Real-time Control Strategy Research Articles

Real‐time control algorithm for minimising energy consumption in parallel hybrid electric vehicles

In this work, a real-time energy management problem for a parallel hybrid electric vehicle (HEV) is proposed. The considered powertrain is built from a commercial HEV model. First, a non-linear optimal control problem under model predictive control scheme is formulated. The designed controller aims to generate the optimal power split and gear ratio schedule with respect to minimise the energy consumption of fuel and electricity. Moreover, the multiple shooting algorithm is introduced to decouple the dynamic constraints with the ability of avoiding the strong non-linearity while solving the optimisation problem. After that the optimisation problem is solved using sequential quadratic programming solver. Then, to evaluate the performance of the proposed real-time optimisation strategy on different traffic scenarios, the controller is applied to an adaptive cruise control (ACC) under connected environment. In this case, a solution of ACC with consideration of minimising energy consumption and maintaining string stability is provided. Finally, the proposed controller can be implemented in the traffic-in-the-loop platform without the knowledge of the predefined driving route. Simulations reveal that the proposed real-time control scheme shows great optimisation performance under the designed scenarios.

A Multiscale-Grid-Based Stacked Bidirectional GRU Neural Network Model for Predicting Traffic Speeds of Urban Expressways

In recent decades, studies on short-term traffic speed forecasting of the large-scale road are a new challenge for researchers and engineers. Especially based on deep learning neural networks, studies on short-term traffic forecasting have achieved mush-room growth. This study proposes a stacked Bidirectional Gated Recurrent Unit neural network model to predict the traffic speed of the expressway over different estimation time intervals in an effective manner. By building a multiscale-grid model, it can take less time to derive a set of key traffic parameters of different scales to predict traffic speed of the various-scale road. The speed prediction of small-scale sections can cover more detailed road spatial features preparing for Vehicle Navigation System, and the speed prediction of large-scale sections can establish the real-time traffic control strategies. In order to validate the effectiveness of the proposed model, we use the floating car data, with an updating frequency of 1 minute from the urban freeway of Beijing, for model training and testing. The experimental results show that the stacked BiGRU network with the multiscale-grid model enables to capture the spatial-temporal characteristics of traffic speed efficiently. Furthermore, the BiGRU with two layers (BiGRU-2L) outperforms benchmark models in the prediction of the traffic speed, which presents a significant advantage in reducing the overfitting problem, decreasing the excessive time-consuming and improving the effective use of limited computation resources.

Real-Time Optimal Control Strategy for Multienergy Complementary Microgrid System Based on Double-Layer Nondominated Sorting Genetic Algorithm

Because of the problems of low operation efficiency and poor energy management of multienergy input and output system with complex load demand and energy supply, this paper uses the double-layer nondominated sorting genetic algorithm to optimize the multienergy complementary microgrid system in real-time, allocating reasonably the output of each energy supply end and reducing the energy consumption of the system on the premise of meeting the demand of cooling, thermal and power load, so as to improve the economy of the whole system. According to the system load demand and operation mode, the first layer of this double-layer operation strategy calculates the power required by each node of the microgrid system to reduce the system loss. The second layer calculates the output of each equipment by using nondominated sorting genetic algorithm with various energy values calculated in the first layer as constraint conditions, considering the operation characteristics of various equipment and aiming at economy and environmental protection. In this paper, a typical model of energy input-output is established. This model combines with the operation control strategy suitable for multienergy complementary microgrid system, considers the operation mode and equipment characteristics of the system, and uses a double-layer nondominated sorting genetic algorithm to optimize the operation of each equipment in the multienergy complementary system in real time, so as to reduce the operation cost of the system.

Simulating a combined lysis-cryptic and biological nitrogen removal system treating domestic wastewater at low C/N ratios using artificial neural network

In this study, a combined alkaline (ALK) and ultrasonication (ULS) sludge lysis-cryptic pretreatment and anoxic/oxic (AO) system (AO + ALK/ULS) was developed to enhance biological nitrogen removal (BNR) in domestic wastewater with a low carbon/nitrogen (C/N) ratio. A real-time control strategy for the AO + ALK/ULS system was designed to optimize the sludge lysate return ratio (RSLR) under variable sludge concentrations and variations in the influent C/N (⩽ 5). A multi-layered backpropagation artificial neural network (BPANN) model with network topology of 1 input layer, 3 hidden layers, and 1 output layer, using the Levenberg–Marquardt algorithm, was developed and validated. Experimental and predicted data showed significant concurrence, verified with a high regression coefficient (R2 = 0.9513) and accuracy of the BPANN. The BPANN model effectively captured the complex nonlinear relationships between the related input variables and effluent output in the combined lysis-cryptic + BNR system. The model could be used to support the real-time dynamic response and process optimization control to treat low C/N domestic wastewater.

A real time control strategy for improvement of autonomous operation in AC / DC micro grids using electric springs

A real time control strategy for improvement of autonomous operation in <scp>AC</scp> / <scp>DC</scp> micro grids using electric springs

Online learning with stability guarantees: A memory-based warm starting for real-time MPC

We propose and analyze a real-time model predictive control (MPC) scheme that utilizes stored data to improve its performance by learning the value function online with stability guarantees. For linear and nonlinear systems, a learning method is presented that makes use of basic analytic properties of the cost function and is proven to learn the MPC control law and the value function on the limit set of the closed-loop state trajectory. The main idea is to generate a smart warm start based on historical data that improves future data points and thus future warm starts. We show that these warm starts are asymptotically exact and converge to the solution of the MPC optimization problem. Thereby, the suboptimality of the applied control input resulting from the real-time requirements vanishes over time. Numerical examples show that existing real-time MPC schemes can be improved by storing optimization data and using the proposed learning scheme.

Vehicle Speed and Gear Position Co-Optimization for Energy-Efficient Connected and Autonomous Vehicles

This work proposes a real-time implementable control strategy to optimize vehicle speed and transmission gear position simultaneously for connected and autonomous vehicles (CAVs). Co-optimization of vehicle speed and transmission gear position has the advantage to maximize the fuel benefits. Drivability is considered during the optimization to satisfy the acceleration requirement and avoid shift busyness. The target vehicle's speed and gear position are controlled intelligently using predicted future traffic conditions based on information enabled by connectivity. The optimal control problem is a hybrid one with both continuous (vehicle speed) and discrete (gear position) control inputs. The problem is formulated and simplified to a mixed-integer programming problem with a convex quadratic objective function and mixed-integer linear constraints. The optimal control solutions are obtained in real time using an efficient numerical solver in the model predictive control (MPC) fashion. Future traffic conditions are anticipated using a traffic prediction method based on a traffic flow model. The traffic prediction method can be applied to scenarios where both connected vehicles and nonconnected vehicles share the road. As a case study, a vehicle platooning scenario on an urban road is evaluated in both simulation and experiment. The target vehicle is at the end of the vehicle platoon and follows the preceding vehicle. The average computational time of the optimization is 0.44 s. By co-optimizing vehicle speed and gear position, the target vehicle can achieve 10.6% fuel benefits compared with the immediate preceding vehicle and 8.9% energy benefits compared with a human-driven vehicle (driven by VISSIM's car-following model). The proposed control strategy can be potentially extended to various CAV applications and traffic scenarios as well.

Real Time Control of Rainwater Harvesting Systems: The Benefits of Increasing Rainfall Forecast Window

AbstractUse of real time control (RTC) technology in rainwater harvesting systems can improve performance across water supply, flood protection, and environmental flow provision. Such systems make the most of rainfall forecast information, to release water prior to storm events and thus minimize uncontrolled overflows. To date, most advanced applications have adopted 24‐hr forecast information, leaving longer‐term forecasts largely untested. In this study, we aimed to predict the performance of four different RTC strategies, based on different forecast lead time and preferred objectives. RTC systems were predicted to yield comparatively less harvested rainwater than conventional passive systems but delivered superior performance in terms of flood mitigation and delivery of environmental water for streamflow restoration. More importantly, using a 7‐day rainfall forecast was shown to enhance the ability of RTC in mitigating flood risks and delivering an outflow regime that is close to the natural (reference) streamflow. Such a finding suggests that RTC combined with 7‐day forecast can enhance the functionality of rainwater harvesting systems to restore and even mimic the entire natural flow regimes in receiving streams. This also opens up a new opportunity for practitioners to implement smart technology in managing urban stormwater in a range of contexts and for a range of stream health objectives.

Analyzing power and dynamic traffic flows in coupled power and transportation networks

The increasing penetration of electric vehicles is tightly coupling the operation of power and transportation systems. The representation of the dynamics of network flows in response to real-time information and control strategies is a problem of considerable practical significance. However, most of work considers static network models that are only suitable for long-term planning horizons. This paper seeks to develop an integrated modeling framework for the real-time analysis and operation of the coupled systems. In particular, a dynamic traffic assignment model is exploited to account for the time-varying travel demand and flow dynamics. An accelerated diagonalization algorithm is proposed to compute the traffic flows in an efficient manner. Moreover, we show that the equilibrium flow pattern of the coupled systems can be characterized by a fixed-point problem, thus motivating a decentralized approach to attain the flow solutions. Numerical studies demonstrate both the spatial and temporal interconnections between the power and traffic flows. The comparison of network flow solutions based on the dynamic, semi-dynamic and static transportation network models is presented.

Elimination of the Impact Produced by DG Units on the Voltage Profile of Distribution Networks

In this paper, an alternative strategy for real-time control of active distribution network voltage is developed, not by controlling the bus voltage as in the various centralized, decentralized, and local approaches presented in literature but rather by only eliminating the impact produced by active and reactive power of distributed generation (DG) units on the voltage of all network nodes and keeping the traditional voltage control systems dealing with the same constraints of passive systems. In literature, voltage deterioration introduced by DGs has been reported as one of the main obstacles for the interconnection of large amounts of DG units to the existing networks. In this paper, the novel control strategy is based on a sensitivity formula developed to calculate the compensation needed for additional distributed flexible AC transmission system (D-FACTS) devices to push and pull the exact reactive power and to eliminate the impact produced by DGs on the network voltage profile. The criteria of the allocation of the var devices and the required network reinforcement are developed in this paper, considering all possible topology structures, and an innovative codification method is introduced to reduce the needed computation time and communication data to actualize the sensitivity coefficients and get the proposed control approach flexible with network topology reconfiguration. The risk of the conflict of the proposed control system with the traditional voltage equipment is reduced due to the fast capability of D-FACTS devices to regulate their reactive power in finer granularity. A case study of two meshed IEEE 15-bus feeders is introduced to compare the voltage behavior with and without the presence of DG units and to evaluate the total system losses. The proposed method could be used for the interconnection of the first generation units in emerging networks, which does not yet have an active voltage control strategy, as it could be used for DG units not able to be connected to existing centralized control systems and it could also be used as the principal voltage control strategy, with the extension for several neighboring units and the preservation of the traditional voltage control systems.

Grid-aware distributed control of electric vehicle charging stations in active distribution grids

The penetration of electric vehicle (EV) charging stations (CSs), along with the progressive connection of stochastic distributed generation, is increasing the probability of violating the power distribution grid operational constraints and deteriorate the quality of power supply. To this end, the paper proposes a real-time control scheme for allocating power set-points to EV CSs while accounting for the grid operational requirements. In the proposed problem formulation the grid and the power injections are modelled accounting for their unbalanced 3-phase nature, thus enabling to formulate the problem objective and its constraints adopting the sequence decomposition. The EVs’ users need, along with the stochastic nature of other uncontrollable injections (e.g. loads and generation from photovoltaic generation units), are also taken into account. A distributed control scheme, with a minute-scale control horizon, is proposed where local controllers, operating at EV aggregation level, compute EV battery-secure power set-points. These controllers send their set-points to a central controller operating at the grid aggregation level. The central controller solves a scenario-based linearised optimal power flow accounting for grid operational and power quality constraints. Then, it sends back its solution to the respective local controllers. The obtained iterative algorithm is efficiently solved until convergence. We analyse the performance of the proposed control scheme via a simulation ran on the IEEE-34 feeder. Comparisons with two other control algorithms, a grid-unaware local controller and a myopic maximum power controller, are included to benchmark the proposed control scheme.

Adaptive neural-bias-sliding mode control of rugged electrohydraulic system motion by recurrent Hermite neural network

This paper deals with design and implementation of a real-time position control scheme based on the synergistic combination of a recurrent neural network, an integral sliding mode controller and a bias controller for a rugged electrohydraulic actuation system. The controller design is based on recurrent Hermite neural network comprising a single hidden layer with orthonormal Hermite polynomial basis functions as activation functions for each hidden neuron and an integral sliding surface as the input. The bias controller is designed as a hyperbolic tangent of the error. Additionally, an adaptive scheme has been formulated based on the Lyapunov criterion and its convergence has been established. The performance of the proposed scheme has been evaluated on a laboratory scale single-rod electrohydraulic actuation system with a large dead band (∼10%) proportional valve in real-time. The experimental results suggest a significant improvement in the position tracking performance of the system for conventional tracking trajectories compared to other established methodologies.

Hierarchical density-based clustering methods for tolling zone definition and their impact on distance-based toll optimization

Real-time network control strategies such as congestion pricing have been used in a number of metropolitan areas around the world for traffic congestion mitigation. Recent advances in Global Navigation Satellite System (GNSS) technology have led to increasing interest in distance- or usage-based road pricing as an effective alternative to traditional facility-, cordon- and area-based pricing that typically rely on fixed infrastructure. In this paper, we propose the use of feature-variant clustering methods, OPTICS and HDBSCAN*, as a systematic approach for tolling zone definition to operationalize distance-based tolling schemes. Subsequently, we develop a framework for predictive distance-based toll optimization to evaluate network performance for the various tolling zone definitions derived from the aforementioned feature-variant clustering methods. In this framework, for a specific tolling zone definition, tolling function parameters are optimized using a simulation-based Dynamic Traffic Assignment (DTA) model operating within a rolling horizon scheme. Predictive optimization is integrated with the guidance information generation. Behavioral models capture drivers’ responses to the tolls in terms of trip cancellation and choices of mode, route and departure time. Experiments on the real-world Expressway and Major Arterials network of Singapore demonstrate improved effectiveness of distance-based toll optimization given tolling zone definitions derived from feature-variant clustering, compared to fixed cordon-based pricing, adaptive cordon-based pricing, as well as distance-based pricing with ad hoc tolling zone definitions. Further, the results indicate that the use of the marginal link cost tolls as a clustering feature produces the most robust tolling zone definitions and yields significant improvements in social welfare over ad hoc zone definitions and cordon-pricing. Finally, experiments on the Boston CBD network also demonstrate the effectiveness of distance-based toll optimization schemes on urban traffic networks.

A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles

Abstract Hybrid energy storage systems have attracted more and more interests due to their improved performances compared with sole energy source in system efficiency and battery lifetime. This study aims to propose a real-time energy management control strategy for achieving these goals. The strategy is based on a combination of wavelet transform, neural network and fuzzy logic. Wavelet transform is an effective tool in extracting different frequency components of load power demand to match the characteristics of battery and supercapacitor. However, it is hard to be directly applied in a real-time system. For this, a neural network model, which is offline trained using the dataset obtained from the wavelet transform decomposition, is developed to online predict the low frequency power demand for the battery. Accordingly, the high frequency power demand is online calculated and distributed to the supercapacitor. In addition, a fuzzy logic based supervisory controller is further developed for controlling the supercapacitor voltage within a certain suitable range. Finally, a 72 V battery and 96 V supercapacitor hybrid energy storage system real-time hardware platform has been developed to validate the effectiveness of the proposed energy management control strategy.

Real-Time Model Predictive Powertrain Control for a Connected Plug-In Hybrid Electric Vehicle

The continued development of connected and automated vehicle technologies presents the opportunity to utilize these technologies for vehicle energy management. Leveraging this connectivity among vehicles and infrastructure allows a powertrain controller to be predictive and forward-looking. This paper presents a real-time predictive powertrain control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) in a connected vehicle environment. This work focuses on the optimal energy management of a multi-mode PHEV based on predicted future velocity, power demand, and road conditions. The powertrain control system in the vehicle utilizes vehicle connectivity to a cloud-based server in order to obtain future driving conditions. For predictive powertrain control, a Nonlinear Model Predictive Controller (NMPC) is developed to make torque-split decisions within each operating mode of the vehicle. The torque-split among two electric machines and one combustion engine is determined such that fuel consumption is minimized while battery SOC and vehicle velocity targets are met. The controller has been extensively tested in simulation across multiple real-world driving cycles where energy savings in the range of 1 to 4% have been demonstrated. The developed controller has also been deployed and tested in real-time on a test vehicle equipped with a rapid prototyping embedded controller. Real-time in-vehicle testing confirmed the energy savings observed in simulation and demonstrated the ability of the developed controller to be effective in a real-time environment.

Probabilistic State Forecasting and Optimal Voltage Control in Distribution Grids under Uncertainty

Nowadays, the uncertainty in distribution systems rises, notably due to an increasing share of solar panels and electric vehicles whose power production and consumption are characterized by a high volatility. This poses challenges to distribution system operators to ensure stable and secure operation of their grid. Hence, an optimal integration of these distributed energy resources in real-time control schemes inevitably relies on appropriate forecasts of the near-future system state. This paper investigates the short-term probabilistic state prediction of low-voltage grids for operation purposes. The performance of two quantile forecasting algorithms is evaluated for different levels of distributed energy resources penetration and availability of measurements. Quantile forecasts are finally integrated into the framework of an optimization problem that aims at minimizing the costs associated with overvoltages by suitable solar power curtailment. The advantages of quantile forecasts considering different imbalance prices are demonstrated.

Real-Time Optimal Torque Control of Interior Permanent Magnet Synchronous Motors Based on a Numerical Optimization Technique

This study presents a real-time optimal torque control scheme for interior permanent magnet synchronous motors (IPMSMs). The proposed scheme enables computation of optimal current reference for a torque reference under all operating regions, including the maximum torque per ampere (MTPA), flux weakening (FW), maximum current (MC), and maximum torque per voltage (MTPV), using numerical optimization techniques to simplify the problems and obtain the corresponding solutions with reduced computation burden. Linearizing the torque equation of IPMSM is utilized to derive solutions for the MTPA and FW. These solutions are proved to converge to their optimum with the aid of the reference smoother. Numerical solutions for the MC and MTPV are derived based on the assumption that the resistive voltage drop is small enough but not zero. These solutions differ from the existing solutions that ignore the resistive voltage drop. The effectiveness of the proposed method is numerically and experimentally verified using a 7.5-kW IPMSM with a considerable reduction in computational time of approximately 90% in the simulation compared with the analytic solution.

Adaptive regression model-based real-time optimal control of central air-conditioning systems

Model-based, real-time optimal control is an effective tool to improve the energy efficiency of central air-conditioning systems. However, its performance relies heavily on the accuracy of the system models, whereas the development of accurate models for central air-conditioning systems is not easy due to their complex dynamics and non-linearities. This study presents an adaptive regression model-based real-time optimal control strategy for central air-conditioning systems. In the proposed strategy, regression models are adopted to describe the relationship between the power consumption of the system and the variables that are optimised. Their simple structures enable a low computation load for model updating and real-time optimisation. The length of the training data (for model updating) is investigated, and a suitable length is found using a similarity check-based method. Case studies were carried out to assess the performance of the proposed strategy, and they demonstrated that (1) a week was the optimal length of the training data for the case system, (2) the proposed strategy saved energy use by 3.48–10.59% when compared with a benchmark system with no optimisation, and (3) the proposed method reduced the computational load by 85% when compared with a simplified physical model-based optimal control without adaptive modelling.

Real-Time Building Smart Charging System Based on PV Forecast and Li-Ion Battery Degradation

This paper proposes a two-stage smart charging algorithm for future buildings equipped with an electric vehicle, battery energy storage, solar panels, and a heat pump. The first stage is a non-linear programming model that optimizes the charging of electric vehicles and battery energy storage based on a prediction of photovoltaïc (PV) power, building demand, electricity, and frequency regulation prices. Additionally, a Li-ion degradation model is used to assess the operational costs of the electric vehicle (EV) and battery. The second stage is a real-time control scheme that controls charging within the optimization time steps. Finally, both stages are incorporated in a moving horizon control framework, which is used to minimize and compensate for forecasting errors. It will be shown that the real-time control scheme has a significant influence on the obtained cost reduction. Furthermore, it will be shown that the degradation of an electric vehicle and battery energy storage system are non-negligible parts of the total cost of energy. However, despite relatively high operational costs, V2G can still be cost-effective when controlled optimally. The proposed solution decreases the total cost of energy with 98.6% compared to an uncontrolled case. Additionally, the financial benefits of vehicle-to-grid and operating as primary frequency regulation reserve are assessed.

Guidance Mechanism for Flexible-Wing Aircraft Using Measurement-Interfaced Machine-Learning Platform

The autonomous operation of flexible-wing aircraft poses theoretical and technical challenges not yet addressed in the literature. The lack of an exact modeling framework is due to the complex nonlinear aerodynamics driven by the deformations of the flexible-wings, which in turn complicates the controls and instrumentation setup of the navigation system. This urges for innovative approaches to interface affordable instrumentation platforms to autonomously control this type of aircraft. This article leverages the ideas from instrumentation and measurements, machine learning, and optimization fields in order to develop an autonomous navigation system for a flexible-wing aircraft. A novel machine-learning process based on a guiding search mechanism is developed to interface real-time measurements of wing-orientation dynamics into control decisions. This process is realized using an online value iteration algorithm based on two improved and interacting model-free control strategies in real time. The first strategy is concerned with achieving the tracking objectives, whereas the second supports the stability of the system. A neural network platform that employs adaptive critics is utilized to approximate the control strategies while approximating the assessments of their values. An experimental actuation system is utilized to test the validity of the proposed platform. The experimental results are shown to be aligned with the stability features of the proposed model-free adaptive learning approach.