Rule-based Control Strategy Research Articles

MPGA-based-ECMS for energy optimization of a hybrid electric city bus with dual planetary gear

To improve the fuel economy and reduce the exhaust emissions of a hybrid electric city bus (HECB) with dual planetary gear, a vehicle model is proposed based on the coupling mechanism between engine and battery motor in the gear. Then, two kinds of adaptive equivalent consumption minimization strategy (ECMS) algorithms based on fuzzy proportional-integral (PI) controller: Fuzzy PA-ECMS and Fuzzy MPGA-ECMS (MGPA: multiple population genetic algorithm), are established to improve the control effect of ECMS with equivalent factor (EF) as the core. Firstly, an approximate expression of optimal EF is derived based on the Pontryagin’s minimum principle (PMP). Subsequently, the deviation between the reference state of charge (SOC) and the actual SOC and the corresponding variation rate are calculated, which are combined with the fuzzy logic controller to adjust the EF. Finally, the driving style is introduced to rectify the trajectory of EF. According to different driving conditions (different initial values of SOC), the PI parameters of EF are optimized offline by multiple population genetic algorithm. Meanwhile, the interval of PI parameters is continuously optimized by the fuzzy controller. Then, simulation experiments are conducted to verify the efficacy of the two proposed algorithms. The simulation results show that compared to the conventional bus and rule-based control strategy, the proposed Fuzzy PA-ECMS algorithm can improve the fuel economy of bus by 41.70% and 5.29%, respectively. Further, compared to Fuzzy PA-ECMS, Fuzzy MPGA-ECMS algorithm can improve the fuel economy of bus by 0.3%.

An innovative thermal management method for cooling loop of electric driving system for durable and high efficiency electric vehicle

This paper proposed an innovative thermal management framework and its control strategies for the cooling loop of electric drive to achieve a more refined thermal management. To investigate the performance of this method used in EVs, the proposed innovative framework model and the conventional framework of the cooling loop of electric drive were developed in KULI based on the heating principle and heat transfer theory. And the rule-based control strategies were established in MATLAB/Simulink, which was set as a controller to regulate fan, pump and proportional valve. Then, the cooling performance and the energy consumption of the two frameworks were compared through co-simulation under steady-state and transient conditions of high temperature. The results showed that this method has a better performance in temperature control of key components (exit temperature of motor is reduced by at least 5.0 °C under 3 steady-stage simulation conditions) and improved efficiency of the pump (from 6.4% to 10.1% in mode 1, from 15.2% to 23.6% in mode 2, and from 21.4% to 36.8% in mode 3), which is useful to achieve long lifetime and high efficiency of electric vehicles.

Research on Fuzzy Control Strategy and Genetic Algorithm Optimization for Parallel Hybrid Electric Vehicle

In this paper, the fuzzy control strategy for parallel hybrid electric vehicle(HEV) is proposed. Based on fuzzy logic theory, SOC and torque demand scale factor are taken as the input, a fuzzy logic controller is designed. Combining with the rule-based control strategy(RBCS), the engine and motor output torque are gotten, so the whole energy control strategy is proposed. Then the genetic algorithm(GA) is used to get the optimal results: fuel consumption and emission level, to verify the accuracy of fuzzy control strategy.

A Strategy to Maximally Utilize Outdoor Air for Indoor Thermal Environment

In order to reduce the energy consumption of HVAC systems in buildings, the use of energy-saving solutions is necessary. One of these solutions is ventilation, which is usually used for maintaining acceptable indoor air quality and thermal comfort. As the change in outdoor environment is unpredictable and the occupant control is spontaneous, it is critical to control the windows and HVAC systems to achieve a maximum use of outdoor air for indoor ventilation. A new rule-based control strategy that could change the opening factor of windows is proposed in this study and its effectiveness was tested in five representative climates, ranging from a subtropical region to a severely cold region. A building model was set up and the indoor air temperature and energy consumption were predicted using EnergyPlus. The results show that the proposed control strategy can utilize ventilation to maintain a comfortable indoor environment with an annual uncomfortable percentage in an occupied period lower than 5%, thus leading to an energy-saving rate of 13.5–55.6%. The simulation results indicate that there are periods of ventilation available during the summer in climate zones with hot summers and warm winters, whereas the control strategy has a better energy-saving performance in temperate areas. This study conducted a preliminary exploration for practical applications of the combined operation of controllable natural ventilation and HVAC systems in buildings.

Development of a Soft Actor Critic deep reinforcement learning approach for harnessing energy flexibility in a Large Office building

This research is concerned with the novel application and investigation of ‘Soft Actor Critic’ based deep reinforcement learning to control the cooling setpoint (and hence cooling loads) of a large commercial building to harness energy flexibility. The research is motivated by the challenge associated with the development and application of conventional model-based control approaches at scale to the wider building stock. Soft Actor Critic is a model-free deep reinforcement learning technique that is able to handle continuous action spaces and which has seen limited application to real-life or high-fidelity simulation implementations in the context of automated and intelligent control of building energy systems. Such control techniques are seen as one possible solution to supporting the operation of a smart, sustainable and future electrical grid. This research tests the suitability of the technique through training and deployment of the agent on an EnergyPlus based environment of the office building. The agent was found to learn an optimal control policy that was able to minimise energy costs by 9.7% compared to the default rule-based control scheme and was able to improve or maintain thermal comfort limits over a test period of one week. The algorithm was shown to be robust to the different hyperparameters and this optimal control policy was learnt through the use of a minimal state space consisting of readily available variables. The robustness of the algorithm was tested through investigation of the speed of learning and ability to deploy to different seasons and climates. It was found that the agent requires minimal training sample points and outperforms the baseline after three months of operation and also without disruption to thermal comfort during this period. The agent is transferable to other climates and seasons although further retraining or hyperparameter tuning is recommended.

Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building

In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building’s setpoint deviation improved for 87% of time, on average, by 0.53 °C compared to the current deviations.

Assessing the energy flexibility of building clusters under different forcing factors

Given the increasing penetration of variable renewable resources in energy networks, future buildings should become flexible, i.e., able to modify their energy demands in response to external forcing factors to pursue specific goals. Furthermore, strong interaction among interconnected prosumer buildings and energy systems will require flexible energy management strategies at cluster level. Accordingly, this study implements the IEA EBC Annex 67 approach and proposes a quantification methodology to assess the energy flexibility performance of building clusters. The availability of renewable energy sources and carbon intensity in the energy mix are selected as forcing factors and a rule-based control scheme is applied to the space heating set-point to exploit thermal storage in the building structure. Specific indicators are defined to quantify flexibility as (i) a reduction in energy demand not covered by renewables or (ii) a percentage decrease in carbon emissions enabled by smart control of the cluster, compared to a reference scenario. The methodology is applied to four cluster configurations characterized by different building thermal mass levels, modeled in Dymola dynamic simulation environment. Results show that smart operation for simulated clusters enables an improvement of renewable energy usage (up to a 13% saving of the residual annual heating demand), and up to an 18% reduction of energy-related carbon emissions. The suggested quantitative assessment and indicators represent valuable support for building designers to easily compare multiple technological solutions and design strategies in terms of energy flexibility.

Energy Management Using a Rule-Based Control Strategy of Marine Current Power System with Energy Storage System

With the rapid development of renewable energy technology, marine current energy is treated as the most desirable form of ocean energies. Due to the nature of marine current energy, simple structure, high reliability, and good control performance are the primary consideration for the energy management strategy. This paper proposes an energy management control strategy based on rules to compensate for the fluctuating power caused by tidal motion. The hybrid energy storage system composed of vanadium redox flow battery (VRB) is applied to reallocate power. Supercapacitor banks (SCBs) are applied as the auxiliary power source to absorb or release the required power according to energy management strategy based on control rules in the marine current power system. SCB makes the grid-connected power track the grid command power and also improves the operational efficiency of the vanadium redox flow battery (VRB). VRB compensates for the low-frequency fluctuating power caused by tidal motion and plays an important role in compensating for the difference in power between the grid-connected power and the grid command power to ensure the reliability of the marine current power system. A simulation model of a 3 MW marine current power system is built to verify the effectiveness of the energy management strategy based on the real marine current velocity data.

Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings

Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting their energy demand to meet specific grid requirements. However, in most cases, the energy flexibility of a single building is typically too small to be exploited in the flexibility market, highlighting the necessity to perform analysis at a multiple-building scale. This study explores the economic benefits associated with the implementation of a Reinforcement Learning (RL) control strategy for the participation in an incentive-based demand response program of a cluster of commercial buildings. To this purpose, optimized Rule-Based Control (RBC) strategies are compared with a RL controller. Moreover, a hybrid control strategy exploiting both RBC and RL is proposed. Results show that the RL algorithm outperforms the RBC in reducing the total energy cost, but it is less effective in fulfilling DR requirements. The hybrid controller achieves a reduction in energy consumption and energy costs by respectively 7% and 4% compared to a manually optimized RBC, while fulfilling DR constraints during incentive-based events.

Design and analysis of a hybrid electric powertrain for military tracked vehicles

Electrification of military vehicles offers the potential for extended stealth operation, enhanced vehicle performance, and onboard electric power. This study proposes a hybrid electric powertrain for a military tracked vehicle with hybrid energy storage (battery and capacitor) and multi-speed transmission. Initially, component sizing of the proposed powertrain and a series hybrid electric powertrain was done based on power and torque analysis of vehicle performance requirements on different operating terrains. A rule-based control strategy was then developed for the proposed powertrain to maintain high performance as well as reduce energy losses. The performance of the proposed powertrain and the series configuration was evaluated using AVL Cruise software over synthesized drive cycles that represent the real-world activities of military tracked vehicles. Comparative analysis during component sizing showed that the proposed powertrain provides a reduction in powertrain mass by 389 kg (2.72%). From the performance analysis, it was observed that the fuel economy of the proposed powertrain improved by almost 30.27% as compared to the series configuration while maintaining vehicle performance.

Implementation of model predictive control in a large-sized, low-energy office building

Modern and energy-optimized buildings often lack an intelligent and advanced control strategy. Instead, conventional rule-based control (RBC) strategies are still mainly used today, which do not exploit the full performance potential of these buildings. Model predictive control (MPC) has proven in simulation studies and pilot cases to be a promising approach to reduce the energy consumption of buildings, while improving occupants’ comfort. However, there is still a lack of implementing MPC in real, large-scale and fully occupied buildings, to further prove this potential in real building operations. This paper describes the implementation and operation of MPC in a large-sized, low-energy office building. The MPC controller was implemented in a section of the building during a three-month test period from February to April 2020, controlling the supply temperature of heating circuits for thermally activated building systems (TABS). Its performance was compared to the default rule-based control which is active in the other building sections. This allows for a detailed evaluation of MPC versus RBC under identical environmental and operational conditions. The MPC controlled building section used 30% less heating energy than RBC controlled building sections, while the existing high level of thermal comfort could be maintained. Especially in transition periods (i. e. interseasonal periods like late winter/early spring) , the MPC is superior to the conventional heating-curve based control strategy, with heating energy savings of 75%.

Optimal Peak Shaving Control Using Dynamic Demand and Feed-In Limits for Grid-Connected PV Sources With Batteries

Peak shaving of utility grid power is an important application, which benefits both grid operators and end users. In this article, an optimal rule-based peak shaving control strategy with dynamic demand and feed-in limits is proposed for grid-connected photovoltaic (PV) systems with battery energy storage systems. A method to determine demand and feed-in limits depending on the day-ahead predictions of load demand and PV power profiles is developed. Furthermore, an optimal rule-based control strategy that determines day-ahead charge/discharge schedules of battery for peak shaving of utility grid power is proposed. The rules are formulated such that the peak utility grid demand and feed-in powers are limited to the corresponding demand and feed-in limits of the day, respectively, while ensuring that the state-of-charge (SoC) of the battery at the end of the day is the same as the SoC of the start of the day. The optimal inputs required for applying the proposed rule-based control strategy are determined using a genetic algorithm for minimizing peak energy drawn from the utility grid. The proposed control algorithm is tested for various PV power and load demand profiles using MATLAB.

Cooling system energy flexibility of a nearly zero-energy office building using building thermal mass: Potential evaluation and parametric analysis

Building energy flexibility has been considered as a key resource in achieving reliable energy systems. The nearly zero-energy buildings are low-energy and usually equipped with self-production energy systems, which are developing rapidly and play an important role in the energy network. This work aims to investigate the cooling system energy flexibility using building thermal mass in nearly zero-energy office buildings. A simulation-based methodology is proposed to obtain and evaluate the energy flexibility, including a rule-based control strategy and quantification method. The methodology is applied to assess the energy flexibility of a nearly zero-energy office building in Beijing. The influence of different structural parameters, internal heat gains, and cooling terminals on the cooling system energy flexibility performance is considered. Compared with the traditional night set-back control, the peak power and the peak energy can be curtailed by 55.6% and 54.0% in the investigated building under the rule-based control, respectively. The parametric analysis shows that the flexibility realized from thermal mass is highly dependent on the total structural thermal capacity, internal heat gains, and types of cooling terminals. However, the external wall insulation level is a non-significant parameter influencing the cooling system energy flexibility in nearly zero-energy office buildings.

Mitigation of sulfation in lead acid battery towards life time extension using ultra capacitor in hybrid electric vehicle

Batteries act as one of the primary sources of energy for high power Hybrid Electric Vehicle (HEV). The life of the battery becomes a significant constraint while building an HEV. So researchers found Lithium-ion batteries are more suitable for HEV with a better lifecycle. But the manufacturing cost of Lithium-ion batteries is expensive. Thus, while designing an economic HEV, the cost of batteries also a constraint. In subsequent years, lead-acid batteries are found one of the best alternatives for lithium-ion. Sulfation is the main problem in lead-acid batteries. So de-sulfation is a solution to recover the sulphated lead-acid battery. But de-sulfation was not found as a better solution for preventing sulfation. Hence battery management system for proper charging or discharge is found as a passive solution for the sulfation. In literature, many methods are reviewed related to proper charging and a discharging controller which may trap in sulfation problem. Hence, this paper, an Atom Search Algorithm (ASA) based Hybrid Energy Storage System (HESS) is designed to enable proper charging and discharging controller for increasing the lifecycles of the lead-acid battery by avoiding sulfation. The lead-acid battery is connected with Ultra-Capacitor (UC) through a bidirectional DC-DC converter to enable proper charging and to discharge of controller in a. The lifetime extension of lead-acid battery is attained by maintaining the proper charging and discharging through the conservation of Depth of Charge (DOC) and State of Charge (SOC). The charging and discharging controller of the lead-acid battery are enabled by a rule-based control strategy in the converter. The optimal operation of the converter is to provide essential supply to meet the load drive cycle as well as battery charging progress. To avoid battery's dry conditions, UC is playing the leading role to supply by the converter. The controller action is performed through the consumption of the Fractional-Order Proportional Integral Derivative Controller (FOPID) in the bidirectional DC-DC converter. The optimal switching operations are selected with the utilisation of the ASA algorithm. The proposed method is implemented in MATLAB/ Simulink and contrasted with existing methods. Finally, the proposed method extends the lifecycles of the battery to 7500 cycles by proper charging and discharging controller.

Recommendations for model identification for MPC of an all-Air HVAC system

Rule-based control (RBC) strategies are often unable to execute the optimal control action, which leads to unnecessary energy consumption and suboptimal comfort. Model predictive control (MPC) is a dynamic control strategy for heating, ventilation and air-conditioning (HVAC) systems that is mostly more capable of performing optimal control actions. The identification process of predictive models is an essential aspect of MPC. However, this model identification process remains time consuming due to the large variation in buildings and systems. The aim of this paper is to determine guidelines to identify predictive grey-box models more time efficient, thus enhancing the applicability of MPC. This paper focusses on a case study building equipped with an all-air HVAC system, which combines ventilation, heating and cooling. Making both temperature and CO2-concentration key parameters to predict. The grey-box model represents an open zone in a landscaped office, making the influence of neighbouring zones an additional challenge. Different models for predicting the zone temperature and CO2-concentration are identified, evaluated and validated using CTSM-R. The following aspects are studied: the dataset size, the influence of neighbouring zones, the difference between winter and summer conditions, number of states and the prediction horizon. A three state RC-model with the implementation of the zone temperature of one neighbouring zone is preferred for predicting the indoor temperature with an acceptable prediction horizon of one day. However, this temperature model is not suitable during sunny periods. A simple model representing a mass balance obtains accurate predictions of the zone CO2-concentration for a timestep of 15 minutes. For both model types the utilization of 5-day datasets is favoured over 12-day datasets due to a shorter monitoring period, lower prediction error and an easier parameter convergence. The usage of 12-day datasets is only preferred when an accurate estimation of the thermal inertia is pursued.

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

The energy management strategy of hybrid electric vehicles is of significant importance to improve the fuel economy. In this regard, two energy management strategies are designed for power-split hybrid electric city bus (HECB), which are based on the linear time-varying stochastic model predictive control (LTV-SMPC) and stochastic model predictive control based on Pontriagin’s minimum principle (PMP-SMPC). In the present study, the Markov chain and long short-term memory (LSTM) forecast demand torque and velocity respectively are applied to establish a combination forecast model. Then several processes, including linear approximation, processing simplified control model, the proposed nonlinear vehicle model is converted into a linear time-varying model. Meanwhile, the energy management problem in a linear quadratic programming problem is solved. Considering linearization error and limitations of the quadratic optimization, Pontriagin’s minimum principle (PMP) is applied to optimize the nonlinear model predictive control. Based on the reference theory, the range of coordinated variables is derived, and the optimal coordination variable is searched by dichotomy to realize the rolling optimization of the model predictive control (MPC). Finally, the effectiveness of the proposed energy management strategy is verified by simulating several case studies. Obtained results show that compared with the rule-based (RB) control strategy, the fuel economy of LTV-SMPC and PMP-SMPC increases by 8.79% and 14.42%, respectively. Meanwhile, it is concluded that the two strategies have real-time computing potential.

Rule-Based Control Strategy to Increase Photovoltaic Self-Consumption of a Modulating Heat Pump Using Water Storages and Building Mass Activation

The use of photovoltaic (PV) energy in combination with heat pump systems for heating and cooling of residential buildings can lead to renewable energy self-consumption, reducing the energy required from the grid and the carbon footprint of the building uses. However, energy storage technologies and control strategies are essential to enhance the self-consumption level. This paper proposes and analyzes a new control strategy for the operation of a modulating air-source heat pump, based on the actual PV availability. The solar energy surplus is stored as thermal energy by the use of water tanks and the activation of the thermal capacitance of the building. The efficacy of the control strategy is evaluated considering different rule-based strategies, and different boundary conditions. The effect of climate data, building insulation level and thermal inertia are investigated and compared. The results show the efficacy of the proposed strategy to decrease up to 17% the amount of electricity purchased from the grid and to increase the self-consumption by 22%, considering a high-insulated building in Bolzano, Northern Italy. The thermal mass activation is found effective to increase the self-consumption of the system. Nonetheless, the achievable energy reduction depends largely on the building characteristics and the boundary conditions.

Real-Time Volt/Var Control in Active Distribution Networks With Data-Driven Partition Method

The penetration of photovoltaics (PVs) and electric vehicles (EVs) is increasing in active distribution networks (ADN), which may lead to severe voltage violation problems. This paper proposes a two-stage real-time Volt/Var control method to mitigate fast voltage violations. In the first stage, hourly on-load tap changer (OLTC) and capacitor banks (CBs) are scheduled based on the optimal power flow method. The optimization problem is formulated as a mixed-integer second-order cone programming (MISOCP) which can be effectively solved. In the second stage, a data-driven network partition method is proposed to select critical bus and assess the voltage violation risk of each control area, followed by the intra-day dispatch of EVs. Based on the partition results, reactive power of PVs and EVs is controlled in real-time to mitigate voltage violations. Considering the suddenly active power drop of PVs, a rule-based control strategy coordinating PVs and CBs is proposed to improve the reactive power reserve in ADN. The proposed approach is tested on the IEEE 33-bus and 123-bus distribution networks and simulation results verify the effectiveness both in network partition and real-time voltage control.

Impact of Wall Constructions on Energy Performance of Switchable Insulation Systems

This paper evaluates the potential energy savings when switchable insulation systems (SIS) are applied to walls of residential buildings located in Belgium and other locations in Europe. The study considers two low-energy prototypical dwellings (an apartment and a detached house) that are representative of post-2010 constructions and renovations in Belgium. Using an 3R2C-based analysis tool, the performance of both dwellings is evaluated with static and dynamic wall insulation systems. First, the switchable insulating system is described along with its associated simple 2-step rule-based control strategy. Then the modeling strategy and simulation analysis tools are presented. In Belgium, it was found that SIS-integrated walls allow energy savings up to 3.7% for space heating and up to 98% for cooling. Moreover, it was found that to further reduce the energy consumption of SIS-integrated buildings in various European climates, thermal mass placement needs to be considered. By optimizing the placement and the parameters of the various wall layers, it is possible to increase the space heating savings by up to a factor of 4 and those of cooling by up to a factor of 2.5.

Multistage Time-Optimal Control for Synchronization Process in Electric-Driven Mechanical Transmission With Angle Alignment Considering Torque Response Process

Abstract The synchronization process takes up almost one half of the time of the gear-shifting process, and also influences the impacts between the sleeve and the gear ring. To avoid impacts and reduce time duration, a time-optimal control strategy with angle alignment is necessary for the synchronization process. Moreover, to be better accord with practice, the motor torque response process should be taken into account. The parameters in the torque response process depend on control commands, which makes the control problem a multistage one. To solve these issues, a rule-based control strategy is extracted from the dynamic programming (DP) solution of the multistage time-optimal control problem. To obtain this strategy, the dynamic model for the synchronization process with a modified Sigmoid model to precisely depict the torque response process is first solved. Then, the control problem is formulated as a multistage time-optimal control problem with three states and solved by DP. Based on the DP results, a three-stage and a four-stage rule-based control strategies are extracted for normal operation situation and startup situation, respectively. Finally, through comparative studies, the proposed rule-based control strategy can eliminate the speed difference and angle difference simultaneously with almost the same time of the bang–bang control, while the bang–bang control cannot obtain the zero terminals. Moreover, the proposed control strategy only takes 20 ms more than the pure speed synchronization control in the worst case. It would decrease when the initial speed difference increases.