Optimal Charging Strategy Research Articles

An Optimal Domestic Electric Vehicle Charging Strategy for Reducing Network Transmission Loss While Taking Seasonal Factors into Consideration

With the rapid growth of domestic electric vehicle charging loads, the peak-valley gap and power fluctuation rate of power systems increase sharply, which can lead to the increase of network losses and energy efficiency reduction. This paper tries to regulate network loads and reduce power system transmission loss by optimizing domestic electric vehicle charging loads. In this paper, a domestic electric vehicle charging loads model is first developed by analyzing the key factors that can affect users’ charging behavior. Subsequently, the Monte Carlo method is proposed to simulate the power consumption of a cluster of domestic electric vehicles. After that, an optimal electric vehicle charging strategy based on the 0-1 integer programming is presented to regulate network daily loads. Finally, by taking the IEEE33 distributed power system as an example, this paper tries to verify the efficacy of the proposed optimal charging strategy and the necessity for considering seasonal factors when scheduling electric vehicle charging loads. Simulation results show that the proposed 0-1 integer programming method does have good performance in reducing the network peak-valley gap, voltage fluctuation rate, and transmission loss. Moreover, it has some potential to further reduce power system transmission loss when seasonal factors are considered.

Optimal Borehole Energy Storage Charging Strategy in a Low Carbon Space Heat System

Domestic heating is the major demand of energy systems, which can bring significant uncertainties to system operation and shrink the security margin. From this aspect, the borehole system, as an interseasonal heating storage, can effectively utilize renewable energy to provide heating to ease the adverse impact from domestic heating. This paper proposes an optimal charging strategy for borehole thermal storage by harvesting energy from photovoltaic (PV) generation in a low-carbon space heating system. The system optimizes the heat injection generated by air source heat pump in the charging seasons to charge the borehole, which provides high inlet temperature for ground source heat pump to meet space heating demand in discharging seasons. The borehole is modeled by partial differential equations, solved by the finite-element method at both 2D and 3D for volume simulation. The pattern search optimization is used to resolve the model. The case study illustrates that with the optimal charging strategies, less heat flux injection can help the borehole to reach a higher temperature so that the heating system is more efficient compared with boilers. This paper can benefit communities with seasonable borehole storage to provide clean but low-cost heating and also maximize PV penetration.

Heuristic Versus Optimal Charging of Supercapacitors, Lithium-Ion, and Lead-Acid Batteries: An Efficiency Point of View

Electrical energy storage systems are extensively utilized in applications, including electrified vehicles, renewable power generation, and electronic devices. While discharging events are a function of the power demand, the charging procedure is often controllable. This paper evaluates different charging strategies for stand-alone supercapacitors (SCs), lithium-ion (Li-ion), and lead-acid batteries. Constant power and optimal charging strategies are formulated and the corresponding charging currents are obtained. Efficiency analysis for different charging strategies and charging times (slow and fast) is performed. The identical objective function for all modules is to minimize the resistive losses during a given charging time by utilizing Pontryagin’s minimum principle. An analytical solution exists for the SC case, which is constant current charging. The variation of the total internal resistance with state of charge in lead-acid chemistry is considerable, and the optimal charging problem results in a two-point boundary value problem. In case of the Li-ion battery, the model includes the electronic as well as polarization resistance. Furthermore, in order to investigate the influence of temperature on the optimal charging of the Li-ion battery, a constrained optimal control problem for a three state electrothermal model is formulated and solved using dynamic programming.

Design of a Model-based Fractional-Order Controller for Optimal Charging of Batteries

this paper proposes a robust closed-loop control strategy for optimal charging of batteries. Model-based design of the charging controller is performed based on a coupled electro-thermal aging battery model. For developing an optimal charging strategy, besides minimization of charging time, prolonging the lifetime of the battery is of great importance. As the first step towards implementing a health-conscious charging controller, trajectory planning is carried out by solving a nonlinear optimization problem. The aim is to obtain optimal charging profiles while minimizing the aging. By linearization of the nonlinear battery model, a large uncertainty is observed in the behavior of the linear battery model under different operating conditions. In order to address this problem, CRONE control methodology is used. CRONE control scheme is a robust non-conservative control methodology that shows to be a good candidate for achieving the control targets for our case of study. Subsequently, the design and parametrization of the controller is explained. The simulation results are provided to prove the effectiveness of the CRONE controller. Simulation tests show that the developed controller meets the design requirements while tracking the reference signals in presence of model uncertainties.

System theoretic analysis of battery charging optimization

Innovative optimal charging sequences are sought to minimize battery degradation and reduce charging loses. One charging sequence under investigation incorporates high frequency pulses or sinusoidal perturbations. However, there is disagreement in the literature on the benefits/disadvantages of pulsed charging. This work analytically determines optimal battery charging sequences for two cases: minimizing energy losses and maximizing charge supplied to the battery while respecting lithium plating constraints. Assuming relevant battery physics have linear, time-invariant dynamic behavior, optimal charging strategies are derived analytically. The analysis exposes specific features of the battery dynamic response (frequency response and impulse response) that must be present for optimal charging sequences to have periodic components. In the case of energy minimization, the battery's electrochemical impedance must approach the imaginary axis. In the case of limiting lithium plating, a criterion is developed involving impulse responses of relevant battery variables.

Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario

This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.

LiFePO4 battery charging strategy design considering temperature rise minimization

The temperature rise of lithium-ion batteries during the charging process is a significant factor that can influence battery capacity degradation and produce potential safety hazards. In this paper, an optimal charging strategy for LiFePO4 batteries is proposed to minimize the charging temperature rise. First, a battery charging temperature rise model is employed to simulate the temperature variation, and a first-order equivalent circuit model is established to identify the model parameters. Then, the genetic algorithm is applied to search the optimal charging current profiles considering different initial state of charge values and different control intervals. Experimental results prove that the proposed charging strategy not only features a lower temperature rise but also shows less charging time compared to the constant current charging method.

Real-Time Differential Pricing Scheme for Active Consumers with Electric Vehicles

Electric vehicles (EVs) are becoming increasingly popular because of their low operating costs. However, the anticipated increase in EV usage presents opportunities as well as challenges for utilities. This paper models EV charging behavior of active consumers who strategize their EV charging schedule to minimize the charging cost. The electric utility employs a temporal and spatially varying real-time pricing scheme that minimizes peak-to-average load ratio by influencing charging behavior of multiple active consumers. These active consumers decide their optimal charging strategy based on real-time electricity prices in order to minimize their EV charging cost. A model predictive control (MPC)-based optimal pricing scheme for electric utility and optimal charging strategy for active consumers is proposed. Effects of the proposed approach on load profile is verified using simulations.

A cooperative game theoretic analysis of electric vehicles parking lot in smart grid

Plug-in Hybrid Electric Vehicles (PHEVs) play a major role in decreasing amount of fossil fuels led by transportation system. PHEVs in both Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes can effect on the power market. In order to diminish potential challenges related to these effects, various methods like developing optimal charging strategies for the connected PHEVs and managing energy exchange between the PHEVs’ parking lots can be taken into consideration. In this paper, a cooperative game model has been proposed in order to determine charging/discharging price adaptively. Simulation results show how this model leads to the maximization of utilities’ profit and minimization of the parking lots’ cost. Furthermore, a stochastic analysis has been done over the proposed model in order to well understand how much the deviation of profit and expected value of profit are in different levels of uncertainty. The numerical results prove that higher deviation over spot market price leads to both higher mean and deviation over profit for utilities, and the owner of utilities should consider the effect of price’s uncertainty whenever it is considerable.

Charging cost optimization for EV buses using neural network based energy predictor

For conventional buses, based on the decades of their operational knowledge, public transport companies are able to optimize their cost of operation. However, with recent trend in the usage of electric buses, cost optimal operation can become challenging. In this paper an offline optimal charging strategy is developed to minimize the energy cost. This is done by exploiting the periodicity and predictable operation of the city buses. For effective usage of the developed offline strategy, the actual energy demand of the electric bus must be known a-priori, which can be demanding. In order to address this issue, a predictor is designed. The neural network based predictor is able to estimate the energy demand for the next day. Using this, three different optimal charging strategies are implemented. Initially, only the operational constraints are considered to ensure the completion of a trip, later, a more involved problems consisting of battery state of charge (SoC) constraints and temperature constraints are included for the second and third optimization problems, respectively. All the three approaches result in significant energy cost minimization when compared to the non-optimal strategy of charging the electric bus to the full capacity at every available opportunity. Additionally, for the second and third formulations, namely, SoC and temperature constraints, by using a qualitative aging approach, some enhancements in the battery health is observed when compared to the non-optimal charging strategy.

Optimal Management for Parking-Lot Electric Vehicle Charging by Two-Stage Approximate Dynamic Programming

This paper targets the day-time charging scenario for plug-in electric vehicles at parking-lots near commercial places, where most vehicles have extended parking time. Compared with night-time charge scenarios for residential buildings, commercial building parking-lot charging during day-time feature significant stochastic vehicle arrival and departure, as well as highly dynamic electricity price. A two-stage approximate dynamic programming framework is proposed to determine the optimal charging strategy, utilizing the predicted short-term future information and long-term estimation from historical data. All the vehicles are desired to be charged to full prior to the departure time specified under constrained total charging capacity. The uncharged amount is subject to a significant penalty cost. Simulation scenarios are created by modeling the vehicle arrival behavior as Poisson process, including arrival time, departure time, and arrival state of charge. The simulation results show that the proposed method can significantly decrease the energy cost.

Research on the Optimal Charging Strategy for Li-Ion Batteries Based on Multi-Objective Optimization

Charging performance affects the commercial application of electric vehicles (EVs) significantly. This paper presents an optimal charging strategy for Li-ion batteries based on the voltage-based multistage constant current (VMCC) charging strategy. In order to satisfy the different charging demands of the EV users for charging time, charged capacity and energy loss, the multi-objective particle swarm optimization (MOPSO) algorithm is employed and the influences of charging stage number, charging cut-off voltage and weight factors of different charging goals are analyzed. Comparison experiments of the proposed charging strategy and the traditional normal and fast charging strategies are carried out. The experimental results demonstrate that the traditional normal and fast charging strategies can only satisfy a small range of EV users’ charging demand well while the proposed charging strategy can satisfy the whole range of the charging demand well. The relative increase in charging performance of the proposed charging strategy can reach more than 80% when compared to the normal and fast charging dependently.

A Reduced Wide-temperature-range Electro-thermal Model and Thermal Parameters Determination for Lithium-ion Batteries

Abstract A reduced wide-temperature-range electro-thermal model, which is based on the frequency-dependent equation, is presented and validated under different frequencies and temperatures. The temperature dependence of resistances and capacitances in a wide temperature range is proposed. In contrast to state-of-the-art electro-thermal models, the presented identification for thermal parameters, which are validated with a very small error, does not require expensive thermal equipment, like calorimeter and is free from the challenges for their installations. Simulation results exhibit good agreement with experiments, where the maximum relative voltage error and temperature error are less than 2.31% and 0.89°C, respectively. The reduced model, thanks to its reliability and simplified characteristic, provides a promising candidate for development of optimal charging strategies in a wide temperature range, for prediction of the electrical and thermal performance of batteries in on-board battery management system.

(Invited) Analyzing and Minimizing Capacity Fade through Optimal Model-based Control - Theory and Experimental Validation

In order to significantly expand the BEV market, and to increase the use of lithium-ion batteries in electric grids, there is a need to develop optimal charging strategies to utilize the batteries more efficiently and enable longer life. Advanced battery management systems that can calculate and implement such strategies in real time are expected to play a critical role for this purpose. This article investigates different approaches for determining model-based optimal charging profiles for batteries, and experimentally validates the gain obtained using such profiles. Optimal profiles that maximize the cycle life of the cells are implemented on 16 Ah NMC cells for 30 minutes of charge followed by 5C discharge, and the cycle life is compared to a standard 2C CC-CV charge and 5C discharge. An improvement of more than 100% in cycle life is observed experimentally, for our test conditions on this cell design. This study is the first to experimentally demonstrate that the improved extra knowledge obtained by sophisticated physics-based models results in significant improvements in battery performance when employed in a real time control algorithm.

An Optimal Charging Strategy for PV-Based Battery Swapping Stations in a DC Distribution System

Photovoltaic- (PV-) based battery swapping stations (BSSs) utilize a typical integration of consumable renewable resources to supply power for electric vehicles (EVs). The charging strategy of PV-based BSSs directly influences the availability, cost, and carbon emissions of the swapping service. This paper proposes an optimal charging strategy to improve the self-consumption of PV-generated power and service availability while considering forecast errors. First, we introduce the typical structure and operation model of PV-based BSSs. Second, three indexes are presented to evaluate operational performance. Then, a particle swarm optimization (PSO) algorithm is developed to calculate the optimal charging power and to minimize the charging cost for each time slot. The proposed charging strategy helps decrease the impact of forecast uncertainties on the availability of the battery swapping service. Finally, a day-ahead operation schedule, a real-time decision-making strategy, and the proposed PSO charging strategy for PV-based BSSs are simulated in a case study. The simulation results show that the proposed strategy can effectively improve the self-consumption of PV-generated power and reduce charging cost.

Optimal Charging Strategy of Electric Vehicles in Unbalanced Three-Phase Distribution Network

Background: The increasing penetration of Electric Vehicles (EV) could lead to significant impact on the power systems, particularly for existing distribution networks. Methods: These impacts contain phase unbalance, excessive voltage deviations and overloading of equipment, which arise when a large numbers of Electric Vehicles are simultaneously charged. Results: In the current research, an optimal charging strategy is designed to control charging rates of Electric Vehicles in unbalanced three-phase distribution networks. A technique according to the Particle Swarm Optimization (PSO) is designed in order to reduce the charging cost of vehicles, with considering certain constraints. The constraint set involves transformer and line restrictions, unbalance of phase and voltage range. Conclusion: The proposed charging strategy is investigated on a real distribution network. Findings shows that high penetration of Electric Vehicles can be sustained in the existing distribution networks, demonstrating the effectiveness of the proposed method.

An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model

Lithium-ion batteries are widely adopted as the power supplies for electric vehicles. A key but challenging issue is to achieve optimal battery charging, while taking into account of various constraints for safe, efficient and reliable operation. In this paper, a triple-objective function is first formulated for battery charging based on a coupled thermoelectric model. An advanced optimal charging strategy is then proposed to develop the optimal constant-current-constant-voltage (CCCV) charge current profile, which gives the best trade-off among three conflicting but important objectives for battery management. To be specific, a coupled thermoelectric battery model is first presented. Then, a specific triple-objective function consisting of three objectives, namely charging time, energy loss, and temperature rise (both the interior and surface), is proposed. Heuristic methods such as Teaching-learning-based-optimization (TLBO) and particle swarm optimization (PSO) are applied to optimize the triple-objective function, and their optimization performances are compared. The impacts of the weights for different terms in the objective function are then assessed. Experimental results show that the proposed optimal charging strategy is capable of offering desirable effective optimal charging current profiles and a proper trade-off among the conflicting objectives. Further, the proposed optimal charging strategy can be easily extended to other battery types.

Numerical analysis of electric bus fast charging strategies for demand charge reduction

Electric transit buses have been recognized as an important alternative to diesel buses with many environmental benefits. Electric buses employing lithium titanate batteries can provide uninterrupted transit service thanks to their ability of fast charging. However, fast charging may result in high demand charges which will increase the fuel costs thereby limiting the electric bus market penetration. In this paper, we simulated daily charging patterns and demand charges of a fleet of electric buses in Tallahassee, Florida and identified an optimal charging strategy to minimize demand charges. It was found that by using a charging threshold of 60–64%, a $160,848 total saving in electricity cost can be achieved for a five electric bus fleet, comparing to a charging threshold of 0–28%. In addition, the impact of fleet sizes on the fuel cost was investigated. Fleets of 4 and 12 buses will achieve the lowest cost per mile driven when one fast charger is installed.

A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

With the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price.

A reduced low-temperature electro-thermal coupled model for lithium-ion batteries

A low-temperature electro-thermal coupled model, which is based on the electrochemical mechanism, is developed to accurately capture both electrical and thermal behaviors of batteries. Activation energies reveal that temperature dependence of resistances is greater than that of capacitances. The influence of frequency on polarization voltage and irreversible heat is discussed, and frequency dependence of polarization resistance and capacitance is obtained. Based on the frequency-dependent equation, a reduced low-temperature electro-thermal coupled model is proposed and experimentally validated under different temperature, frequency and amplitude conditions. Simulation results exhibit good agreement with experimental data, where the maximum relative voltage error and temperature error are below 2.65% and 1.79°C, respectively. The reduced model is demonstrated to have almost the same accuracy as the original model and require a lower computational effort. The effectiveness and adaptability of the proposed methodology for model reduction is verified using batteries with three different cathode materials from different manufacturers. The reduced model, thanks to its high accuracy and simplicity, provides a promising candidate for development of rapid internal heating and optimal charging strategies at low temperature, and for evaluation of the state of battery health in on-board battery management system.