Model Predictive Control-based Energy Management Research Articles

Electric Vehicle Energy Management via Traffic Light Detection and Segmental Velocity Forecasting

Abstract Predictive-based power control has been widely recognized as a promising approach to boost driving range and improve system-level energy efficiency for electric vehicles (EVs), in which vehicle velocity forecasting generally serves as a preliminary input to optimally schedule the operations of varying on-board electrical and thermal systems. A segment-based velocity forecasting approach for individual commuting vehicles developed in this study reveals that it is challenging to forecast the velocity at intersection segments only using the velocity data. To address this challenge, this study seeks to develop a YOLO-V2-based object detection deep network to recognize the traffic lights in advance, and leverage the detected signals to establish a forecasting model that integrates with the probability-based hybrid forecasting approach. The case study results show that the traffic light detection-based forecasting model can significantly improve the forecasting accuracy for intersection segments. Based on the forecasting velocity 5-15 seconds ahead, the effectiveness of model predictive control-based energy management strategy is further evaluated with a liquid-based battery thermal control system. The proposed Battery Thermal Management System (BTMS) model shows promising results in maintaining battery temperature within an appropriate range, thus improving the overall energy efficiency of the EV. Moreover, a traffic light-based real-time energy management framework is developed to directly control the power demand from the Air Conditioning (AC) system.

Multi-stage control design for oscillating water column-based ocean wave energy conversion system

Despite the enormous global potential of ocean wave energy, it has yet to achieve a level of maturity and economic competitiveness that would result in a substantial impact. Challenges include direct integration into weak or isolated microgrids, a high proportion of uncertain marine environments, nonlinear dynamics, oscillating water column (OWC) device limitations, slower response times, unplanned power outages, power fluctuations, high capital and operational costs in ocean wave energy conversion (OWEC) systems. To this end, a new independent multi-stage design approach is proposed for the performance enhancement of an OWC-based OWEC system. Firstly, an airflow and rotational speed optimal control stage enhances power capture in the Wells turbine-based OWC plant. Secondly, compared to conventional control, the proposed permanent magnet synchronous generator control incorporates an adaptive nonlinear back-stepping control algorithm based on Lyapunov stability theory. Thirdly, introducing reconfigurable control into the conventional six-leg power converter ensures the uninterrupted operation of an OWEC system. Lastly, a model-predictive control-based energy management system is integrated with a bidirectional DC-DC converter that delivers steady power from the grid-connected OWC OWEC system. Hence, MATLAB simulations ensure the overall performance enhancement and feasibility of the OWEC system application and verify that the proposed multi-stage solution is efficient, robust, and reliable.

Decentralized energy management regime for prosumer community Microgrids using flexible tube model predictive control

Achieving reliable, cost-effective, and low-carbon energy management for community microgrids (CMs) under suitable computational complexity is challenging due to uncertainties in renewable generation and load demand. To address this challenge, this paper proposes a flexible tube model predictive control-based energy management regime (EMR) for prosumer CMs, enabling robustness against system uncertainties in the energy management strategy with fewer sacrifices in economic performance and computational efficiency. The proposed EMR adopts a multistage receding horizon optimization structure, which allows the prosumers to dynamically adjust their energy scheduling according to the updated energy price and forecast information. Further, a decentralized algorithm with a conditional communication strategy is developed, highlighting less communication burden and private information preservation. Case studies on a typical prosumer CM demonstrate the effectiveness of the proposed method in confronting high system uncertainties. Notably, the proposed method ensures that CMs operate with high reliability (having a maximum energy supply shortage probability of just 0.42 %), in a low-carbon and economical manner (the local accommodation rate of renewable energy generation with low costs reaches 98.83 %), even in scenarios where disturbances occur simultaneously on both the energy supply and demand sides.

Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS.

Model predictive control-based energy management system for an isolated electro-thermal microgrid in the Amazon region of Ecuador

Access to electricity is a fundamental key to developing countries' growth. Microgrids (MG) have become potential solutions to provide energy to isolated rural areas in a safe and environmentally friendly way. Therefore, proposals for alternative solutions to bridge the electrification gap in the Ecuadorian Amazon – which has the most significant percentage of its population without access to electricity – are of significant interest. Consequently, this paper presents the design of an Energy Management System (EMS) based on Model Predictive Control (MPC) for an isolated electro-thermal microgrid comprising a photovoltaic generator, a diesel generator, a lithium-ion battery Energy Storage System (ESS), electrical loads, and a domestic hot water system. The EMS aims to supply energy reliably and safely, minimize the MG's operation costs, and extend the ESS's useful life while satisfying the end users' comfort. This study includes an estimated degradation model for the ESS’s State of Health (SOH), an essential parameter contributing to reducing the microgrid's operating costs in the long term. Simulation results using one-year data present the influence of the prediction horizon on the MG’s scheduling. In addition, the benefits of the proposed EMS are highlighted by comparing it with the results achieved by a Unit Commitment approach, where it is demonstrated that the proposed EMS presents a reduction in MG operating costs and greenhouse gas emissions while maximizing the utilization of renewable energy and extending the lifespan of the ESS. Finally, an experimental validation, using a Typhoon Hardware-in-the-loop HIL-402 device in real-time operation, stands out the effectiveness and feasibility of the proposed MPC-based EMS.

Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive (AWD) hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability. Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue. A unique multi-mode coupling (MMC) AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles. Based on the parameters of the benchmarking model of a hybrid vehicle, the best model-predictive control-based energy management strategy is proposed. First, the drive system model was built after the analysis of the MMC-AWD's drive modes. Next, three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed, which was followed by the design of a road driving force observer. Then, the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient. Finally, the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD. The findings indicate that, in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6, the MMC-AWD's capacity to accelerate increases by 5.26% and 7.92%, respectively. When the road adhesion coefficient is 0.8, 0.6, and 0.4, the maximum climbing ability increases by 14.22%, 12.88%, and 4.55%, respectively. As a result, the dynamic performance is greatly enhanced, and the fuel savings rate per 100 km of mileage reaches 12.06%, which is also very economical. The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

A Model Predictive Control-Based Energy Management Strategy for Secure Operations in Shipboard Power Systems

A Model Predictive Control-Based Energy Management Strategy for Secure Operations in Shipboard Power Systems

Model Predictive Control-Based Energy Management System for a Hybrid Electric Agricultural Tractor

Model Predictive Control-Based Energy Management System for a Hybrid Electric Agricultural Tractor

Model predictive control-based energy management strategy with vehicle speed prediction for hybrid electric vehicles

The speed of a hybrid electric vehicle is a critical factor that affects its energy management performance. In this study, we focus on the importance of solving the problem of inaccurate speed prediction in the energy management strategy (EMS) and application of dynamic programming (DP) needs to know the entire driving cycle. A gated recurrent unit neural network (GRU-NN) speed predictive model based on machine learning is developed by using the model predictive control (MPC) framework and solved in the prediction domain by employing DP. The neural network is trained on the training set, which is a collection of standard driving cycles. The results are compared with other two types of speed predictive models to verify the effects of different parameters of different speed predictive models on the state of charge and fuel consumption under Urban Dynamometer Driving Schedule driving cycle. Simulation shows that MPC based on the GRU-NN speed predictive model can effectively improve the fuel economy of hybrid electric vehicles, with a 94.14% fuel economy, which proves its application potential. Finally, the GRU-NN speed predictive model is applied under the Real-World Driving Cycle, whose fuel consumption has a fuel economy of 91.95% compared with that of the original rule-based EMS.

Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty

A microgrid is considered to be a smart power system that can integrate local renewable energy effectively. However, the intermittent nature of renewable energy causes operating pressure and additional expense in maintaining the stable operation by the energy management system in a microgrid. The structure of multi-microgrids provides the possibility to construct flexible and various energy trading framework. In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded multi-microgrids. This strategy balances the robustness and economy of single-microgrid system operation by combining the advantages of robust optimization and model predictive control, while coping with the uncertainty of renewable energy sources. Furthermore, a dynamic energy trading market is formed among microgrids, which can enhance the overall economy of the multi-microgrids system. Simulation results verify the feasibility of the proposed DRMPC strategy.

MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids

This paper presents the improvements of the hosting capacity of photovoltaics (PVs) and electric vehicles (EVs) in a stand-alone microgrid (MG) with an energy storage system (ESS) by consider-ing a model predictive control (MPC) based energy management system. The system is configured as an MG, including PVs, an ESS, a diesel generator (DG), and several loads with EVs. The DG is controlled to operate at rated power and the MPC algorithm is used in a stand-alone MG, which supplies the energy demanded for several loads with EVs. The hosting capacity of the load in-cluding the EV and PVs can be expanded through the ESS to the terminal node of the microgrid. In this case, the PVs and the load can be connected in excess of the capacity of the diesel genera-tor, and each bus in the feeder complies with the voltage range required by the grid. The effec-tiveness of the proposed algorithm to resolve the hosting capacity is demonstrated by numerical simulations.

An Analysis of Multi Objective Energy Scheduling in PV-BESS System Under Prediction Uncertainty

Energy storage systems (ESSs) are being considered to overcome issues in modern grids, caused by increasing penetration of renewable generation. Nevertheless, integration of ESS should also be supplemented with an optimal energy management framework to ensure maximum benefits from ESS. Conventional energy management of battery, used with PV system, maximises self-consumption but does not mitigate grid congestion or address battery degradation. Model predictive control (MPC) can alleviate congestion, degradation while maximizing self-consumption. As such, studies will be carried out, in this work, to highlight the improvement with MPC based energy management over conventional method using simulations of one-year system behaviour. As MPC uses forecast information in decision making, the impact of forecast uncertainties will be assessed and addressing the same through constraint tightening will be presented. It is concluded that MPC provides improvement in system behaviour over multiple performance criteria.

Shifting strategy and energy management of a two-motor drive powertrain for extended-range electric buses

This paper studies the shifting strategy and energy management of a two-motor drive powertrain for extended-range electric buses. To do this, a multibody dynamic model is first established for transient investigation. A novel shift schedule is second proposed by integrating a priority factor for the engaging gear into the equivalent motor efficiency. According to vehicle speed and power, the shift schedule results in two possible cases: single-gear and double-gear change. Optimal shifting strategies are third recommended to eliminate the torque interruption under those possible gear changes. To optimize the torque allocation to two motors and minimize fuel consumption, a model predictive control-based energy management strategy is then developed. Finally, this paper simulates shift quality and energy economy in comparison with a conventional one-motor drive powertrain. The results show that torque interruptions generate large jerks in the one-motor configuration. Conversely, the two-motor configuration achieves great shift quality by eliminating the torque interruptions mostly during single-gear upshift or entirely under double-gear downshift. The two-motor drive powertrain also improves energy economy significantly by 9.1% compared to the conventional configuration.

Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles

Fuel cell hybrid construction vehicles are attractive for future fuel cell applications. Model predictive control as an energy management strategy has been applied to various hybrid electric vehicles. In this paper, a hierarchical model predictive control-based energy management strategy for fuel cell hybrid construction vehicles is explored. The hierarchical model predictive control framework consists of economic and control levels. The economic level considers economic costs, including of hydrogen consumption and components use cost. Real-time optimization is implemented at the economic level to identify the optimal reference trajectory. The control layer is a model predictive control that controls the system to follow the reference trajectory. A prediction methodology-based on wavelet transformation and Levenberg-Marquardt optimised neural network is proposed for the complex load prediction of fuel cell hybrid construction vehicle. The simulations performed with cyclic loading show that the accuracy of the proposed prediction methodology is satisfactory, and demonstrate the superiority of the proposed energy management strategy. The proposed hierarchical model predictive control-based energy management strategy considering economy and controllability is important for commercial application in hybrid electric vehicles.

An integrated predictive energy management for light-duty range-extended plug-in fuel cell electric vehicle

A reliable power distribution strategy is of great significance towards the performance enhancement of fuel cell electric vehicles. In this work, a novel model predictive control-based energy management is developed for a fuel cell based light-duty range-extended hybrid electric vehicle. To fulfill the model predictive control framework, a cooperative speed forecasting method based on Markov Chain and fuzzy C-means clustering technique is proposed, which contains multiple predictive sub-models for handling different driving patterns. The final prediction results are obtained by synthesizing the forecasted speed profiles from all sub-models with the quantified fuzzy membership degrees. Besides, an adaptive battery State-of-Charge reference generator is built, which can regulate the SoC depleting rates against various power requirements. Combined with the forecasted speed and SoC reference, the desirable control actions are derived through minimizing the performance index over each finite time horizon. As a result, under the realistic urban-based postal-delivery mission profiles, the proposed strategy can achieve over 3.79% equivalent hydrogen consumption saving and over 40.04% fuel cell power dynamics decrement against benchmark strategy. Moreover, the presented predictive energy management is robust to certain level of trip duration estimation errors, further indicating its suitability for real applications.

A Model Predictive Control-Based Energy Management Scheme for Hybrid Storage System in Islanded Microgrids

Model predictive control (MPC) facilitates online optimal resource scheduling in electrical networks, thermal systems, water networks, process industry to name a few. In electrical systems, the capability of MPC can be used not only to minimise operating costs but also to improve renewable energy utilisation and energy storage system degradation. This work assesses the application of MPC for energy management in an islanded microgrid with PV generation and hybrid storage system composed of battery, supercapacitor and regenerative fuel cell. The objective is to improve the utilisation of renewable generation, the operational efficiency of the microgrid and the reduction in rate of degradation of storage systems. The improvements in energy scheduling, achieved with MPC, are highlighted through comparison with a heuristic based method, like Fuzzy inference. Simulated behaviour of an islanded microgrid with the MPC and fuzzy based energy management schemes will be studied for the same. Apart from this, the study also carries out an analysis of the computational demand resulting from the use of MPC in the energy management stage. It is concluded that, compared to heuristic methods, MPC ensures improved performance in an islanded microgrid.

Model Predictive Control for Optimal Energy Management of an Island Wind Storage Hybrid Power Plant

This paper presents a Model Predictive Control-based Energy Management System for compliance with the day-ahead power dispatching plan of a hybrid power plant connected to the Guadeloupe Island electrical grid. The hybrid power plant combines a wind farm and a Li-ION battery energy storage system. The proposed EMS handles several operation rules, in order to solve the optimization problem while considering the production forecasting data as well as the battery lifespan. A simulation study is implemented via PowerFactory and Matlab.

Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction

A major advantage of plug-in hybrid electric vehicles is their high fuel economy, which is closely related to their energy management strategy and driving cycles. In this study, an improved velocity prediction method is formulated based on the Markov model and a back propagation neural network. The root mean square error of the predicted velocity for the New European Driving Cycle is 0.1511 m/s when the prediction time is 3 s. Moreover, a vehicle test for the velocity prediction algorithm is implemented on a hybrid electric bus, which verifies the reliability and real-time performance. On this basis, a model predictive control-based energy management strategy incorporating the velocity prediction is proposed. In order to lessen the computation and memory burden and constrain the battery's state of charge, a state of charge-based adaptive equivalent consumption minimization strategy is applied to the predictive control-based energy management strategy. By simulation, the proposed velocity prediction-based energy management strategy improves fuel economy by 3.11% and 7.93% for a plug-in hybrid electric commercial vehicle in the New European Driving Cycle, while 2.96% and 11.02% in the Worldwide Harmonized Light Vehicles Test Procedures, when compared with the adaptive equivalent consumption minimization strategy and equivalent consumption minimization strategy, respectively.

An Adaptive Online Prediction Method With Variable Prediction Horizon for Future Driving Cycle of the Vehicle

Accurate prior knowledge of future driving cycle is quite essential in many research and applications related to optimal control of the vehicle and transportation, especially for model predictive control-based energy management for hybrid electric vehicles. Therefore, an adaptive online prediction method with variable prediction horizon is proposed for future driving cycle prediction in this paper. In particular, two aspects of efforts have been explored. First, combining Markov chain and Monte Carlo theory, a multi-scale single-step prediction method is proposed and compared with traditional fixed-scale multi-step method, improving by about 7% in prediction accuracy. Second, to further adapt to variable actual driving cycles, online reconstructions of driving cycle and state filling are introduced to guarantee continuous and robust online application; principal component analysis and cluster analysis are employed to adjust real-time prediction horizons for better overall prediction accuracy. In the end, the proposed method is verified by the experiment of hardware-in-loop simulation, showing more than 20% improvement in prediction accuracy than fixed-horizon prediction method, and relatively good robustness and universality in different driving conditions.

Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle

The series hybrid electric tracked bulldozer (HETB)’s fuel economy heavily depends on its energy management strategy. This paper presents a model predictive controller (MPC) to solve the energy management problem in an HETB for the first time. A real typical working condition of the HETB is utilized to develop the MPC. The results are compared to two other strategies: a rule-based strategy and a dynamic programming (DP) based one. The latter is a global optimization approach used as a benchmark. The effect of the MPC’s parameters (e.g. length of prediction horizon) is also studied. The comparison results demonstrate that the proposed approach has approximately a 6% improvement in fuel economy over the rule-based one, and it can achieve over 98% of the fuel optimality of DP in typical working conditions. To show the advantage of the proposed MPC and its robustness under large disturbances, 40% white noise has been added to the typical working condition. Simulation results show that an 8% improvement in fuel economy is obtained by the proposed approach compared to the rule-based one.