Optimal Charging Strategy Research Articles

Aging-Aware optimal charging strategy for lithium-ion batteries: Considering aging status and electro-thermal-aging dynamics

High quality charging strategy, which can minimize the impact of inevitable aging/degradation mechanisms and avoid unfavorable side reactions, is critical for battery management systems to extend the battery life, maximize charging capability, ensure safety, and improve the overall system reliability. An aging-aware optimal charging strategy is proposed in this effort to avoid the formation of lithium plating and minimize the charging time and/or capacity loss due to solid electrolyte interface (SEI) layer growth and active material loss. A physics-based reduced-order lithium plating model is developed based on the solid and electrolyte phase charge conservation equations, and used in the optimal charging strategy to prevent the occurrence of lithium deposition. The interaction between SEI layer growth, reduction of anode porosity, and lithium plating is also investigated. The proposed charging strategy provides the flexibility to be further calibrated into three strategies: aging-aware minimum-time strategy, aging-aware minimum-capacity-loss strategy, and minimum time and capacity loss balanced strategy. They are compared against each other and evaluated with a fresh battery, a moderately aged battery, and a fully aged battery, to demonstrate how the aging status of the battery impacts the charging strategies.

Charging Strategy Optimization at Low Temperatures for Li-Ion Batteries Based on Multi-Factor Coupling Aging Model

Lithium-ion batteries (LIBs) charging at low temperatures will easily accelerate the aging of LIBs and reduce the useful life. This paper applies advanced multi-factors coupling aging model and bi-objective particle swarm optimization (PSO) algorithm to derive suitable charging patterns for LIBs at low temperatures. Based on the results of orthogonal experiments, which consider the effects of charging rate, charging temperature and charging cut-off voltage on battery aging, the low-temperature capacity degradation model was established. Furthermore, two important yet competing charging objectives, including battery health and charging time, are taken into account simultaneously. These two conflicting charging objectives are traded off by solving a bi-objective optimization problem based on battery electrothermal and aging behavior. Combined with PSO algorithm, the optimal low-temperature charging strategy is obtained. As a result, the three-stage constant current and constant voltage (CC-CV) charging strategy is optimized to balance various combinations of charging objectives. Different tradeoffs are compared and analyzed based on the Pareto frontiers. The verification results show that the optimized three-stage CC-CV charging strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and battery health under low-temperature charging conditions.

Design and validation of a battery management system for solar-assisted electric vehicles

Expanding the travel mileage of power batteries is of great significance for electric vehicles (EVs). The solar battery pack is considered as a promising supplement to the battery management system (BMS) of EVs but integrating solar power into EVs remains a challenge. This paper proposes a BMS that coordinates the solar panels and the lithium battery system. The proposed BMS mainly involves three aspects. Firstly, an equivalent second-order resistance-capacitance model is established and afterwards is identified by using an improved recursive least squares algorithm. Then, the maximum power prediction strategy is developed based on the advanced state of charge (SOC) algorithm and the available solar energy estimation algorithm. Thirdly, a multi-stage constant current charging strategy based on the adaptive genetic algorithm is designed to optimize the battery temperature rise and charging time simultaneously. The proposed BMS is validated by the experiment on a real-world solar-assisted EV. The results indicate that the proposed power prediction strategy can accurately estimate the available power for EVs. Compared with the widely-used charging method, the developed optimal charging strategy reduces the charging time and temperature rise by 7%–11% and 36%–45%, respectively.

An iterative algorithm for regret minimization in flexible demand scheduling problems

AbstractA major challenge to develop optimal strategies for allocation of flexible demand toward the smart grid paradigm is the uncertainty associated with the real‐time price and electricity demand. This article presents a regret‐based model and a novel iterative algorithm which solves the minimax regret optimization problem. This algorithms exhibits low computational burden compared with traditional linear programming methods and affords iterative convergence through updates of feasible power schedules, thus enabling a scalable parallel implementation for large device populations. Specifically, our approach seeks to minimize the induced worst‐case regret over all price scenarios and solves the optimal charging strategy for the electrical devices. The convergence of the method and optimality of the computed solution is justified and some numerical simulations are discussed for the case of a single device operating under different types of price realizations and uncertainty bounds.

Optimal charging strategy for large-scale electric buses considering resource constraints

• Limited charging resources and time-dependent electricity prices are considered. • A column-generation-based model is proposed for electric bus fleet charging problem. • Charging continuity is emphasized for battery's state of health in the subproblems. • The utilization rate of nighttime charging resources can be increased to 97.96%. • Large scale real-world cases can be solved within a short computation time. The large-scale application of electric buses highlights a series of practical problems, such as high charging costs, unreasonable utilization of charging resources, and chaotic charging schedules. In this study, a mixed integer programming model was first proposed to reduce the total charging cost of electric bus fleets by optimizing the charging power and charging time. Meanwhile, to improve the efficiency of the model under large-scale charging scenarios, a column-generation-based algorithm is developed to decompose the original model into a master problem and several subproblems, where each electric bus’s charging strategy is solved in an independent subproblem to guarantee a stable operational bus fleet schedule and to avoid intermittent charging. An experimental analysis is carried out. The results show that the optimal charging strategy can reduce the charging cost by approximately 36.1% compared with the uncontrolled charging strategy, which carries the potential to be applied in large-scale bus fleet operation.

Contract and Lyapunov Optimization-Based Load Scheduling and Energy Management for UAV Charging Stations

Nowadays, a large number of civilian unmanned aerial vehicles (UAVs) are increasingly being used in many of our daily applications. However, there are many kinds of UAVs that need to enhance their endurance due to their limited resources. In order to make the UAVs operate efficiently, it is necessary to schedule UAVs with charging requirements. In this paper, renewable energy production and storage equipment on the basis of traditional charging stations is adopted to reduce the power purchase from the distribution network as much as possible. An online algorithm based on Lyapunov optimization is proposed to schedule the charging of UAVs and the energy management of the charging station. Meanwhile, contract theory is used to design the optimal charging strategy in the case of information asymmetry. Hence, local renewable energy can be utilized to the greatest extent, and electricity purchase costs can be reduced. Through the incentive system, users can spontaneously charge at low peak times and avoid the risk of grid overload and high energy cost. The simulation results show that the algorithm can improve the efficiency of charging station operators, allowing users to avoid charging at peak times, and only use real-time information to schedule UAVs. Compared to other algorithms, the proposed scheme can bring good revenue to operators while ensuring long-lasting operations of charging stations.

Optimal charging strategy for intercity travels of battery electric vehicles

Limited driving range, long charging time and high charging costs affect the use of battery electric vehicles (BEVs) for intercity travels and often compel drivers to charge their vehicles more than once. This study proposes a multistage optimisation model to provide BEV drivers with a charging strategy for intercity travel. This model aims to jointly minimise travel time and charging cost and to determine the optimal amount of charged energy in each charging station located along the available routes. A dynamic programming method for solving this model is also designed. The feasibility and effectiveness of the proposed model and solution method are verified through the numerical example and simulations. The results indicate that the trade-offs between travel time and charging cost significantly influence the proposed solution, and the residual energy at the destination affects the availability of routes. The policy implications for the BEV-based intercity travels are discussed.

Two‐layer optimal charging strategy for electric vehicles in old residential areas

Two‐layer optimal charging strategy for electric vehicles in old residential areas

Optimization of charging strategy for lithium-ion battery packs based on complete battery pack model

This study focuses on a charging strategy for battery packs, as battery pack charge control is crucial for battery management system. First, a single-battery model based on electrothermal aging coupling is proposed; subsequently, a battery pack cooling model and battery pack equilibrium management model are combined to form a complete battery pack model that describes the state parameters of the battery pack and the interaction between the individual batteries in the battery pack. Then, a multi-objective optimal charging strategy considering charging time, aging, and energy loss is proposed, and the equilibrium management, temperature, and battery parameters are restricted. A genetic algorithm method was used to optimize the adaptive multi-phase constant-current constant-voltage charging strategy. A fast charging strategy based on the shortest charging time is proposed. The results show that the fast charging strategy can significantly reduce charging time but leads to aging and increases energy loss. The contradictory relation between charging time and aging and the interaction between charging time and energy loss are revealed. The balance optimization charging strategy appears on the left side of the Pareto curve. Finally, a balanced charging strategy considering charging time, aging, and energy loss is obtained. In comparison with single batteries with the same average initial current charging, the constant current phase order decreases because of battery pack state parameters and equilibrium management, resulting in different charging strategies.

Charging strategy through analysis of charging influence factors of ultra-light hydrogen storage with polyethylene terephthalate liner

A lightweight type 4 vessel with a polyethylene terephthalate (PET) liner is analyzed. The derived heat transfer coefficients between the gas and wall are applied, and a parametric study is performed. An optimized charging strategy is also developed. Firstly, when the injected hydrogen temperature decreases, the charging time increases, and the charged gas temperature decreases. Secondly, the higher the ambient temperature, the shorter the charging time, and the higher the charged gas temperature. Thirdly, the larger the mass flow rate, the shorter the charging time, and the higher charged gas temperature. Fourthly, as the initial pressure inside the vessel increases, the charging time shortens, and the charged gas temperature decreases. Fifthly, using the formulated charging strategy, during summer, the charged gas temperature decreases by approximately 9 °C. In winter, the charging time is reduced by approximately 58 s. The results provide important information of temperature control for ensuring vessel safety.

Selecting the Optimal Charging Strategy of Electric Vehicles Using Simulation Based on Users’ Behavior Pattern Data

Low-carbon-emitting vehicles, such as plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs), are becoming increasingly common as an emerging solution to environmental problems. As the number of EVs increases in a market, EV charging requires significantly more electricity use and charging strategies could have a significant impact on the peak load and electricity demand in the power system, as well as the electricity bill for each household. The purpose of this paper is to develop a model to find a user-specific optimal EV charging strategy using actual data of users' behavior patterns in daily life. Toward this end, we deduce the time when each user utilizes electrical appliances and drives an EV based on actual user behavior data. The daily electricity consumption of a household by time is then calculated according to three different charging strategies. Comparing these results, we can obtain the optimal charging strategy for users to minimize their electricity bills. As a result of a simulation, it is found that, when each user follows her optimal charging strategy, the total electricity bill can be decreased by 35% and the peak-to-average ratio by 12% compared to when every user's charging is uncontrolled. The results show that the charging EVs according to the optimal strategy obtained by our model would significantly affect not only the user's electricity bill but also the whole power grid system.

An Electro-Thermal Model based fast optimal charging strategy for Li-ion batteries

<abstract> <p>This paper utilizes an integrated electro-thermal model of a lithium-ion battery to search for an optimal multistage constant current charge pattern that will minimize the total charging time of the battery, while restricting its temperature rise in each stage within safe limits. The model consists of two interlinked components, an electrical equivalent circuit model to continuously predict the battery's terminal voltage and a thermal model to continuously predict its temperature rise as charging progresses. The proposed optimization algorithm is based on a novel stepwise single-variable search technique that is very easy to implement and converges quickly. The results of our extensive simulation studies clearly indicate that the proposed charging strategy offers a fast, safe and easy-to-implement alternative to many of the existing computationally intensive optimal charging strategies.</p> </abstract>

Optimal Charging Strategy of Electric Vehicles with Consideration of Battery Storage

The high penetration of electric vehicles (EVs) will increase burden of a power grid. However, the expansion of capacity of distribution facilities is not always possible, especially in some old residential community. This paper proposes to use an optimal charging strategy of EVs with additional battery energy storage (BES) to improve the charging capabilities in a residential community. By modeling the EV charging behavior, the required charging capacity is evaluated using Monte Carlo method and the BES size is determined as the difference between the required capacity and the distribution capacity. An optimal charging strategy is then proposed to reduce the charging cost and ensure the safe running of distribution network.

Study on Battery Charging Strategy of Electric Vehicles Considering Battery Capacity

This paper proposes an improved fast charging strategy for electric vehicles (EVs) by considering available battery capacity. According to previous research and battery experiment reports, the energy capacity of batteries is not fixed, and it can decrease temporarily depending on the magnitude of charging or discharging power. This paper addresses the decreased capacity in proportion to the magnitude of the charging power which leads to a reduction in the driving range. For effective and practical use for EV users, the change in battery capacity is expressed through an equation, and a new state of charged indicator is proposed. To improve the conventional charging method that uniformly supplies power regardless of the battery capacity, this paper proposes an optimal charging strategy injecting constant power. The proposed charging strategy provides an optimal charging power reference to minimize costs considering charged energy, charging time, and usable energy loss based on billing system of EV charging. To verify the effectiveness of the proposed charging method, the optimal charging power reference for each battery is calculated based on capacity and characteristics, and the total cost is compared. The results show that the performance of the proposed charging strategy is effective in minimizing both the reduced battery capacity and economic burden on EV users.

Toward safe and rapid battery charging: Design optimal fast charging strategies thorough a physics‐based model considering lithium plating

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Optimal charging strategy for electric vehicles in residential charging station under dynamic spike pricing policy

Nowadays, much attention has been drawn to environmental protection. Traveling by electric vehicles (EVs) instead of conventional fuel cars is strongly supported by national governments for the sustainable development of urban transportation. However, the increasing EV charging load in residential areas have brought a heavy burden to the distribution transformer. Therefore, coordinated charging of EVs in the residential charging station (RCS) is essential to relieve the power supply pressure. In this paper, an optimal charging strategy based on dynamic spike pricing (DSP) policy is proposed to reduce the charging cost of EVs and ensure the normal operation of the distribution transformer. First of all, the load model of EVs on four kinds of typical days is established with consideration of the seasonal and holiday characteristics of EV charging demands. Then, a new DSP policy based on Time-of-Use (TOU) mechanism is designed with an additional spike time period and a spike price to transfer peak loads in rush hours. To protect EV users from financial losses and prevent transformer overload, an optimal charging model is formulated to minimize the charging cost of EVs with considering the power margin of the distribution transformer. Ultimately, the genetic algorithm (GA) is used to solve the model. The simulation results show that the optimal charging strategy proposed in this paper is effective in peak shaving and reducing charging cost.

Game Theoretic-Based Distributed Charging Strategy for PEVs in a Smart Charging Station

This article investigates the charging problem of plug-in electric vehicles (PEVs) in a smart charging station (SCS) under a new interaction mechanism that allows the interactions among PEVs. The target is to coordinate the charging strategies of all PEVs such that the energy cost of SCS is minimized without compromising a set of constraints for PEVs and SCS. To this end, we first construct a non-cooperative game framework, in which each player (i.e., PEV) expects to minimize its cost by choosing the optimal charging strategy over the entire charging horizon. Then, the existence and optimality of Nash equilibrium (NE) for the formulated non-cooperative game is provided. Moreover, to find the unique generalized Nash equilibrium (GNE), we propose a distributed GNE-seeking algorithm based on the Newton fixed-point method. And a fast alternating direction multiplier method (fast-ADMM) framework is applied to determine the best response of PEVs. The convergence of the proposed distributed GNE-seeking algorithm and PEVs' best response are also provided with theoretical analysis. Simulations are presented at last to validate the effectiveness of the proposed algorithm.

Optimal sizing of portable modular batteries for electric vehicles

Electric Vehicles (EV) powered by portable batteries address two important deployment challenges in countries in Europe and Asia, where most people live in high-rise buildings. First is the issue of a lack of power-charging infrastructure, where an American-style charging model that operates in the garage of a detached or attached single-family residence is not practical. Second is the range-anxiety problem, as it is much easier to take portable batteries to nearby public facilities for recharging, such as restaurants and cafes. The modularity of portable batteries further enables better battery-to-vehicle interoperability across different auto makes, brands and manufacturers, thus, faster and easier EV deployment. In this paper, we show that such a portable modular battery development scenario for EVs can be accomplished, within the realm of current technologies, over the next few years. This can be done with the help of thin film photovoltaic cells embedded in automobile windows and a carefully optimized charging strategy. We propose a modeling framework to plot the EV battery technology frontier, where such a scenario is feasible, and derive the optimal sizing of modular batteries.

Voltage regulation in constrained distribution networks by coordinating electric vehicle charging based on hierarchical ADMM

The charging coordination of large-scale electric vehicles (EVs) for avoiding the voltages at some nodes to drop below feasible ranges in distribution networks, which is formulated as a class of optimisation problems with a certain class of coupled constraints, is studied. Then the alternating direction method of multipliers (ADMM) is introduced to obtain the optimal charging strategies. However, due to the non-separable property of the objective function which includes a non-linear term of the total charging power of EV populations, it is difficult to decentralise the update step of the charging strategy in the ADMM method. Consequently, a novel decentralised hierarchical method is proposed by further developing an iterative update of best responses of individual EVs at each iteration step of the proposed ADMM method, such that individual EVs can implement the coordination behaviours locally and simultaneously without sharing any other private information except the charging power. Furthermore, the proposed approach does not require the objective function to be continuously differentiable. The convergence and optimality of the proposed method are verified and some numerical simulations are studied to illustrate the developed results.

Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging

This paper deals with optimal charging versus aging minimization for lithium-ion batteries. The optimal charging strategy proposed involves charging controllers whose design relies on a battery model. The model, especially designed for automotive battery management systems applications, is recalled in this paper. It provides the voltage response of a cell to an input current. It also models side reactions that produce degradation mechanisms and thus decrease battery performance. Side reaction modelling involves taking into account the temperature cell variations, which are thus also modelled. The association of the three above-mentioned sub-models leads to an electro-thermal battery aging model used to design an optimal charging strategy that simultaneously takes into account the minimization of charging time and maximization of battery lifetime. Thus, to achieve a charging controller that manages battery health, an appropriate charging trajectory was computed by solving an optimization problem minimizing aging. Then, a charge control loop was designed. The nonlinear behavior of the battery was taken into account through the linearization of the electro-thermal aging model in different operating conditions. To take into account the resulting linear model family, the CRONE design methodology was used. The principles of this methodology are recapped and the design of the charging control loop is explained. The efficiency of the resulting charge controller is illustrated by several simulations.