Predictive Energy Management Research Articles

Solar Panel Cleaning System Using Smart Hybrid Embedded Systems with IoT Assistance

The solar panel cleaning system utilizes a rechargeable battery and is activated by a switch through a mobile application. The cleaning tool is moved horizontally by pressing a button in the mobile application and sends an output signal to the gear motors using internet access. Cleaning solar panels on a commercial and industrial scale can be difficult due to the lack of readily available water and power sources. Rooftops, carports, and other elevated structures provide unique cleaning challenges. A solar panel cleaning system based on a Linear Piezoelectric Actuator (LPA) is intended to enable a solar panel to operate in optimal power-producing conditions regardless if employed in a gross climate. The energy flow, the solar power source, the supercapacitor-battery hybrid storage system, and the load are effectively managed using a Support Vector Machine (SVM)-based load predictive energy management system. Hence, LPA-SVM is a solar panel cleaning system that can diminish dirt accumulating on the surface. Cleaning solar panels may improve performance by removing dust, dirt, and pollutants like leaves and bugs. Clean the contaminants with water and a soft, non-abrasive sponge and towel for optimal results and to avoid damaging the solar panels in the accessibility.

Health-conscious predictive energy management strategy with hybrid speed predictor for plug-in hybrid electric vehicles: Investigating the impact of battery electro-thermal-aging models

Improving plug-in hybrid electric vehicles (PHEVs) fuel economy requires a proper energy management strategy (EMS). Efforts to enhance the energy-saving performance of predictive EMSs have concentrated on advanced speed prediction methods. However, the impact of battery models on the predictive EMS hasn't been investigated. This paper aims to fill the research gap by suggesting a model predictive control-based (MPC) EMS framework that uses a hybrid speed predictor. Firstly, six control-oriented battery electro-thermal-aging models with various terminal voltage and temperature simulation accuracies have been developed and verified. Secondly, it also examines the effects of different battery models on MPC-based EMS through quantitative analysis, including battery dynamics, calculation time, and resultant operating costs, which leads to the suggestions of model selection for the design of the EMS under low- and room-temperature driving scenarios. Finally, a novel hybrid speed prediction model is proposed, where the historical speed is decomposed into strongly periodic intrinsic mode functions (IMFs) by variational mode decomposition (VMD), and backpropagation (BP) neural network is utilized to learn the feature parameter and mapping relationship of each IMF. In addition, a case study is conducted by applying the proposed speed prediction model in an MPC-based EMS method. The simulation results highlight that the proposed hybrid speed prediction model can achieve preferable speed prediction. The operating cost errors (compared with the MPC-based EMS with 100% speed prediction) are reduced to 0.4% and 0.98% at −20 °C and 25 °C driving scenarios, respectively.

Energy Management Strategy of Hybrid Ships Using Nonlinear Model Predictive Control via a Chaotic Grey Wolf Optimization Algorithm

Reducing energy consumption and carbon emissions from ships is a major concern. The development of hybrid technologies offers a new direction for the rational distribution of energy. Therefore, this paper establishes a torque model for internal combustion engines and motors based on first principles and fitting the data collected from the test platform; in turn, it develops a model for fuel consumption and carbon emissions. Furthermore, the effect of irregular waves using an extended Kalman filter is estimated as well as feedback to the controller as a disturbance variable. Then, a parallel hybrid ship energy management strategy based on a new real-time nonlinear model of predictive control is designed to achieve energy conservation and emission decrease. A hybrid algorithm of chaotic optimization combined with grey wolf optimization is utilized to solve the nonlinear optimization problem in the nonlinear model predictive control strategy and a local refined search is performed using sequential quadratic programming. Through the comparison of fuel consumption, carbon emissions, real-time performance, and the engine load path, the superiority of the nonlinear model predictive control energy management strategy based on the chaotic grey wolf optimization algorithm is verified.

A cloud-based eco-driving solution for autonomous hybrid electric bus rapid transit in cooperative vehicle-infrastructure systems: A dynamic programming approach

Efficient public transportation has always intrigued extensive research. Aiming to improve the commuting efficiency and fuel economy of the autonomous hybrid electric buses in the Bus Rapid Transit (BRT), a cloud-based eco-driving solution adopting dynamic programming and model predictive control is proposed in this paper. This solution contains an upper-level cloud-based scheduling strategy and a lower-level onboard predictive energy management, which is conceived to function in a Cyber-physical system of the cooperative vehicle-infrastructure system. The scheduling model carefully considered coupled spatiotemporal constraints for the driving of autonomous BRT buses, including traffic lights, traffic regulations, stations, and ride comfort. The onboard energy management leverages the pre-planned scheduling information to achieve near-optimal fuel economy. The eco-driving solution is examined in three scenarios with intersections, stations, and ramps. Simulation results show that the proposed method can deal with different spatiotemporal limits along the route, with virtually no non-essential stops and sudden acceleration or braking, and achieves 97%–98% energy-saving potential compared with the baseline performance.

Data-driven bi-level predictive energy management strategy for fuel cell buses with algorithmics fusion

This paper aims to answer how to effectively integrate the data-driven method into the traditional predictive energy management algorithm rather than replacing it outright. Given the challenge of selecting an appropriate prediction horizon for predictive energy management, this study seeks to bridge traditional predictive energy management with machine learning approaches, thereby presenting a novel bi-level predictive energy management strategy for fuel cell buses with multi-prediction horizons. In the upper layer, the core parameter, prediction horizon, of the traditional model predictive control energy management framework is optimized using two distinct data-driven methods. The first method employs deep learning to establish a mapping relationship between the vehicle states and the optimal prediction horizon through deep neural networks. The second method utilizes reinforcement learning to obtain the best prediction horizon under varying vehicle states through intelligent agent exploration. In the lower level, predictive energy management is performed on fuel cell buses based on optimization levels. Finally, the proposed strategy is validated using test data from actual fuel cell buses. The results demonstrate that two data-driven methods, based on the optimal ΔSoC approximation and the deep reinforcement learning, can select the appropriate prediction horizon more conducive to energy saving according to the vehicle states. Regarding energy consumption, the multi-horizon predictive energy management based on deep reinforcement learning exhibits a remarkable reduction in energy consumption by 7.62 %, 4.55 %, 4.60 %, and 7.80 %, when compared with the predictive energy management employing fixed prediction horizons of 5 s, 10 s, 15 s, and 20 s, respectively. Furthermore, it outperforms the multi-horizon predictive energy management approach based on the optimal ΔSoC approximation by 3.59 %.

Adaptive equivalent consumption minimization strategy based on road grade estimation for a plug-in hybrid electric truck

Road grade plays an important role in deciding power repartition and improving energy management performances. In this paper, instead of predictive energy management strategies where terrain information is obtained from GIS maps, an adaptive equivalent consumption minimization strategy (A-ECMS) considering current road grade information is proposed, aiming to design an instantaneous feedback supervisory controller based on the estimation of current road grade without using external devices. To achieve real-time control for a plug-in hybrid electric truck (PHET), the bounds of the optimal equivalent factor (EF) are analyzed considering comprehensive performances, then the sensitivity of EF is evaluated towards road grade. According to the effect of different road grade scenarios on control performances, the real-time EF adaptation is divided into two conditions, i.e. SOC-based adaptation and Estimation-based adaption. The proposed A-ECMS can achieve good performances on both fuel economy improvement and emission reduction, which approximates the results obtained from the DP algorithm, and the high computing load can be avoided for real-time implementation.

Hierarchical predictive energy management strategy for fuel cell buses entering bus stops scenario

This paper aims to answer how to use traffic information to design energy management strategies for fuel cell buses in a networked environment. For the buses entering the bus stops scenario, this paper proposes a hierarchical energy management strategy for fuel cell buses, which considers the traffic information near the bus stops. In the upper-level trajectory planning stage, the optimal SOC trajectory under various historical traffic conditions is solved through dynamic planning. The traffic information and the best SOC trajectory are mapped through BiLSTM, which can achieve fast, real-time long-term SOC reference. In the lower-level real-time predictive energy management strategy, the optimal SOC is used as the state reference to guide the predictive energy management of fuel cell buses when entering the bus stops. Simulation results show that compared with the strategy without SOC trajectory reference, the life cost of the proposed strategy is reduced by 13.8%, and the total cost is reduced by 3.61%. The SOC of the proposed strategy is closer to the DP optimal solution.

IGBT Thermal Model-Based Predictive Energy Management Strategy for Plug-In Hybrid Electric Vehicles Using Game Theory

Energy management strategy (EMS) of plug-in hybrid electric vehicle (PHEV) can coordinate the efficient coupling operation between multiple power sources, which is of great significance to the improvement of fuel economy and transmission efficiency. During the actual operation, EMS needs the electric drive system operate frequently to achieve excellent vehicle performance. In this case, the temperature of insulated gate bipolar transistor (IGBT) module inevitably increases and the thermal-mechanical stress caused by temperature fluctuation will lead to failure. To solve the above problems, a model predictive control (MPC)-based EMS considering IGBT temperature is proposed. Firstly, Markov chain-based driver model is built for the prediction of future vehicle speed. Secondly, according to the thermoelectric coupling model description of the IGBT module, a predictive energy management framework is constructed with fuel economy and IGBT temperature indexes as objective functions. Then, under the MPC framework, a non-cooperative game model with engine and IGBT as participants is established, and Nash equilibrium is taken as the solution. Finally, the simulation results indicate that the proposed strategy not only improves the fuel economy by 13.88% and 13.48% compared with the rule-based strategy under two driving conditions, but also effectively reduces the temperature of IGBT.

Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method

Fuel cell lifetime is strongly affected by dynamic conditions. Most existing energy management works only focus on the fuel cell durability protection from the perspective of output power slope, without deeply considering the influence of the important parameters inside the stack. However, considering the variation of stack internal parameters (mechanism analysis) is more significant for fuel cell lifetime evaluation. In this paper, a health-aware model predictive control (HA-MPC) energy management strategy is proposed for fuel cell electric vehicle. A fuel cell health state model is established from the perspective of stack hydrogen excess ratio (HER), oxygen excess ratio (OER) and humidity through the hybrid modeling method. The fuel cell mechanism model and the low-dimensional data-driven model are established through the grey-box model estimation method and genetic algorithm-based radial basis function (GA-RBF) neural network. Then the objective function of energy management strategy is developed considering the total equivalent hydrogen consumption and stack improper parameter changes of HER, OER and humidity. Comparing with model predictive control strategy based on the typical power cost function, the HA-MPC can effectively reduce the steep drop of HER and OER in low power region by 3.58% and 4.41%, which can protect the fuel cell system lifetime.

Development of simplified air drag models including crosswinds for commercial heavy vehicle combinations

Accurate range prediction requires good knowledge of the prevailing wind conditions and how they affect the energy consumption of the ego vehicle. A few different simplified vehicle air drag models that explicitly include the effect from crosswinds are presented and compared through some objective criteria. The models are developed from the normal air drag equation where the effect from wind is implicit and therefore often forgotten or neglected. The purpose is to find a low-complexity model complementing CFD models and wind tunnel tests, that can be used for range estimation and predictive energy management algorithms. To simplify online estimation, a requirement is that the air drag models only contain a few tuning parameters. The models are validated against CFD calculations for a few vehicle combinations and the best models show good accuracy for air attack angles up to at least 60 degrees. It is shown that the parameters of the simplified models can loosely be connected to some basic geometrical attributes of a vehicle combination so it should be possible to give at least a rough estimate of the parameters of a simplified model based on these geometrical attributes. This is useful for making a first estimate of the aerodynamic properties of a vehicle combination after major changes in the exterior, e.g. when adding a trailer. It also highlights that the size and the shape of the vehicle side may be mainly responsible for the high longitudinal air drag sensitivity to crosswinds for large vehicle combinations.

Dynamic Traffic Prediction-Based Energy Management of Connected Plug-In Hybrid Electric Vehicles with Long Short-Term State of Charge Planning

Vehicle electrification, automation, and connectivity in today's transportation require significant efforts in control design to meet conflicting goals of energy efficiency, traffic safety, as well as comfort. The rapid development of intelligent transportation systems (ITS) and the rapid growth of connectivity technologies enable vehicles to receive more information about traffic conditions, which provides a reliable solution for the energy management of plug-in hybrid electric vehicles (PHEVs). This article proposes a predictive energy management strategy (EMS) for connected PHEV based on real-time dynamic traffic prediction. First, the future traffic information is predicted by establishing a wavelet neural network (WNN). Thus, the global driving condition can be predicted. Then, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of WNN to plan a global battery state-of-charge (SOC) reference. Second, a long short-term memory-based velocity predictor is proposed for the predictive EMS, by planning SOC over a prediction horizon based on the global SOC reference. Finally, the performance of the proposed EMS with WNN and PSO-WNN is verified by the actual traffic data. The results show that it can improve the fuel economy by 17.57% and 28.19%, respectively.

Stochastic Model Predictive Energy Management of Electric Trucks in Connected Traffic

This paper proposes a cost-effective power management strategy utilizing the data provided by V2I communication techniques for dual electric machine coupling propulsion trucks. We formulate a bilevel program where the high-level optimizes operation mode implicitly, while the low-level computes an explicit policy for power distribution of two electric machines. Stochastic model predictive control (SMPC) strategy is employed at the high-level, the performance of which highly depends on the prediction accuracy of future driving information. To establish a position-dependent stochastic velocity predictor using limited amount of historical data, two improved approaches are developed: 1) Predictor using multiple features; 2) Predictor combining data and model. Simulations are performed to validate the performance of the proposed predictors compared with a benchmark. The results show that the controllers using the proposed predictors can reduce driving cost by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3.36 \,\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4.26 \,\%$</tex-math></inline-formula> , respectively.

A tolerant sequential correction predictive energy management strategy of hybrid electric vehicles with adaptive mesh discretization

This paper establishes an adaptive correction predictive energy management strategy (EMS) to obtain optimal power distribution in the case of inaccurate prediction with excellent computational efficiency for parallel hybrid electric vehicle (HEV). Firstly, deep neural network (DNN) models are trained to forecast future speed sequence with different prediction horizons to prepare for online model predictive control (MPC) implementation. According to the degree of forecasting errors, a novel tolerant sequential correction algorithm as the solver of MPC strategy is selected in tuning mechanism instead of traditional dynamic programming (DP) to cope with the rough accumulated prediction. Besides, to compromise between fuel optimization and lower computation burden, statistical analysis of battery SOC variation distribution is gained from historical driving cycles' calculation through offline DP. Then, nearest neighbor interpolation model is fitted to generate optimal ranges of state variable in each step, which adaptive mesh discretization method is intelligently reducing state and control calculation grid's number. Numerical simulations demonstrate that the proposed tolerant sequential correction algorithm applied in MPC scheme with adaptive mesh discretization have yielded the favorable capability of the fuel economy in the consideration of inaccurately velocity prediction and excellent computational efficiency, which exhibits the practical adaptability in real driving routes.

Incorporating Driving Style Recognition Into MPC for Energy Management of Plug-In Hybrid Electric Buses

Vehicle fuel economy is greatly influenced by the driver’s driving style. To achieve remarkable promotion in the fuel economy, a novel predictive energy management strategy (EMS) with strong adaptability to driving styles is presented for plug-in hybrid electric buses (PHEBs) in this article. First, a combined unsupervised and supervised algorithm for the driving style identification is devised based on instant driving conditions. In the algorithm, a multidimensional Gaussian distribution (MGD)-based analysis on factors influencing driver’s driving style provides valuable inputs for local mean <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> nearest neighbor (LMKNN) algorithm, and outstanding regression ability of the LMKNN returns exact recognition result and then incorporating the driving style recognition function into the model predictive control (MPC) to formulate a driver-oriented predictive EMS, where estimate distribution and particle swarm optimization (ED-PSO) algorithm is introduced to obtain optimal control sequence over a receding horizon. Finally, simulation validations in the MATLAB/Simulink environment show that the total cost of the proposed strategy is decreased by 12.53% compared to the charge-depleting and charge-sustaining (CD-CS) method, without sacrificing the real-time applicability.

Predictive Energy Management for Hybrid Electric Aircraft Propulsion Systems

We present a model predictive control (MPC) algorithm for energy management in aircraft with hybrid electric propulsion systems consisting of gas turbine and electric motor components. Series and parallel configurations are considered. By combining a point-mass aircraft dynamical model with models of electrical losses and losses in the gas turbine, the fuel consumed over a given future flight path is minimized subject to constraints on the battery, electric motor, and gas turbine. The optimization is formulated as a convex problem under mild assumptions and its solution is used to define a predictive energy management control law that takes into account the variation in aircraft mass during flight. We investigate the performance of algorithms for solving this problem. An alternating direction method of multipliers (ADMM) algorithm is proposed and compared with a general purpose convex interior point solver. We also show that the ADMM implementation reduces the required computation time by orders of magnitude in comparison with a general purpose nonlinear programming solver, making it suitable for real-time supervisory energy management control.

Predictive Energy Management Strategy for Hybrid Electric Air-Ground Vehicle Considering Battery Thermal Dynamics

Hybrid electric air-ground vehicles (HEAGVs), which can run on the land and fly in the air, are considered a promising future transportation. The operation of HEAGVs, accompanied by high energy consumption, could lead to increasing battery temperature, which may affect the lifespan of the battery. To make the battery last longer and improve energy efficiency, an effective energy management strategy (EMS) is necessary for the operation of HEAGVs. In this regard, this paper proposes a predictive EMS based on model predictive control (MPC). Firstly, speed information is obtained by intelligent network technology to achieve a prediction of power demand, and then the state of charge (SOC) reference trajectory is planned. Secondly, a Pontryagin’s minimum principle-based model predictive control (PMP-MPC) framework is proposed, including battery thermal dynamics. Under the framework, fuel efficiency is improved by reducing the temperature of the battery. Finally, the proposed method is compared to PMP, dynamic programming (DP), and rule-based (RB) methods. The effect of different preview horizon sizes on fuel economy and battery temperature is analyzed. Verification results under two driving cycles indicate that compared with the rule-based method, the proposed method improves fuel economy by 5.14% and 5.2% and decreases the temperature by 5.9% and 4.9%, respectively. The results demonstrate that the proposed PMP-MPC method can effectively improve fuel economy and reduce temperature.

Markov velocity predictor based on state space optimization and its applications in PHEV energy management

Predictive energy management (PEM) strategy has shown great advantages in improving fuel economy for plug-in hybrid electric vehicles (PHEV). A Markov velocity predictor optimization method and its applications in PHEV energy management is studied in this paper. The initial Markov velocity predictor is constructed using complete driving cycle information and the state space of the Markov velocity predictor is then optimized for specified driving conditions using simulated annealing algorithm (SAA). The practical driving conditions are identified using a multi-feature driving condition recognition unit by using the support vector machine (SVM) method. Based on the driving conditions identified, velocities are predicted using the proposed method and optimized using dynamic programming (DP) algorithm in conjunction with the state of charge (SOC) reference and vehicle state. The energy management strategy derived is then implemented in the vehicle controllers. Comparing with the traditional rule-based energy management strategy, simulation results indicate that the PEM strategy proposed herein can reduce fuel consumption.

Cost-optimal design and energy management of fuel cell electric trucks

Road freight transport on hilly routes represents a significant challenge for the advancement of fuel cell electric trucks because of the high-performance requirements for fuel consumption, vehicle lifetime, and battery charge control. Therefore, it is essential to optimize the vehicle design and energy management, which greatly influence the driving performance and total cost of ownership. This paper focuses on the cost-optimal design and energy management of fuel cell electric trucks, considering five key influencing factors: powertrain component sizing, driving cycle, vehicle weight, component degradation, and market prices. The cost optimization relies on a novel predictive energy management scheme based on dynamic programming and the systematic calibration of control parameters. The paper analyzes the simulation results to highlight three main findings for fuel cell electric trucks: 1) cost-optimal energy management is essential to define the best trade-off between fuel consumption and component degradation; 2) the total cost of ownership is significantly influenced by component sizing, driving cycles, vehicle weight, and market prices; 3) predictive energy management is highly beneficial in challenging road topographies for substantial cost-saving and lower component size requirements.

Approach for a Global Route-Based Energy Management System for Electric Vehicles with a Hybrid Energy Storage System

The usage of batteries and supercapacitors in the field of electric vehicles is becoming increasingly prominent in both research and development. Due to the complementary advantages of the two systems, high energy density, and high power density, a combined battery/supercapacitor system offers potential. To effectively utilise the potential of such a hybrid energy storage system (HESS), one requires an intelligent energy management system (EMS). The EMS is responsible for controlling the electrical power between the battery and the supercapacitor in such a way that the required power can be optimally distributed at all times (currently and in the future). For this purpose, the energy management system utilises information on the driving route and, based on this information, shall calculate a global strategy for the continuous power distribution. The controlled power distribution should take place in real time and be robust against discrepancies so that unpredictable or unreliably predictable events do not significantly influence the functionality. For the implementation of the concept, a rule-based power distribution is implemented in combination with a predictive energy management. Here, the energy management is combined with a rule-based strategy calculation based on data on the route to be driven with a global optimization for the calculation of a route-specific strategy. Depending on the selected objective, the increase in energy efficiency, or lifetime, the operation of the power control is optimised. Due to the functional separation, the continuous power control can operate in real time, while more computational time can be spent on the calculations of the power management strategy, which accordingly does not need to be executed in real time. The results show that by using the presented EMS, especially in combination with a route-specific parameterisation, a significant effect on the energy efficiency and/or battery lifetime can be achieved. The average battery energy consumption can be reduced by up to 9.14% on urban routes. Regarding battery lifetime, the average battery usage can be reduced up to 13.35% and the battery energy losses even up to 62.72%.

A two-layer multi-energy management system for microgrids with solar, wind, and geothermal renewable energy

The inherent intermittency of high-penetrated renewable energy poses economic and reliable issues of microgrid energy management. This study proposes a two-layer predictive energy management system (PEMS) for high-renewable multi-energy microgrid (MEM). In this MEM, geothermal, solar, and wind energy is converted and conditioned for electricity, thermal, and gas supplies, in which multi-energy complementarities are fully exploited based on electrolytic thermos-electrochemical effects. The proposed microgrid multi-energy management is a complicated and cumbersome problem because of their increasingly tight energy couplings and uncertainties of renewable energy sources (RESs). This intractable problem is thus processed by means of a two-layer PEMS with different time scales, where the system operating costs are minimized in the upper layer and the renewable fluctuations are coped with in the lower layer. Simulation studies on a high-renewable MEM are provided to indicate its effectiveness and superiority over a single time scale scheme. Simulations results show that the operating cost can be reduced by 22.2% with high RESs accommodation.