Desired State Of Charge Research Articles

Modeling state-of-charge dependent mechanical response of lithium-ion batteries with volume expansion

The behavior of lithium-ion batteries under mechanical abuse conditions has been studied extensively in recent years. Typically, characterization is performed by conducting experiments on discharged batteries to minimize the chances of thermal runaway and fire. However, studies have shown that material models calibrated at discharged state may not be as accurate at higher states of charge. In this study, a combined experimental-numerical method is proposed to simulate the stiffening of mechanical response in electrode stacks/jellyrolls of lithium-ion batteries at various states of charge and levels of confinement. Our study indicates that the mechanical properties of the jellyroll do not inherently change as a function of the state of charge, rather, the primary cause of the higher stiffness in charged batteries is the volume expansion of the jellyroll during charging. Therefore, mechanical characterization tests are conducted at the discharged state and the expansion of the jellyroll is induced in the finite element models to match the equivalent volume at the desired state of charge. In the second phase of the simulation, mechanical abuse loading is applied to validate the accuracy of simulations using the test data. This method was applied and validated for both pouch and cylindrical batteries with high accuracy.

A deep learning based encoder-decoder model for speed planning of autonomous electric truck platoons

Electric truck platooning offers a promising solution to extend the range of electric vehicles during long-haul operations. However, optimizing the platoon speed to ensure efficient energy utilization remains a critical challenge. The existing research on implementing data-driven solutions for truck platooning remains limited and implementing first principles solution is still a challenge. However, recognizing the resemblance of truck platoon data to a time series serves as a compelling motivation to explore suitable analytical techniques to address the problem. This paper presents a novel deep learning approach using a sequence-to-sequence encoder-decoder model to obtain the speed profile to be followed by an autonomous electric truck platoon considering various constraints such as the available state of charge (SOC) in the batteries along with other vehicles and road conditions while ensuring that the platoon is string stable. To ensure that the framework is suitable for long-haul highway operation, the model has been trained using various known highway drive cycles. Encoder-decoder models were trained and hyperparameter tuning was performed for the same. Finally, the most suitable model has been chosen for the application. For testing the entire framework, drive cycle/speed prediction corresponding to different desired SOC profiles has been presented. A case study showing the relevance of the proposed framework in predicting the drive cycle on various routes and its impact on taking critical policy decisions during the planning of electric truck platoons has also been presented. This study would help to efficiently plan the feasible routes for electric trucks considering multiple constraints such as battery capacity, expected discharge rate, charging infrastructure availability, route length/travel time, and other on-road operating conditions while also maintaining stability.

Optimal charging of electric vehicles for cost minimization in re-configurable active distribution network considering conservation voltage reduction

ABSTRACT This paper presents a coordinated plug-in electric vehicle (PEV) charging scheme in the presence of conservation voltage reduction (CVR) with a possibility of network re-configuration in a half-hourly based tariff environment. The information of the arrived PEVs in time slot “t” are gathered in a centralized manner, including initial and desired state of charge (SOC), rating of PEV charger, and arrival and departure times. In addition, the load forecasted data of all the nodes are also collected. A linear programming (LP) based intelligent charging framework is developed whereby an optimal global solution for EV charging is obtained that tends to minimize the charging cost, and the schedule is dispatched to all the PEVs. At first, PEV charging cost is minimized using the existing time of use (TOU) based tariff structure. After that, the charging schedule is carried out under the same TOU tariff structure but on a re-configured network with and without CVR deployment. The proposed charging strategy through re-configuration of the distribution network under CVR is implemented on a modified IEEE-33 bus test system. The results obtained from the simulation show that the proposed PEV charging scheme through network re-configuration under a TOU-based tariff structure considerably reduces the charging cost by 55.8% compared to uncoordinated charging. However, a 50.2% cost reduction is achieved in the absence of network re-configuration. Therefore, the proposed PEV charging through network re-configuration under a TOU-based tariff is more effective in cost reduction. Further, investigations were carried out on EV charging through network re-configuration under TOU-based tariff and CVR deployment. It was observed that the PEV charging cost was reduced by 51.3% compared to uncoordinated charging, where higher cost benefits were obtained through the reduction in energy consumed by constant impedance loads. It has been further observed that the proposed scheme effectively flattens the load profile by clipping the peak and filling the valley load.

Optimizing Storage with Artificial Intelligence

This paper presents the battery energy control system by using Fuzzy Logic Controller (FLC) for a renewable energy sources (Solar Panel, Wind Turbine). A fuzzy control strategy is used in this article for battery control. To improve battery life, fuzzy control manages the desired state of charge (SOC).By using MATLAB/ Simulink, the modelling, analysis and control of the energy generator devices and energy storage devices (ESD) are proposed.

Study of the oversized capacity and the increased energy loss of hybrid energy storage systems and design of an improved controller based on the low-pass filter

A hybrid energy storage system (HESS) consisting of batteries and supercapacitors (SCs) is an effective approach to stability problems brought by renewable energy sources (RESs) in microgrids. This paper investigates the energy exchange between the two energy storage devices (ESDs) caused by the low-pass filter (LPF), which leads to the oversized capacity of HESSs. In addition, the energy exchange between the ESDs leads to more energy loss of HESSs. Based on the analysis of the power flows, this paper proposes an improved controller based on the LPF controller. A power direction control strategy eliminates the non-beneficial power flow to reduce the capacity of HESSs and improve the round-trip energy efficiency. In addition, a SOC control strategy regime balances the desired state of charge (SOC) of the ESDs instead of depending on the LPF. In this paper, the case study shows that the improved LPF controller reduces the capacity of the HESS to the minimized capacity and improves the round-trip energy efficiency. Furthermore, it has no adverse effect on battery aging and achieves the battery lifetime extension with a smaller capacity. A scaled-down HESS experimental setup validates the effectiveness of the improved LPF controller and the simulation results. Finally, the proposed improved controller is compared with various existing controllers to verify the performance.

Off-line and on-line optimization of the energy management strategy in a Hybrid Electric Helicopter for urban air-mobility

This study addresses the optimization of the energy flows in a Hybrid Electric Helicopter for air-taxi operation in order to minimize the fuel consumption on four typical missions (defined in terms of power and altitude profiles) and contemporarily allowing electric back-up operation at any time during the mission. The proposed parallel hybrid electric powertrain includes a turboshaft engine, two electric machines, a lithium ion battery and all necessary control systems. The powertrain is modeled with an empirical but comprehensive approach in order to make possible the combination with numerical optimization algorithms. In particular, the battery is modeled with an electric equivalent circuit model that includes the effect of battery aging. The fuel consumption of the turboshaft engines is calculated with a mathematical function of power request, altitude and Mach number whose coefficients are fitted by means of comparison with the commercial code Gas-turbine Simulation Program (GSP). The optimization of the energy management of the hybrid powertrain is performed in two steps. The first one was the application of Dynamic Programming in order to obtain the optimal usage of the battery for a given mission (target values) and to provide insights into how to develop a suitable on-line optimizer to be applied during the real operation of the rotorcraft. For this second step analysis, the authors developed a version of the Equivalent Consumption Minimization Strategy opportunely adapted to the specific case of an aerial vehicle with turboshaft engine, in particular taking in to account the desired state of charge and the actual state of health of the battery. After an off-line optimization of its parameters with a multi-objective approach, the on-line optimizer guaranteed results similar to the target values and allowed a reduction of the fuel burn ranging between 10% and 22% with respect to using only the thermal engine to power the rotor shaft and without any need to adapt the parameters in case of aged battery.

Management and Control of a DC Bus Powered by Renewable Energies

This article proposes a method of management and control of a continuous bus powered by renewable energies for autonomous applications. The DC bus is obtained from two systems of renewable sources (the solar system and the wind system) and storage battery (Lithium Ion). The continuous bus control and management procedure require efficiency in the control of the charge and discharge of the battery according to the load energy demand (DC Motor). The battery charging process is non-linear, varying over time with considerable delay, so it is difficult to achieve the best performance on control with energy management using traditional control approaches. A fuzzy control strategy is used in this article for battery control. To improve battery life, fuzzy control manages the desired state of charge (SOC). The entire system designed is modeled and simulated on MATLAB/Simulink Environment.

MODELLING AND CONTROL OF RURAL PV MICRO GRID USING FUZZY LOGIC CONTROLLER

This paper proposes an approach for the hybrid solar photovoltaic and Battery management for stand-alone applications. Battery charging process is non-linear, time-varying with a considerable time delay so it is difficult to achieve the best energy management performance by using traditional control approaches. A fuzzy control strategy for battery charging or discharging used in a renewable power generation system is analyzed in the paper. To improve the life cycle of the battery, fuzzy control manages the desired state of charge. A fuzzy logic-based controller to be used for the Battery SOC control of the designed hybrid system is proposed. This paper presents a general description of the implemented microgrid topology. The exact linearization theory adapted for power converters is applied to both a Single-Ended PrimaryInductor converter (SEPIC) to extract energy from PV modules and to a Boost converter to increase the voltage. The entire designed system is modeled and simulated using MATLAB/Simulink environment.

Distributed energy management for ship power systems with distributed energy storage

ABSTRACTElectric systems for naval applications create a challenge for the power system associated control. When incorporating loads with a high-power ramp rate within what is essentially an islanded microgrid, energy sources that supplement generators must be used due to the ramp rate constraints of the generators; this is where energy storages play a key role. A higher-level control layer is needed to effectively coordinate the distributed energy storage in order to ensure that the effect on the generators is minimised. The Energy Management layer is responsible for maintaining the desired state of charge for the distributed energy storage and ensuring that load demand is met while minimising ramp rate violations. In this paper, a distributed Energy Management scheme for a 4-zone ship power system is presented. Experimental results validate the proposed control technique using hardware controllers interfacing with a real-time simulator.

Controller to enable the enhanced frequency response services from a multi‐electrical energy storage system

The increased adoption of renewable energy generation is reducing the inertial response of the Great Britain (GB) power system, which translates into larger frequency variations in both transient and pseudo-steady-state operation. To help mitigate this, National Grid, the transmission system operator in GB, has designed a control scheme called enhanced frequency response (EFR) specifically aimed at energy storage systems (ESSs). This study proposes a control system that enables the provision of EFR services from a multi-electrical ESS and at the same time allows the management of the state of charge (SOC) of each ESS. The proposed control system uses a Fuzzy Logic Controller to maintain the SOC as near as possible to the desired SOC of each ESS while providing EFR. The performance of the proposed controller is validated in transient and steady-state domains. Simulation results highlight the benefits of managing the SOC of the energy storage assets with the proposed controller. These benefits include a reduced rate of change of frequency and frequency nadir following a loss of generation as well as an increase in the service performance measure which renders into increased economic benefits for the service provider.

Practical Analysis and Design of a Battery Management System for a Grid-Connected DC Microgrid for the Reduction of the Tariff Cost and Battery Life Maximization

This study is focused on two areas: the design of a Battery Energy Storage System (BESS) for a grid-connected DC Microgrid and the power management of that microgrid. The power management is performed by a Microgrid Central Controller (MGCC). A Microgrid operator provides daily information to the MGCC about the photovoltaic generation profile, the load demand profile, and the real-time prices of the electricity in order to plan the power interchange between the BESS and the main grid, establishing the desired state of charge (SOC) of the batteries at any time. The main goals of the power management strategy under study are to minimize the cost of the electricity that is imported from the grid and to maximize battery life by means of an adequate charging procedure, which sets the charging rate as a function of the MG state. Experimental and simulation results in many realistic scenarios demonstrate that the proposed methodology achieves a proper power management of the DC microgrid.

Power factor correction and load demand peak shaving in residential town using electric vehicles fleet in smart parking

This paper presents an algorithm for compensating the active and reactive power simultaneously in a real residential distribution household system using the amount of energy saved in plug-in hybrid electric vehicles (PHEVs) or in electric vehicles (EVs) parked in smart parking. The system and vehicle's specifications are discussed in separate sections. Moreover, the effects of the algorithm on active power peak shaving, reactive power compensation, and power factor correction are discussed as well. The economic effect of the algorithm on consumers' energy payment bills in Iran's distribution market is depicted in Section 6. Reactive power compensation leads into correction of power factor and power loss reduction. The main reason for connecting vehicles to grid is to charge them in their departure time to the desired state of charge (SOC). This algorithm guarantees that all vehicles will reach their desired state of charge at least half-hour before departure. Finally, the positive effect of the algorithm on all items is demonstrated.

Technical and economical evaluation of reactive power service from aggregated EVs

In this paper we present a novel framework to evaluate the capability and cost of providing reactive power service by electric vehicles (EVs). EVs are becoming a promising reactive power service provider because of their short response time and little or no battery degradation during this service. The presented framework is flexible enough to be easily integrated in different market structures proposed in the literature. A linear program is developed to minimize the operating cost of an EV under a range of practical constraints. These constraints include owner's desired state of charge, battery degradation, and limitation in the current ripple from the DC-link capacitor of the EV charger. The developed framework yields optimal charging/discharging schedules for EVs. We also present an algorithm to calculate the reactive power supply function (RPSF) of the EV, as a step-wise ascending function of reactive power, in real-time. The RPSF is calculated based on the price of electricity provided by the grid operator, scheduled charging/discharging activities, and constraints on the EV charger. Simulation results clearly show an opportunity to provide reactive power service by EVs with low marginal cost, especially during on-peak periods when the need of reactive power is high.

Scalable Real-Time Electric Vehicles Charging With Discrete Charging Rates

Large penetration of electric vehicles (EVs) can have a negative impact on the power grid, e.g., increased peak load and losses, that can be largely mitigated using coordinated charging strategies. In addition to shifting the charging process to the night valley when the electricity price is lower, this paper explicitly considers the EV owner convenience that can be mainly characterized by a desired state of charge at the departure time. To this end, the EV charging procedure is defined as an uninterruptible process that happens at a given discrete charging rate and the coordinated charging is formulated as a scheduling problem. The scalable real-time greedy (S-RTG) algorithm is proposed to schedule a large population of EVs in a decentralized fashion, explicitly considering the EV owner criteria. Unlike the majority of existing approaches, the S-RTG algorithm does not rely on iterative procedures and does not require heavy computations, broadcast messages, or extensive bi-directional communications. Instead, the proposed algorithm schedules one EV at a time with simple computations, only once (i.e., at the time the EV connects to the grid), and only requires low-speed communication capability making it suitable for real-time implementation. Numerical simulations with significant EVs penetration and comparative analysis with scheduling policies demonstrate the effectiveness of the proposed algorithm.

Design, Operation, Control, and Economics of a Photovoltaic/Fuel Cell/Battery Hybrid Renewable Energy System for Automotive Applications

Meeting rapidly growing global energy demand—without producing greenhouse gases or further diminishing the availability of non-renewable resources—requires the development of affordable low-emission renewable energy systems. Here, we develop a hybrid renewable energy system (HRES) for automotive applications—specifically, a roof-installed photovoltaic (PV) array combined with a PEM fuel cell/NiCd battery bus currently operating shuttle routes on the University of Delaware campus. The system’s overall operating objectives—meeting the total power demand of the bus and maintaining the desired state of charge (SOC) of the NiCd battery—are achieved with appropriately designed controllers: a logic-based “algebraic controller” and a standard PI controller. The design, implementation, and performance of the hybrid system are demonstrated via simulation of real shuttle runs under various operating conditions. The results show that both control strategies perform equally well in enabling the HRES to meet its objectives under typical operating conditions, and under sudden cloud cover conditions; however, at consistently high bus speeds, battery SOC maintenance is better, and the system consumes less hydrogen, with PI control. An economic analysis of the PV investment necessary to realize the HRES design objectives indicates a return on investment of approximately 30% (a slight, but nonetheless positive, ~$550 profit over the bus lifetime) in Newark, DE, establishing the economic viability of the proposed addition of a PV array to the existing University of Delaware fuel cell/battery bus.

An Aggregate Model of Plug-In Electric Vehicles for Primary Frequency Control

The penetration level of plug-in electric vehicles (PEVs) has the potential to be notably increased in the near future, and as a consequence, power systems face new challenges and opportunities. In particular, PEVs are able to provide different types of power system ancillary services. The capability of storing energy and the instantaneous active power control of the fast-switching converters of PEVs are two attractive features that enable PEVs to provide various ancillary services, e.g., primary frequency control (PFC). However, concurrently, PEVs are obliged to be operated and controlled within limits, which curbs the grid support from PEVs. This paper proposes a new model for PEV using a participation factor, which facilitates the incorporation of several PEV fleets characteristics such as minimum desired state of charge (SOC) of the PEV owners, drive train power limitations, constant current and constant voltage charging modes of PEVs. In order to reduce computational complexity, an aggregate model of PEVs is provided using statistical data. In the end, the performance of PEVs for the provision of PFC is evaluated in a power system. Results show that PEV fleets can successfully improve frequency response, once all the operating constraints are respected.

Fuzzy Logic Based Battery Power Management for PV and Wind Hybrid Power System

This paper proposes an approach for the hybrid solar photovoltaic and wind power system in Battery management for stand-alone applications. Battery charging process is nonlinear, time-varying with a considerable time delay so it is difficult to achieve the best energy management performance by using traditional control approaches. A fuzzy control strategy for battery charging or discharging used in a renewable power generation system is analyzed in the paper. To improve the life cycle of the battery, fuzzy control manages the desired state of charge (SOC). A fuzzy logic-based controller to be used for the Battery SOC control of the designed hybrid system is proposed and compared with a classical PI controller for the performance validation. The entire designed system is modelled and simulated using MATLAB/Simulink Environment.

Self-scheduling of electric vehicles in an intelligent parking lot using stochastic optimization

Electric vehicles charging and discharging management as well as large scale intermittent renewable power generation management are known as the two most important challenges in the future distribution system operation and control. Proper integration of these energy sources may introduce a solution for overcoming to challenges. In this paper, a stochastic charging and discharging scheduling method is proposed for large number of electric vehicles parked in an intelligent parking lot where intelligent parking lots are potentially introduced as aggregators allowing electric vehicles interact with the utilities. A self-scheduling model for an intelligent parking lot equipped with photovoltaic system and distributed generators is presented in this paper in which practical constraints, solar radiation uncertainty, spinning reserve requirements and electric vehicles owner satisfaction are considered. The results show that the proposed parking lot energy management system satisfies both financial and technical goals. Moreover, electric vehicle owners could earn profit by discharging their vehicles as well as having desired state of charge in the departure time.

Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition

The anticipation of a large penetration of EVs (electric vehicles) into the market brings up many technical issues. The power system may put at risk the security and reliability of operation due to uncontrolled EV charging and discharging. It is necessary to carry out intelligent scheduling for charging and discharging of EVs. In this paper, a smart management and scheduling model is proposed for large number of EVs parked in an urban parking lot. The proposed model considered practical constraints such as desired charging electricity price, remaining battery capacity, remaining charging time and age of the battery. The results show that the proposed parking lot energy management system satisfies both financial and technical goals. Moreover, EV owners could earn profit from discharging their vehicles as well as having desired SOC (state of charge) in the departure time.

Design and Implementation of Energy Management System With Fuzzy Control for DC Microgrid Systems

This paper presents the design and implementation of an energy management system (EMS) with fuzzy control for a dc microgrid system. Modeling, analysis, and control of distributed power sources and energy storage devices with MATLAB/Simulink are proposed, and the integrated monitoring EMS is implemented with LabVIEW. To improve the life cycle of the battery, fuzzy control manages the desired state of charge. The RS-485/ZigBee network has been designed to control the operating mode and to monitor the values of all subsystems in the dc microgrid system.