMS is usually based on experience and it is difficult to achieve optimal performance. In this paper, the impact of operation-mode prediction on rule-based EMS fuel economy has been explored to achieve real-time on-line corrections to motor and engine torque and to enhance their capacity for on-line optimization. Thus, a new EMS for HEV has been proposed based on operation-mode prediction using a Markov chain, which determines the on-line correction of torque distribution between the engine and the electric motor. The Markov decision processes and transition matrix are introduced first, and then, the transition probability matrix and torque correction model are established using the MATLAB/Simulink platform. The results of the simulation show that the proposed approach provides a 13.1% and 9.6% improvement in real fuel consumption under the New European Driving Cycle and the Urban Dynamometer Driving Schedule respectively, in comparison with the conventional rule-based control strategies without operation-mode prediction. The results also show that the proposed control strategy can significantly enhance the real-time optimization control performance of the EMS while maintaining the state of charge within a reasonable range.

State-based Volt/VAR control strategies for active distribution networks

Voltage limit violation represents a crucial power system problem, and many Volt/VAR control (VVC) strategies have been proposed to tackle violations. However, no comprehensive VVC strategy is available for use under all possible system operating conditions. This paper presents a generic solution to the VVC problem of the systems with wind-based renewable distributed generations (DGs) with the primary goal of determining an optimal control strategy based on system status, identified from system voltages. Each state-based strategy has individual state-related objective functions and control devices, including minimization of power losses, operational control costs, and voltage deviation. For both normal-state and intermediate-state operations, the optimization algorithm is implemented, but for emergency states, to be able to take action quickly, a new rule-based control strategy is proposed. Modified IEEE 34 and 123 bus test feeders are employed to validate the algorithm’s accuracy under consideration of stochastic nature of wind-based DGs.

SRE: Semantic Rules Engine for the Industrial Internet-Of-Things Gateways

The advent of the Internet-of-Things (IoT) paradigm has brought opportunities to solve many real-world problems. Energy management, for example, has attracted huge interest from academia, industries, governments, and regulatory bodies. It involves collecting energy usage data, analyzing it, and optimizing the energy consumption by applying control strategies. However, in industrial environments, performing such optimization is not trivial. The changes in business rules, process control, and customer requirements make it much more challenging. In this paper, a semantic rules engine (SRE) for industrial gateways is presented that allows implementing dynamic and flexible rule-based control strategies. It is simple, expressive, and allows managing rules on-the-fly without causing any service interruption. Additionally, it can handle semantic queries and provide results by inferring additional knowledge from previously defined concepts in ontologies. SRE has been validated and tested on different hardware platforms and in commercial products. Performance evaluations are also presented to validate its conformance to the customer requirements.

Investigation of a Novel Coaxial Power-Split Hybrid Powertrain for Mining Trucks

Due to the different working conditions and specification requirements of mining trucks when compared to commercial passenger vehicles, better fuel efficiency of mining trucks could lead to more significant economic benefits. Therefore, investigating a hybrid transmission system becomes essential. A coaxial power-split hybrid powertrain system for mining trucks is presented in this paper. The system is characterized as comprising an engine, a generator (MG1), a motor (MC2), two sets of planetary gears, and a clutch (CL1). There are six primary operation modes for the hybrid system including the electric motor mode, the engine mode, the hybrid electric mode, the hybrid and assist mode, the regenerative mode, and the stationary charging mode. The mathematical model of the coaxial power-split hybrid system is established according to the requirements of vehicle dynamic performance and fuel economy performance in a given driving cycle. A hybrid vehicle model based on a rule-based control strategy is established to evaluate the fuel economy. Compared with the Toyota Hybrid System (THS) and the conventional mechanical vehicle system using a diesel engine, the simulation results based on an enterprise project indicate that the proposed hybrid system can enhance the vehicle’s fuel economy by 8.21% and 22.45%, respectively, during the given mining driving cycle. The simulation results can be used as a reference to study the feasibility of the proposed coaxial hybrid system whose full potential needs to be further investigated by adopting non-causal control strategies.

Equivalent Consumption Minimization Strategy Based on Dynamic Programming for Plug-in Hybrid Electric Vehicle

Equivalence factor plays a key role in equivalent consumption minimization strategy and due to its strong relevance with driving cycle, the change of driving cycle can be reflected by the equivalence factor and at the same time different driving cycle has different equivalence factor. In this paper, the optimal equivalence factor as one of the control variables is calculated by dynamic programming. The three initial maps of the optimal equivalence factor concerning state of charge and power demand are formulated over NEDC, UDDS, 10-15 mode cycle respectively. These maps are fused together to obtain the final one for simulation analysis. Compared with the rule-based control strategy, the proposed method has a better fuel economy over NEDC, UDDS, 10-15 mode cycle.