Optimal Power Management Strategy Research Articles

Optimization of PV benefits for electric vehicles charging station using a deterministic method and fuzzy inference system method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of −863.4c€/day, whereas the FIS method delivers greater savings of −898.7c€/day. For fast charging, the deterministic approach yields savings of −524.6c€/day, while the FIS method results in even higher savings of −540.7c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20 %, compared to 81.60 % with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

Enhanced hybrid energy storage system combining battery and supercapacitor to extend nanosatellite lifespan

The power limitations of nanosatellites, particularly during peak demand periods such as data transmission and maneuvering, hinder their operational capabilities. Traditional Electrical Power Subsystems relying solely on batteries face challenges in delivering the necessary power density and maintaining battery life, especially during eclipses. This study proposes an innovative Hybrid Energy Storage System for a 3U nanosatellite, integrating high-energy-density batteries with high-power-density supercapacitors, using an active parallel hybrid topology with two bidirectional converters and an optimal power management strategy. Using MATLAB and Simulink models, the study optimizes the Hybrid Energy Storage System by focusing on minimizing the capacity rate and depth of discharge to extend battery life. Simulation results show a 53.42% reduction in depth of discharge compared to a battery-only system, indicating a significant extension of battery life. Additionally, the proposed system allows for a minimal 2.08% increase in overall mass while maintaining enhanced performance. This research fills a critical gap in the literature by exploring active Hybrid Energy Storage System topologies for spacecraft applications, beyond the traditional passive and semi-active configurations. The innovative power management strategy ensures efficient energy utilization, significantly enhancing the reliability and efficiency of nanosatellite missions. The findings offer a practical solution to improve mission performance and extend the operational lifespan of nanosatellites, paving the way for more robust and capable small satellite deployments.

Pontryagin’s Minimum Principle-Based Power Management of Plug-In Hybrid Electric Vehicles to Enhance the Battery Durability and Thermal Safety

Temperature affects the battery aging and stability, bringing about significant effects on the electric vehicle’s economy, reliability, and safety. Therefore, it is necessary to consider the battery thermal condition when designing the power management scheme. In this paper, an optimal power management strategy for plug-in hybrid electric vehicles is proposed based on Pontryagin’s Minimum Principle (PMP). Two situations, normal operation and cooling system failure, are considered. The impacts of battery aging and temperature rise are characterized based on durability analysis model and thermal model. The temperature variation rate of the battery is incorporated into the Hamiltonian function, and the correlation between the cost optimization and temperature rise is disclosed by shooting method. Then the optimal co-state variables in different cases are determined. Based on above efforts, the PMP-based multi-objective optimal control strategy is proposed and evaluated based on the temperature statistical data in Shenyang, China. The results show that the proposed method can improve the durability of the battery and inhibit the excessive temperature rise. In case of the cooling system failure, it can realize the trade-off between safety and economy to reduce the risk of battery thermal runaway.

A multi-objective optimal power management strategy for enhancement of battery and propellers lifespan in all-electric ships

This paper proposes a multi-objective optimum approach for the participation of the propulsion system in mitigating the All-Electric Ship (AES) power system fluctuations. This strategy optimally balances the propellers torque/speed change and the energy storages charge/discharge rate to obtain an economically-efficient operating point. The method aims to minimize the propeller mechanical fatigue and the battery loss of life (LoL) according to their investment costs, while other operational constraints are considered. For this purpose, a novel index, called the Propulsion System Cooperation Coefficient (PSCC), is developed. This index adjusts the magnitude of the propeller speed/torque variations regarding the AES investment costs, components LoL, and power quality. A straightforward procedure is proposed to acquire this coefficient during an operating condition. The simulations revealed that the proposed multidisciplinary approach remarkably reduces the AES operation and maintenance expenditures associated with the loss of life of the mechanical and electrical equipment. This outcome holds with the vessel being either operational or at the design level. Since the proposed strategy demonstrated that addressing mechanical fatigue damage is significant in a demand-side control approach, it can also benefit the terrestrial microgrids power management systems.

An Offline Closed-Form Optimal Predictive Power Management Strategy for Plug-In Hybrid Electric Vehicles

This article develops an optimal predictive power management strategy (OP-PMS) for plug-in hybrid electric vehicles (PHEVs) to manage the remaining battery level throughout a defined journey. Apart from using the battery before charging it again at the destination, this approach also supports transit through an Ultra Low Emission Zone (ULEZ), where a PHEV needs to operate in pure electric mode to avoid emissions. This type of PHEV power management strategy (PMS) design problem is a noncausal control problem, where the power demand of the remaining driving cycle and the final battery energy level targets influence the current power management action. Rather than solving the whole optimization problem online, this article presents a simplification that leads to a closed-form analytic solution with parameters that can be computed offline. This article makes three key contributions. First, a novel input-linearization method is developed, which converts the OP-PMS into a convex quadratic programming (QP) problem that captures the essential model nonlinearities using an input transformation and a quadratic cost function. Second, the influence of future power demands and final targets on current energy management policy is explicitly quantified from a dynamic optimal control perspective. Third, the noncausal convex QP is approximated with a closed-form analytic solution that is calculated offline. This leads to a real-time implementable OP-PMS with coefficients that can be precalculated and stored in the engine control unit. To demonstrate the viability of this approach, numerical examples are provided based on a generic PHEV model to verify the efficacy of the proposed OP-PMS.

Energy management for solar-hydrogen microgrids with vehicle-to-grid and power-to-gas transactions

This study addresses the energy management of an integrated solar-hydrogen microgrid. The microgrid includes zero-emission vehicles, renewable energy sources, an electrolyzer, bidirectional charging stations, and a hydrogen refueling station with hydrogen storage. Vehicle-to-grid (V2G) charging stations can alleviate renewable electricity variability by discharging the energy of vehicle batteries back to the grid. The electrolyzer can absorb excess solar generation to balance supply and demand with power-to-gas (P2G) transactions. Given the uncertainties from intermittent renewable generation and energy demand, a two-stage stochastic optimization framework derives the optimal power management strategy for the component subsystems, aiming to minimize operating costs. The first stage of the model determines the day-ahead charging price and power supply for electric vehicle charging and hydrogen production, and the second stage performs real-time energy scheduling under different scenarios. The performance of the proposed optimization strategy is examined through a case study using real-world data, and the results demonstrate that daily operating costs can be reduced by up to 27.5%. Moreover, inclusion of the P2G process with hydrogen storage yields better performance to support peak shaving and reducing costs compared to using V2G or dynamic pricing-based demand response policies only.

HİBRİT GÜÇ SİSTEMİ İLE İKLİMLENDİRME YÜKLERİNİN OPTİMAL İŞLETİM MODELİ VE HASTANE İKLİMLENDİRME YÜKLERİ İÇİN TALEP CEVABI UYGULAMASI

Bu çalışmada, kamu kuruluşları, hastane, okul vb. gibi kurumların iklimlendirme cihazlarının yük talebinin karşılanmasında alternatif enerji kaynaklarından optimal olarak yararlanma stratejisi ele alınmıştır. İklimlendirme yüklerinin karşılanmasında rüzgar türbini (RT), güneş pili (GP) ve batarya (Bat) ile teşkil edilen hibrit güç sistemi önerilmiştir. Hibrit güç sistemi optimal talep cevabı uygulamasında kullanılmasına imkan verecek şekilde modellenmiştir. Ayrıca, hibrit kaynakların kesintili üretim karakteristikleri gereği hibrit güç sistemine şebeke gücü de ilave edilmiştir. Optimizasyon probleminde alternatif enerji kaynaklarının maksimum kapasitede kullanımı ve ilgili kaynakların öncelikli olarak iklimlendirme yüklerinin talebine cevap verecek şekilde kullanımı amaç olarak belirlenmiştir. Elde edilen karışık tamsayı lineer programlama modeli GAMS v.24.1.3 ortamında CPLEX v.12 çözücüsü ile test edilmiştir. Kış ve yaz koşulları dikkate alınarak optimal işletim modelinin benzetim çalışmaları yapılmıştır ve ilgili sonuçlar sunulmuştur.

Optimal power management strategy by using fuzzy logic controller for BLDC Motor-Driven E-Rickshaw

In this paper, the authors have addressed the modeling and design of the BLDC Motor-Driven E-Rickshaw based on hybrid energy storage system (HESS) for optimum power management using fuzzy logic. In Hybrid energy sources, solar power is used to charge a battery (primary source) that is effectively coupled to supercapacitor (ancillary source) for peak demand supplies. A power-split control strategy is proposed to control the power supply by using the HESS Fuzzy Logic in different engine operating modes. Projected power layering improves the battery life cycle with the proper use of the Supercapacitor. By providing a new switching algorithm, the DC link voltage is boosted to effectively transfer power to the HESS unit. Fuzzy logic-based HESS provides better performance in electric vehicles, such as deep discharge protection of the battery, and faster acceleration. Also, there is a quick comparison of E-rickshaw solar power with traditional E-rickshaw. The planned design model was simulated by MATLAB®/Simulink environment.

Experimental Study of an Optimal Power Management Strategy for Off-Grid RES/Battery System

Experimental Study of an Optimal Power Management Strategy for Off-Grid RES/Battery System

Hybrid Sources Powered Electric Vehicle Configuration and Integrated Optimal Power Management Strategy

Internal Combustion Engine based transportation rapidly increases the impact on the environment. The burning of fossil fuels in the industries and transportation sector breadth of global warming. However, Hybrid Electric Vehicle is not a permanent solution for emission-less road vehicles. Therefore, electric vehicles are the most feasible technologies to attain the goals of energy savings and zero-emission road vehicles. This paper sequentially surveys the key point of vehicles like; Powertrain strategy, configuration, fuel economy, reduced emission, and control strategy expounds in terms of its basic principles, pros, and cons. These key points make the energy management strategy more conclusive for the more efficient vehicle. In addition, this paper review systematically qualitative and quantitative algorithm in all type of EMS used in HEV and compare them with existing approaches in terms of pros & cons through a comprehensive analysis. Furthermore, addressed the potential research gap and provide the directives for further development in power train and ems in every respect.

A Real-Time Optimal Car-Following Power Management Strategy for Hybrid Electric Vehicles with ACC Systems

This paper develops a model predictive multi-objective control framework based on an adaptive cruise control (ACC) system to solve the energy allocation and battery state of charge (SOC) maintenance problems of hybrid electric vehicles in the car-following scenario. The proposed control framework is composed of a car-following layer and an energy allocation layer. In the car-following layer, a multi-objective problem is solved to maintain safety and comfort, and the generated speed sequence in the prediction time domain is put forward to the energy allocation layer. In the energy allocation layer, an adaptive equivalent-factor-based consumption minimization strategy with the predicted velocity sequences is adopted to improve the engine efficiency and fuel economy. The equivalent factor reflects the extent of SOC variation, which is used to maintain the battery SOC level when optimizing the energy. The proposed controller is evaluated in the New York City Cycle (NYCC) driving cycle and the Urban Dynamometer Driving Schedule (UDDS) driving cycle, and the comparison results demonstrate the effectiveness of the proposed controller.

A novel optimal power management strategy for plug-in hybrid electric vehicle with improved adaptability to traffic conditions

Adaptability to various driving conditions (TCs) is one of the essential indicators to assess the optimality of power management strategies (PMSs) of plug-in hybrid electric vehicles (PHEVs). In this study, a novel optimal PMS with the improved adaptability to TCs is proposed for PHEVs to achieve the energy-efficient control in momentary scenarios by virtue of advanced internet of vehicles (IoVs), thus contributing to remarkable promotion in fuel economy of PHEV. Firstly, the optimal control rules in the novel PMS, corresponding to diverse driving conditions, are optimized offline by the chaotic particle swarm optimization with sequential quadratic programming (CPSO-SQP), which can effectively endow the global optimization knowledge into the rule inspired method. Then, an online TC identification (TCI) method is designed by cooperatively exploiting multi-dimensional Gaussian distribution (MGD) and random forest (RF), where the MGD based analysis on the macrocosmic state of traffic contributes to valuable inputs for the RF based TC classification, and additionally the super regression ability of RF further improves the identification accuracy. Finally, the numerical simulation validations showcase that the novel optimal PMS can reasonably and instantly manage the power flow within power sources of PHEV under different TCs, manifesting its anticipated preferable controlling performance.

Energy Bill Reduction by Optimizing Both Active and Reactive Power in an Electrical Microgrid

The aim of this paper is to develop tools for the Optimal Power Flow Management Control (OPFMC) in a microgrid (MG). It consists of a photovoltaic system, an energy storage system, a gas turbine, and a main grid (energy exchange). Unlike the conventional approaches, which are limited to the distribution of the active power, this paper relies on an electrical modeling system in order to coordinate and to optimize both the active and the reactive power flow using discrete controls. The proposed optimal power management strategy has two objectives: (i) it aims at forecasting, over a time horizon of 24-hours, the optimal distribution of the active and the reactive power required for each power source connected to the microgrid. The proposed management incorporates the consumption forecasts, the weather, and the tariffs; (ii) it aims at reducing the solicitation of the main grid while taking into consideration the requested reactive power. This is done by using dynamic programming based on Bellman algorithm. Such management has been tested by simulation in a Matlab/Simulink environment and its performance has been compared to the conventional management strategy.

Hybrid electric topology for short sea ships with high auxiliary power availability requirement

The inclusion of a battery system for a diesel mechanical short sea ship was investigated. The main benefits of the battery were assumed to emerge from shaving thruster generated power peaks, rather than starting additional generating sets to accommodate the power demand and additionally from replacing a diesel engine as a reserve power source. To support the analysis, an auxiliary engine power output dataset of a roll-on/roll-off passenger ferry operating in the Baltic Sea was acquired. Required capacity for the battery system was derived by considering power availability requirements and battery safety margins for performance deterioration. A multi-period mixed-integer linear programming model was developed to derive a globally optimal power management strategy for the auxiliary engines and the battery, with the goal of minimizing the battery installation total cost. The battery system was found to reduce fuel oil consumption by 257.5 tons annually due to improved auxiliary engine efficiency alone. Furthermore, the battery system total cost advantage was found to vary from -€0.61 to €2.82 million during the ten-year investment period, depending on fuel oil and battery system costs applied in the modeling. For the studied case ship, the hybrid electric topology was concluded to be economically feasible.

Constrained stability control with optimal power management strategy for in-wheel electric vehicles

This paper looks into the energy management and directional stability of four-in-wheel driven electric vehicles, simultaneously. In the proposed strategy, the optimal driving torques are initially distributed between the wheels by considering the condition for minimum losses of motors using the motor efficiency model. In risky maneuvers, a novel optimal torque vectoring system is developed to intentionally change the initial optimal torques for the generation of required stabilizing yaw moment. For designing the stability controller, a new constrained control method is analytically developed based on the prediction of continuous nonlinear vehicle models. The proposed control method restricts the side-slip angle to guarantee the stability. Also, the required control torque for each motor is restricted within the admissible range according to the motor map. As another result of the constrained strategy, a small change in the optimal energy consumption is occurred for improved stability because of using minimum external yaw moment. In simulation studies, a good performance of the developed control system to provide both directional stability and drivability of electric vehicle with high energy efficiency is presented at different driving conditions using 14-degrees-of-freedom vehicle model. A comparative study with the conventional model predictive control method indicates the speed of the proposed constrained control method and the ease of its solution and implementation.

Adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for four-wheel drive hybrid electric vehicles

An adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for a four-wheel drive plug-in hybrid electric vehicle is proposed in this paper. The equivalent factors of adaptive equivalent consumption minimisation strategy are optimised offline based on ISIGHT software over several typical driving cycles, which is integrated with AVL CRUISE and MATLAB/Simulink. To update the equivalent factor adaptively according to the predictive velocity, a neural network-based optimal equivalent factor prediction model is built, which can be used online. The torque distribution strategy considering axle load based on energy management strategy optimisation results and the vehicle dynamics control distribution is proposed: this includes two-wheel drive torque distribution, four-wheel drive torque distribution and brake torque distribution. The proposed energy management strategy is verified in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle driving patterns, and the simulation results show that the fuel economy of adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy is improved by 8.84% and 7.52% in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle, respectively, compared with the benchmark algorithm-based strategy.

New Optimal Power Management Strategy for Series Plug-In Hybrid Electric Vehicles

Recently Plug-in hybrid electric vehicles (PHEVs) have gained increasing attention due to their ability to reduce the fuel consumption and emissions. In this paper a new efficient power management strategy is proposed for a series PHEV. According to the battery state of charge (SOC) and vehicle power requirement, a new rule-based optimal power controller with four different operating modes is designed to improve the fuel economy of the vehicle. Furthermore, the teaching-learning based optimization (TLBO) method is employed to find the optimal engine power and battery power under the specified driving cycle while the fuel consumption is considered as the fitness function. In order to demonstrate the effectiveness of the proposed method, four different driving cycles with various numbers of driving distances for each driving cycle are selected for the simulation study. The performance of the proposed optimal power management strategy is compared with the rule-based power management method. The results verify that the proposed power management method could significantly improve the fuel economy of the series PHEV for different driving conditions.

A graphical approach to optimal power management for uncertain OFF-Grid PV-FC-electrolyzer-battery hybrid systems

The problem of optimal power management for uncertain off-grid hybrid systems is studied in this paper. A suitable parametrization of the energy sources and sinks is used to obtain a graphical representation of the feasible set along with a direct observation of the solution and its piecewise description. The proposed approach pretends to clarify how the optimal solution migrates from the use of one source to another as the solar radiation changes and how the dynamic response of the sources restrict the feasible set. A power generation system (PGS) is used to study the limitations that the sun power variability and the parametric uncertainty impose on the optimality; being the PGS constituted by photovoltaic (PV) panels, a fuel cell (FC), an electrolyzer (E) and a battery bank. A simple optimal Power Management Strategy (PMS) is proposed as a result of the analysis and its performance is illustrated in a telecom facility for three different locations in Mexico. Results are shown indicating the viability of the strategy for highly variable environments.

Control of Energy Storage

In the attempt to tackle the issue of climate change, governments across the world have agreed to set global carbon reduction targets. [...]

Off grid PV system for hydrogen production using PEM methanol electrolysis and an optimal management strategy

In this paper, we present a modelling and simulation study of an Off grid PV system for hydrogen production (PVHPS) using methanol electrolysis process. The system consists mainly on a PV array, lead-acid battery, methanol electrolyser and hydrogen tank. Mathematical models of each component of the system are presented. Using a semi-empirical relationship between hydrogen production rate and power consumption, hydrogen production at 80 °C and 4 M concentration is estimated. Optimal power and hydrogen management strategy (PHMS) is investigated to achieve high system efficiency. Case studies are carried out on two tilts of PV array: horizontal and tilted at 36°. Parametric sensitivity analysis is also carried out to illustrate the effect of the battery depth of discharge (DoD) on the system components sizes and overall system performance. In the simulation, data of solar irradiation and ambient temperature obtained from radiometric measurements at Algiers city are used. Simulation results show that the system with tilted PV array presents performances better than in horizontal PV array configuration and produce greater hydrogen amounts. In addition, reducing the DoD value in order to increase the lifetime of the accumulator induces some power losses which decreases the amount of hydrogen produced and consequently reduces the system efficiency.