Proposed Energy Management Strategy Research Articles

Adaptive real-time optimal energy management strategy for extender range electric vehicle

The extender range electric vehicle (EREV) is an effective way to solve the “mileage anxiety” of pure electric vehicles, and the fuel economy of EREV is the key point of energy optimization. This paper designed an adaptive real-time optimal energy management strategy for EREV. Firstly, an improved shooting algorithm is proposed, which can determine the range of the equivalent factor (EF) according to the power configuration parameters of the vehicle, and then the secant method is used to quickly calculate the initial value of the EF. Secondly, from the perspective of energy flow, the intrinsic operation mechanism of equivalent consumption minimization strategy (ECMS) control strategy is revealed, and the working relationship between the five working modes of EREV is clarified. Thirdly, based on the car navigation and geographic location information system, the EF is periodically updated to achieve effective maintenance of the battery state of charge (SOC), so as to obtain the optimal power allocation. Finally, The fuel economy and real-time performance of the proposed energy management strategy are simulated and compared. To verify fuel economy, the rule-based control strategy and the power following control strategy were used as comparison. The results show that the proposed control strategy has better fuel economy and adaptability. To verify real-time performance, the proportional integral derivative ECMS (PID-ECMS) and shooting method ECMS (S-ECMS) were used as comparison. The results show that the proposed strategy is better in both fuel economy and real-time performance.

Feed-forward modeling and real-time implementation of an intelligent fuzzy logic-based energy management strategy in a series–parallel hybrid electric vehicle to improve fuel economy

A hybrid electric vehicle is powered by: the internal combustion engine and the battery-powered electric motor. These sources have specific operational characteristics, and it is necessary to match these characteristics for the efficient and smooth functioning of the vehicle. The nonlinearity and uncertainties in hybrid electric vehicle model require an intelligent controller to control the energy sharing between battery and engine. In this work, a fuzzy logic-enabled energy management strategy for the hybrid electric vehicle based on torque demand, battery state of charge and regenerative braking is designed and implemented. The proposed energy management strategy allows engine and motor to maneuver in their efficient operating regions. The designed hybrid electric vehicle and its control strategy follow the driver commands and regulations on vehicle performance and liquid fuel consumption. MATLAB/Simulink is used to carry out simulations, and then, the whole system is validated in real time on hardware-in-the-loop testing platform. This work employs an FPGA-based MicroLabBox hardware controller to validate real-time behavior. The proposed scheme results in better fuel economy, faster response and almost nil mismatch between desired and achieved vehicle speeds.

Improvement of ultracapacitors-energy usage in fuel cell based hybrid electric vehicle

Hybrid electric power systems based on fuel cell stack and energy storage sources like batteries and ultracapacitors are a plausible solution to vehicle electrification due to their balance between acceleration performance and range. Having a high degree of hybridization can be advantageous, considering the different characteristics of the power sources. Some parameters to be considered are: specific power and energy, energy and power density, lifetime, cost among others. Ultracapacitors (UC) are of particular interest in electric vehicle applications due to its high-power capability, which is commonly required during acceleration. UCs are commonly used without a power electronics interface due to the high-power processing requirement. Although connecting UCs directly to the DC bus, without using a power converter, presents considerable advantages, the main disadvantage is related to the UC energy-usage capability, which is limited by constant DC bus control. This paper proposes a novel energy-management strategy based on a fuzzy inference system, for fuel-cell/battery/ultracapacitor hybrid electric vehicles. The proposed strategy is able to control the charge and discharge of the UC bank in order to take advantage of its energy storage capability. Experimental results show that the proposed strategy reduces the waste of energy due to dynamic brake in 14%. This represents a reduction in energy consumption from 218 Wh/km to 192 Wh/km for the same driving conditions. By using the proposed energy management strategy, the estimated fuel efficiency in miles per gallon equivalent was also increase from 96 mpge to 109 mpge.

Stochastic Model Predictive Control for Dual-Motor Battery Electric Bus Based on Signed Markov Chain Monte Carlo Method

With the increasing demand for battery electric buses, the dual-motor coupling powertrain (DMCP) shows great advantages, but it makes the energy optimization problem more complex. To solve the hybrid system optimization problem, a stochastic model predictive control (SMPC) method is proposed to exploit the potential performance of DMCP, where the most critical issue is to improve the prediction accuracy and handle the uncertainties. After analyzing the typical velocity profiles, statistical properties are used to develop a novel Signed Markov Chain Monte Carlo (SMCMC) method that can enhance the accuracy of velocity prediction by more than 50%, compared to conventional Markov Chain methods. Next, considering the uncertainties present in various driving scenarios, the development of driving cycle recognition model based on fuzzy logic control (FLC) is introduced; this method permits to identify the current category of driving cycle rapidly. Then, dynamic programming (DP) is adopted to solve the rolling optimization problems in each finite horizon online, including necessary constraints of dynamic response. Finally, simulation results demonstrate that the proposed energy management strategy can address various daily driving cycles well, and can improve the energy performance by 6% under a generalized combination of driving conditions compared to preliminary rule-based control.

Optimal Energy Management System Design Based on Dynamic Programming for Battery Electric Vehicles

Abstract An optimal control strategy for the energy management system on battery electric vehicles with only one power source is proposed. Firstly, this paper builds the control-oriented models for the drivetrain, battery thermal management and battery aging. Then, it is stated that, taking drivetrain power loss, battery thermal management requirements, and battery health performance into consideration simultaneously would lead to a multi-objective optimal control problem. Dynamic programming is adopted to solve the optimization problem. The simulation results could testify the feasibility and efficiency of the proposed energy management strategy in this paper.

A contingency based energy management strategy for multi-microgrids considering battery energy storage systems and electric vehicles

Abstract The emergence of microgrids along with extending the use of new energy resources, energy storage systems and electric vehicles at distribution level has changed traditional distribution systems into multi-microgrids (MMGs) which are usually more stable and reliable. For an MMG system, the probability of a fault occurrence at each time period makes the system operation process more complex. From this point of view, this paper aims at proposing a coordinated energy management strategy for optimal operation of MMG systems using a variable weighted multi-objective function. Based on this method, in the case of occurrence of a contingency problem, multiple operators are able to change the weight of functions depending on contingencies and are responsible for the proper use of energy storage systems and other distributed energy resources. Moreover, an efficient optimization algorithm called targeted search shuffled complex evolution is proposed to quickly optimize decision parameters during faulted and normal operation modes. Finally, a unified framework is presented to implement the proposed energy management strategy along with the reliability study of the intended test system, and the ability of the proposed approach is investigated in a modified reliability-based case study by considering different scenarios.

A Two-Layer EMS for Cooperative Sugarcane-based Microgrids

This paper presents a solution for the energy magement and resource sharing problem of cooperative renewable-energy-based microgrids. Such solution is based on a two-level hierarchical optimization procedure to coordinate the operation of networked microgrids and optimally share their combined energetic resources, aiming to enlarge the renewable generation share while guaranteeing maximal efficiency and profit.The supervision layer is a Real-Time Optimization scheme, designed to determine energy-generation set-points for the distributed plants as well as resource sharing rules, taking into account their models, operational constraints and future predicted behaviour of the renewable sources. The lower layer is a Model Predictive Control loop, designed to follow energy generation set-points passed top-down by the upper layer and abide by demands, while respecting operational constraints.This work is focused on cooperative microgrids based on Brazilian sugarcane industries. Such plants are usually located in various sites, while being owned by the same company that has a fixed energy contract with the Distribution Network Operator. The biomass sources may be transported from one plant to another, if deemed convenient. This case study is taken into account and high-fidelity numerical simulations are presented to assess the effectiveness and qualities of the proposed energy management strategy, which yields overall good results.

Optimized energy management strategy for grid connected double storage (pumped storage-battery) system powered by renewable energy resources

This paper presents a grid-connected double storage system (DSS) consisting of pumped-storage hydropower (PSH) and battery. The system is supplied by photovoltaics and wind turbines. In the proposed hybrid system, batteries absorb excess renewable energy that cannot be stored in PSH and they cover loads that cannot be supplied from the water turbine. To improve the system performance, a novel energy management strategy for the DSS is proposed. The strategy is based on an optimized factor that governs the charging process of the DSS. The problem of the optimal system design is solved by a non-dominated sorting genetic algorithm (NSGA-II). The multi-objective function considers simultaneously the minimal investment cost and minimal CO2 emissions. A comparative study of photovoltaic/wind/pumped-storage hydropower and photovoltaic/wind/double storage system is performed to show the effectiveness of the proposed strategy in terms of system economic and environmental performance. The considered location of the PSH station is on Attaqa Mountain at Suez (Egypt). The results indicate the effectiveness of the proposed energy management strategy for the storage system from economic and environmental perspectives. Coupling the battery with the PSH reduces the electricity cost by 22.2% and results in minimal energy exchange with the national grid (5% of the annual demand). A sensitivity analysis shows the largest variation of the electricity cost with changing the capital cost of the solar and wind generators. Also, it is observed that when the load increases, the optimal size of the system components increases, but it isn’t proportional with the demand increase as could be expected.

Energy Management Strategy of a PEM Fuel Cell Excavator with a Supercapacitor/Battery Hybrid Power Source

Construction machines are heavy-duty equipment and a major contributor to the environmental pollution. By using only electric motors instead of an internal combustion engine, the problems of low engine efficiency and air pollution can be solved. This paper proposed a novel energy management strategy for a PEM fuel cell excavator with a supercapacitor/battery hybrid power source. The fuel cell is the main power supply for most of the excavator workload while the battery/supercapacitor is the energy storage device, which supplies additional required power and recovers energy. The whole system model was built in a co-simulation environment, which is a combination of MATLAB/Simulink and AMESim software, where the fuel cell, battery, supercapacitor model, and the energy management algorithm were developed in a Simulink environment while the excavator model was designed in an AMESim environment. In this work, the energy management strategy was designed to concurrently account for power supply performance from the hybrid power sources as well as from fuel cells, and battery lifespan. The control design was proposed to distribute the power demand optimally from the excavator to the hybrid power sources in different working conditions. The simulation results were presented to demonstrate the good performance of the system. The effectiveness of the proposed energy management strategy was validated. Compared with the conventional strategies where the task requirements cannot be achieved or system stability cannot be accomplished, the proposed algorithms perfectly satisfied the working conditions.

An Efficient Energy Management Approach for a Solar-Powered EV Battery Charging Facility to Support Distribution Grids

The home photovoltaic (HPV) system integrated with energy storages can supply power to the distribution grid which may be reliable and free from HPV intermittency effects. However, this is always associated with the high cost of energy storages. On the other hand, the growth of electric vehicles (EVs) in the market has a potential to place the distribution grid in a high risk as the EV owners may charge the EV battery on demand which may cause an unexpected increase in the evening and power quality problems. This article proposes an efficient energy management approach for the HPV systems to power the electric vehicle battery (EVB) charging facility while utilizing the EVB as an energy storage system (ESS) that can mitigate the HPV impacts and allow the growth of HPV systems in power grids. This research is aimed for EVs that are compatible with the dc fast charging CHAdeMO standard. The operation strategy of the HPV-EVB charging system is designed in such a way that the EVB is charged efficiently either by the HPV or by the distribution grid. The proposed energy management strategy will help reduce the unexpected peak power demand, and can help in the implementation of the vehicle-to-grid (V2G) to improve the stability of the grid during peak load. In addition, the EVB can provide power to the critical loads in the home when there is a loss of power supply from the grid. In the proposed system, the HPV, the grid, and the EVB converters share a common dc bus. Both simulation and experimental results show that the proposed energy management of the HPV-EVB system can reduce the impacts of the high penetration of EVs and HPVs on power distribution grids and can effectively improve the self-consumption of the HPV systems.

LPV-MPC fault-tolerant energy management strategy for renewable microgrids

This paper presents a solution for the Fault-Tolerant Energy Management problem of renewable energy microgrids. This solution is a Energy Management System (EMS) derived from a Model Predictive Controller (MPC) synthesized upon a Linear Parameter Varying (LPV) prediction model. This model describes the energy-generation process in both healthy and faulty operation conditions. The MPC is tuned to adequately coordinate the operation of the microgrid, aiming to optimally use its energetic resources, enlarge the renewable generation share and guarantee maximal efficiency and profit, despite the presence of faults (or even failures) in its subsystems. The quantification of the level of faults in the energy system is provided by an extended-state LPV fault estimation observer that works in parallel to the MPC. The proposed EMS, that acts at an hourly rate, finds time-varying control policies, that are passed as energy-generation set-points for the lower-layer subsystems, with respect to operational constraints, internal demands and taking into account the future (estimation) behaviour of the renewables. To validate the proposed fault-tolerant control scheme, a realistic, high-fidelity case study from the Brazilian sugarcane industry is considered. The achieved simulation results assess the effectiveness and qualities of the proposed energy management strategy; an overall good behaviour is exhibited in both faulty and healthy energy-generation conditions.

Fuel-Efficient Gear Shift and Power Split Strategy for Parallel HEVs Based on Heuristic Dynamic Programming and Neural Networks

In this paper, an online gear shift and power split strategy for parallel hybrid electric vehicles (HEVs) is proposed to optimize the fuel consumption. The gear shift control is implemented with a neural network (NN). The training of the NN is performed with data obtained from dynamic programming (DP) for different driving cycles. Simulation results present that the gear shift sequences and fuel consumption obtained from the NN and DP are very similar, underlining the near-optimal gear shift control for HEVs based on the NN. Furthermore, the power split control is realized with action-dependent heuristic dynamic programming (ADHDP). ADHDP does not need a system model and allows learning continuously. The power split control is therefore robust with respect to uncertainties and disturbances, as well as adaptive with respect to various driving situations and driver behaviors. The gear shift control based on the NN and the power split control based on ADHDP are combined to an adaptive energy management strategy with possible real-time application. It enables online learning of the controller and prevents the curse of dimensionality from DP calculation. Simulations for different driving cycles and comparisons with optimal solutions by DP indicate that the proposed energy management strategy is robust, adaptive, and near optimal.

Energy management strategy for hybrid electric vehicles based on adaptive equivalent consumption minimization strategy and mode switching with variable thresholds.

To improve the real-time capability, adaptivity, and efficiency of the energy management strategy in the actual driving cycle, a real-time energy management strategy is investigated for commute hybrid electric vehicles, which integrates mode switching with variable threshold and adaptive equivalent consumption minimization strategy. The proposed strategy includes offline and online parts. In the offline part based on the historical traffic data on the route of the commute vehicle, particle swarm optimization is applied to optimize all the thresholds of mode switching, equivalence factor of the equivalent consumption minimization strategy, and the engine torque and speed at the engine-alone propelling mode so as to establish their mappings on the battery state of charge and power demand. In the online part, the established mappings are involved in the energy management supervisor to generate timely appropriate mode switching signals, and an adaptive equivalence factor for instantaneous optimization equivalent consumption minimization strategy and the optimal engine torque and speed at engine-alone propelling mode. To fully demonstrate the effectiveness of the proposed strategy, the simulation results and comparison with some other strategies and the benchmark dynamic programming strategy are presented by implementing the strategies on the GT-SUITE test platform. The comparison result indicates that the control effect of the proposed energy management strategy is much nearer to that of the benchmark dynamic programming than those of other strategies (the rule-based control, the conventional equivalent consumption minimization strategy, the adaptive equivalent consumption minimization strategy, the rule-based-equivalent consumption minimization strategy, and the stochastic dynamic programming strategy) with the respective improvement in fuel efficiency by 25.9%, 13.25%, 4.6%, 1.32%, and 1.13%.

Real-Time Bilevel Energy Management of Smart Residential Apartment Building

This article elucidates a real-time energy management strategy for a smart residential apartment building having nonidentical occupants at the dwelling units (DUs). The aim of the present article is to design a distributed energy management algorithm, which can optimize the real-time demand of the entire building against abruptly updated rooftop solar generation and real-time price (RTP) of energy. The proposed energy management strategy differentiates among the DUs by considering a new parameter named load criticality level, which is defined as the value imposed by the DU residents to their power consumption. The optimization portfolio is developed as a novel bilevel, stochastic, multiobjective optimization problem where the maximization of utility of the consumed power is considered simultaneously with the cost minimization. To this end, a virtual energy trading platform is designed in this article between central building management system and the DUs, where they interact with each other by following the directives of single-leader multifollower Stackelberg game. The solution strategy is proposed as a Lyapunov optimization, which needs only the current values of the uncertain parameters, such as load variation, renewable generation, and energy price, and do not require any knowledge about their probabilistic variation, to eliminate the complexities regarding time average stochastic equations. Strenuous simulation on real-time data of four DUs, it is proved that the proposed framework can track the abrupt change in RTP and solar generation efficiently. Comparing with two benchmark methods viz. centralized process and greedy algorithm, the superiority of the designed energy management portfolio is established.

Energy Management Strategy for Solar-Powered UAV Long-Endurance Target Tracking

In this study, two types of three-dimensional (3-D) flight-based energy management strategies for solar-powered unmanned aerial vehicle (SUAV) long-endurance target tracking are proposed. In SUAV dynamic modeling, we consider the influence of wind on SUAV kinematics. Aiming at the complexity of the long-endurance tracking, namely the long flight time and space spans, the influences of the wind, variations of temperature, air density and solar cell photovoltaic efficiency, and a finite battery capacity are considered in our modified SUAV energy model. Then, considering the mission flexibility, we design two types of energy management strategies that comprehensively utilize the solar energy, gravitational potential energy, and wind to address different situations. The proposed strategies can be divided into several stages in chronological order according to the solar irradiation conditions, electrical energy storage conditions, and relative motion relationships among the SUAV, target, and wind. We then design respective strategies for these stages. The core of our proposed energy management strategies is to design the corresponding objective functions in each stage and the switching conditions between adjacent stages. Finally, we verify our proposed strategies by comparison with the traditional 2-D tracking strategy in a 24-h target tracking mission, and the results show the validity of our proposed strategies.

Innovation design and optimization management of a new drive system for plug-in hybrid electric vehicles

A multi-mode coupling drive system has been designed and controlled to improve the dynamic characteristics and fuel economy of plug-in hybrid electric vehicles, which also can make full use of the configured superiority of centralized drive systems and distributed drive systems and avoid their structural defects. The configuration evolution process, working mechanism and drive modes of the multi-mode coupling drive system are introduced. The powertrain model is established for the target vehicle. Based on Charge Depleting-Charge Sustaining energy management strategy, an Electric Vehicle-Charge Sustaining energy management strategy is developed. The Improved Real-valued Genetic Algorithm is used to optimize the system structural and control parameters, it can help prioritize the drive modes which are based on the proposed energy management strategy. While ensuring the vehicle dynamics, the best energy allocation is achieved. The results show that comparing with a series distributed drive hybrid system and the intelligent Multi-Mode Drive (i-MMD) hybrid system under the NEDC condition, the 100-km fuel consumption of the optimized multi-mode coupling drive system is reduced by 16.52% and 15.40%. Respectively, it further proves the superiority of the drive system in improving vehicle economy.

Combined passivity based control and optimal control for energy management of fuel cell/battery hybrid system

AbstractThe energy management of hybrid electric vehicles is becoming an interesting topic for many researchers. Furthermore, the wise choice of the energy management strategy allows not only the best distribution of the power between the used sources, but also it allows reduction of consumption, increase in the lifetime of the sources, and improves the autonomy of the hybrid electric vehicle. The autonomy is guaranteed by the optimization of the embedded sources. In this study, the hybrid system consists of combining the fuel cell as the main source with the battery as the auxiliary source. The novelty of the proposed energy management strategy for the studied hybrid system is the combination between interconnection and damping assignment‐passivity based control and the Hamiltonian Jacobi Bellman method. The stability proof is given and the efficiency of the proposed strategy is proved by the experimental work, where the obtained results show the good and adequate results to the proposed scenario.

Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor

A parallel hybrid electric vehicle (PHEV) is used to investigate the fuel economy effect of the equivalent fuel consumption minimization strategy (ECMS) with the equivalent factor as the core, where the equivalent factor is the conversion coefficient between fuel thermal energy and electric energy. In the conventional ECMS strategy, the battery cannot continue to discharge when the state of charge (SOC) is lower than the target value. At this time, the motor mainly works in the battery charging mode, making it difficult to adjust the engine operating point to the high-efficiency zone during the acceleration process. To address this problem, a relationship model of the battery SOC, vehicle acceleration a, and equivalent factor S was established. When the battery SOC is lower than the target value and the vehicle demand torque is high, which makes the engine operating point deviate from the high-efficiency zone, the time that the motor spends in the power generation mode during the driving process is reduced. This enables the motor to drive the vehicle at the appropriate time to reduce the engine output torque, and helps the engine operate in the high-efficiency zone. The correction function under US06 condition was optimized by genetic algorithm (GA). The best equivalent factor MAP was obtained with acceleration a and battery SOC as independent variables, and the improved global optimal equivalent factor of ECMS was established and simulated offline. Simulation results show that compared with conventional ECMS, the battery still has positive power output even when the SOC is less than the target value. The SOC is close to the target value after the cycle condition, and fuel economy improved by 1.88%; compared with the rule-based energy management control strategies, fuel economy improved by 10.17%. These results indicate the effectiveness of the proposed energy management strategy.

Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning

The energy management approach of hybrid electric vehicles has the potential to overcome the increasing energy crisis and environmental pollution by reducing the fuel consumption. This paper proposes an online updating energy management strategy to improve the fuel economy of hybrid electric tracked vehicles. As the basis of the research, the overall model for the hybrid electric tracked vehicle is built in detail and validated through the field experiment. To accelerate the convergence rate of the control policy calculation, a novel reinforcement learning algorithm called fast Q-learning is applied which improves the computational speed by 16%. The cloud-computation is presented to afford the main computation burden to realize the online updating energy management strategy in hardware-in-loop simulation bench. The Kullback-Leibler divergence rate to trigger the update of the control strategy is designed and realized in hardware-in-loop simulation bench. The simulation results show that the fuel consumption of the fast Q-learning based online updating strategy is 4.6% lower than that of stationary strategy, and is close to that of dynamic programming strategy. Besides, the computation time of the proposed method is only 1.35 s which is much shorter than that of dynamic programming based method. The results indicate that the proposed energy management strategy can greatly improve the fuel economy and have the potential to be applied in the real-time application. Moreover, the adaptability of the online energy management strategy is validated in three realistic driving schedules.

Heuristic Dynamic Programming Based Online Energy Management Strategy for Plug-In Hybrid Electric Vehicles

For the online energy optimization problem of plug-in hybrid electric vehicles (P-HEVs), this paper proposes a heuristic dynamic programming (HDP) based online energy management strategy, to minimize the fuel consumption of the P-HEV. First of all, considering the uncertain nonlinear dynamic process of a vehicle in the actual traffic environment, we adopt the back propagation neural network (BPNN) to construct the dynamic model of the P-HEV. Then, on this basis, we utilize the HDP to establish an energy management controller with the aim of minimizing energy consumption of the P-HEV. Moreover, the energy management controller is implemented by an online energy management strategy algorithm. To verify the effect of the controller, we employ a practical route in Beijing road network to simulate the BPNN model of the P-HEV and the proposed energy management strategy. The experimental results show several advantages of our strategy. First, compared to the analytic model, the BPNN model can reflect the real dynamic process of the P-HEV with a higher precision. Second, the assigned torques by the strategy can effectively make the vehicle track the desired vehicle-speeds, and the tracking accuracy of the vehicle-speed is higher than 98%. Besides, on the premise of ensuring the real-time performance, the proposed strategy can further reduce the fuel consumption and emissions of the P-HEV when compared with the existing online energy management strategies, although its fuel consumption is more than that of the offline global optimization energy management strategy by 4% approximately.