Optimal Charging Strategy Research Articles

Resilient Distributed Coordination of Plug-In Electric Vehicles Charging under Cyber-Attack

The coordinated scheduling of plug-in electric vehicle (PEV) charging should be constructed in distributed architecture due to the growing population of PEVs. Since the information and communication technology makes the adversary more permeable, the distributed PEV charging coordination is vulnerable to cyber-attack which may degrade the performance of scheduling and even cause the failure of scheduler task. Considering the tradeoff between system-wide economic efficiency, distribution level limitations and PEV battery degration, this paper investigates the resilient distributed coordination of PEV charging to resist cyber-attack, where the steps of detection, isolation, updating and recovery are designed synthetically. Under the proposed scheduling scheme, the misbehaving PEVs suffering from cyber-attack are gradually marginalized and finally isolated, and the remaining well-behaving PEVs obtain their own optimal charging strategy to minimize the total system cost in distributed architecture. The simulation results verify the effectiveness of theoretical method.

Distributed control of electric vehicle fleets considering grid congestion and battery degradation

Nowadays, developing coordinated optimal charging strategies for large‐scale electric vehicle (EV) fleets is crucial to ensure the reliability and efficiency of power grids. This paper presents a novel fully distributed control strategy for the optimal charging of large‐scale EV fleets aiming at the minimization of the aggregated charging cost and battery degradation, while satisfying the EVs' individual load requirements and the overall grid congestion limits. We formulate the optimization problem as a convex quadratic programming problem where all the EVs' decisions are coupled both via the objective function and some grid resource sharing constraints. Based on the distributed waterfilling approach, the proposed resolution algorithm requires a minimal shared information between EVs that communicate only with their neighbors without relying on a central aggregator, thus guaranteeing the EV users' privacy. The performance of the proposed approach is evaluated through numerical experiments to validate its effectiveness in achieving a global optimum while respecting the grid constraints with a favorable computational efficiency.

Decentralized Hierarchical Planning of PEVs Based on Mean-Field Reverse Stackelberg Game

In the reverse Stackelberg mechanism, by considering a decision function for the leader rather than a decision value in the conventional Stackelberg game, the leader can explore a wider decision space. This flexibility can result in realizing the globally optimal solution of the leader’s objective function, while controlling the reaction function of the followers, simultaneously. We consider an aggregator who purchases energy from the wholesale energy market. The aggregator acts as the leader for a group of plugged in electric vehicles (PEVs) and determines the price of energy versus consumption at each hour a day as its decision function. In the followers level, since the optimal charging strategies of the PEVs are coupled through the electricity price, the PEVs in a group are considered to cooperate in finding their Nash–Pareto-optimal charging strategy, by minimizing a social cost function. For a large number of PEVs, the cooperative cost minimization of PEVs can be modeled as a cooperative mean-field (MF) game. We propose a decentralized MF optimal control algorithm and prove that the algorithm converges to leader–follower MF $\varepsilon _{N}$ -Nash equilibrium point of the game. Furthermore, a decentralized reverse Stackelberg algorithm is implemented to achieve the optimal linear price function of the leader. Simulation results and comparison with benchmark methods are performed to demonstrate the advantages of the proposed method. Note to Practitioners —The effect of a large population of PEVs on the power grid such as overload and voltage drop is inevitable. Motivated by this, there are many research articles which propose different centralized and decentralized charging coordination solutions to address this problem. The core idea in the most of literature is: “How to IMPLEMENT a demand response (DR) program appropriately for charging coordination problem to avoid high peak load?” However, none of them propose a practical algorithm on “How to DESIGN a DR optimally by having limited information from the clients?” We propose a bilevel optimization algorithm to both design a DR program (i.e., price function) and also implement DR program to flatten the demand curve, accordingly. Another advantage of the proposed method is that the information structure of the problem is close to reality. There is no information exchange among the clients and also the utility company does not need to know the private information of the clients. The Utility Company only knows the charging profile of the PEVs in each day and broadcasts the price signal to the clients for the next day. The method is illustrated in an IEEE 5-bus system that supports our claims.

Optimal Electric Vehicle Charging Strategy With Markov Decision Process and Reinforcement Learning Technique

Electric vehicles (EVs) have rapidly developed in recent years and their penetration has also significantly increased, which, however, brings new challenges to power systems. Due to their stochastic behaviors, the improper charging strategies for EVs may violate the voltage security region. To address this problem, an optimal EV charging strategy in a distribution network is proposed to maximize the profit of the distribution system operators while satisfying all the physical constraints. When dealing with the uncertainties from EVs, a Markov decision process model is built to characterize the time series of the uncertainties, and then the deep deterministic policy gradient based reinforcement learning technique is utilized to analyze the impact of uncertainties on the charging strategy. Finally, numerical results verify the effectiveness of the proposed method.

LCC-S Based Discrete Fast Terminal Sliding Mode Controller for Efficient Charging through Wireless Power Transfer

Compared to the plug-in charging system, Wireless power transfer (WPT) is simpler, reliable, and user-friendly. Resonant inductive coupling based WPT is the technology that promises to replace the plug-in charging system. It is desired that the WPT system should provide regulated current and power with high efficiency. Due to the instability in the connected load, the system output current, power, and efficiency vary. To solve this issue, a buck converter is implemented on the secondary side of the WPT system, which adjusts its internal resistance by altering its duty cycle. To control the duty cycle of the buck converter, a discrete fast terminal sliding mode controller is proposed to regulate the system output current and power with optimal efficiency. The proposed WPT system uses the LCC-S compensation topology to ensure a constant output voltage at the input of the buck converter. The LCC-S topology is analyzed using the two-port network theory, and governing equations are derived to achieve the maximum efficiency point. Based on the analysis, the proposed controller is used to track the maximum efficiency point by tracking an optimal power point. An ultra-capacitor is connected as the system load, and based on its charging characteristics, an optimal charging strategy is devised. The performance of the proposed system is tested under the MATLAB/Simulink platform. Comparison with the conventionally used PID and sliding mode controller under sudden variations in the connected load is presented and discussed. An experimental prototype is built to validate the effectiveness of the proposed controller.

Optimal Multistage Charging of NCA/Graphite Lithium-Ion Batteries Based on Electrothermal-Aging Dynamics

Lithium-ion (Li-ion) batteries have been extensively used in electric vehicles, portable electronics, cell phones, and laptops. The charging protocol, as one of the most critical technologies for Li-ion battery systems, has a significant impact on battery performance. Charging current affects battery degradation and charging time, and therefore, it needs to be carefully optimized. To this end, a novel charging protocol using a series of constant charging currents has been developed, which considers the charging time and the battery capacity fade simultaneously. These two conflicting charging objectives are traded off by solving a multiobjective optimization problem based on battery electrothermal-aging behavior. Particle swarm optimization has been applied to obtain the optimal charging current profile. Three optimal charging strategies for minimum charging time, minimum battery aging, and balanced charging performance are obtained by changing the weight factor. The proposed balanced charging is capable of reducing the charging time significantly with a negligible increase in capacity degradation compared with the 0.5 C constant-current constant-voltage (CC-CV) strategy recommended by the manufacturer.

A new on-line method for lithium plating detection in lithium-ion batteries

The desire to move towards the rapid charging of lithium-ion batteries has motivated many researchers to understand the underpinning degradation mechanisms as a precursor for the design of novel models and control algorithms to help mitigate their occurrence. It is widely reported that lithium plating is a significant ageing mechanism that occurs when charging the battery at low temperatures, high C-rates and high state of charge (SOC). There are multiple efforts embracing the measurement of different parameters during charging, such as battery voltage and current, that may collectively provide critical information useful for the detection of lithium plating. Much of this research is focussed on the post-processing of such data after completion of charge to infer the onset of lithium plating. This study aims to extend recent work, by proposing a new method of lithium plating detection, based on an estimation of cell impedance. This approach is able to operate in real-time during charging and therefore transferable to the battery management system (BMS). Experimental results highlight that the proposed method is highly sensitive and capable of effectively detecting the onset of lithium plating in real-time, underpinning the design of future optimal charging strategies to minimize lithium plating.

A Split-Future MPC Algorithm for Lithium-Ion Battery Cell-Level Fast-Charge Control

Constrained predictive control has emerged as a viable candidate for generating optimal real-time charging strategies for lithium ion batteries. Able to conform to hard constraints on problem variables, model predictive control (MPC) formulates an optimal 2-norm solution at each time step and can thus assure safe and reliable fast-charge operation. Standard MPC implementations make certain simplifying assumptions regarding future control actions beyond a specified control horizon. This paper demonstrates the potential gains that can be realized for the battery charge problem by relaxing these assumptions.

Distributed Scheduling of Charging for On-Demand Electric Vehicle Fleets

Abstract Consider a fleet of N autonomous electric vehicles (EVs), where EV n has a battery state βn. Customers arrive at rate λ and each customer requests a trip that costs some battery charge b. The goal is to coordinate the pick-ups and charging of the fleet in order to minimize the discounted number of unpicked customers over an infinite time horizon. Solving the dynamic program for this scheduling problem is infeasible since the state space has an exponential size in N. We propose a distributed approach instead where each EV models the fleet as a field that picks the customer i.i.d. at random with some pick-up probability α (b). Based on this model, each EV computes its optimal charging and pick-up strategy, and reports which customer they are available to pick-up (as a function of b) or if they decided to charge. The server computes a maximal matching between customers and EVs, taking into account their reported availability. The process continues iteratively as each EV recomputes its optimal response to the new empirical α (b). We prove that the optimal local strategy is a threshold policy with respect to βn, where the threshold depends on α (b) and b. We also prove that the empirical pick-up probability α (b) converges to the true stationary probability, which explains why our algorithm converges to an equilibrium. We then study our algorithm in simulations, that show that at equilibrium, its performance is significantly better than that of an uncoordinated charging strategy. Therefore, our solution has low-complexity but is still sophisticated enough to coordinate the charging of the fleet.

A two-layer model to dispatch electric vehicles and wind power

In this paper, the optimal charging and discharging schedules of electric vehicle (EV) are studied considering wind power under the condition of distribution network. In view of the uncertainty of EV charging-discharging demand and wind power output, the Markov decision process is adopted to model the randomness of supply and demand. Considering the dimensional disaster caused by dispatching a large number of EVs’ charging and discharging behavior in a centralized way, this paper proposes the two-layer dispatching model based on Markov decision process. First, the lower EV agents are responsible for collecting the real-time charging-discharging demands for EV and report to the upper dispatching center. Then the upper dispatching center gives the optimal charging and discharging power according to the real-time distribution operating status, wind power output and the EV information reported by each EV agent. Last, the lower agent gives the optimal charging-discharging sequence of each EV according to the upper optimal power. The goal of the upper dispatching center considers the power losses in the distribution network, load variance and the matching degree between EV charging-discharging and wind power output. The goal of the lower EV agent considers the EV charging-discharging fees and costs by EV battery losses. When deciding the optimal charging strategy, we design the two-layer Rollout algorithm to decide the optimal charging-discharging strategy considering the impact on future strategy decisions by current strategy decisions. Finally, the optimal results under four different strategies are simulated on the IEEE 30-bus distribution network system. The simulation results show that the proposed model and strategy can effectively reduce the distribution network losses and load variance, and greatly improve the utilization rate of wind power. Compared to the cost of uncoordinated EV charging, EV charging-discharging fees and battery loss costs by the proposed strategy have been greatly reduced.

Optimal charging of large-scale electric vehicles over extended time scales

The multi-day spaced charging, hardly considered in current strides aiming at optimal electric vehicle charging, is the concern of this study, and thus, an extended time scale covering several days should be dealt with. This study firstly analyzed the routines and implementations of charging behaviors over the extended time scale and then quantified the range of charging start time of electric vehicles precisely, which turned out to be discontinuous, huge and complicated. Finally, the mathematical formulation of the optimal charging strategy over the extended time scale as well as its constraints was established. The proposed problem can be solved by MATLAB simulations, which was addressed in details. Moreover, numerous simulations were conducted and demonstrated that the proposed strategy can reach a better system load characteristics than the ordered charging on a daily basis with a good maneuverability.

Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station

The increasing use of electric vehicles connected to the power grid gives rise to challenges in the vehicle charging coordination, cost management, and provision of potential services to the grid. Scheduling of the power in an electric vehicle charging station is a quite challenging task, considering time-variant prices, customers with different charging time preferences, and the impact on the grid operations. The latter aspect can be addressed by exploiting the vehicle charging flexibility. In this article, a specific definition of flexibility to be used for an electric vehicle charging station is provided. Two optimal charging strategies are then proposed and evaluated, with the purpose of determining which strategy can offer spinning reserve services to the electrical grid, reducing at the same time the operation costs of the charging station. These strategies are based on a novel formulation of an economic model predictive control algorithm, aimed at minimising the charging station operation cost, and on a novel formulation of the flexibility capacity maximisation, while reducing the operation costs. These formulations incorporate the uncertainty in the arrival time and state of charge of the electric vehicles at their arrival. Both strategies lead to a considerable reduction of the costs with respect to a simple minimum time charging strategy, taken as the benchmark. In particular, the strategy that also accounts for flexibility maximisation emerges as a new tool for maintaining the grid balance giving cost savings to the charging stations.

An optimal charging strategy for crowdsourcing platforms

PurposeThe purpose of this paper is to develop an optimal charging strategy for a third-party crowdsourcing platform.Design/methodology/approachBased on the auction theory, the Stackelberg game theory and the systems theory, this paper presents a new model from the perspective of risk sharing between solution seekers and the crowdsourcing platform, given the utility maximization of the seekers, the crowdsourcing platform and the solvers.FindingsBased on the results, this study shows that the menu of fees, which includes different combinations of a fixed fee and a floating fee schedule, should be designed to attract both solution seekers and solvers. In addition, the related prize setting and the expected payoff for each party are presented.Practical implicationsThis study is beneficial for crowdsourcing platform operators, as it provides a new way to design charging strategies and can help in understanding key influential factors.Originality/valueTo the best of the authors’ knowledge, this study is one of the first to simulate the interactions among the three stakeholders, thereby providing a novel model that includes a fixed fee and a floating commission.

Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning

Driven by the recent advances in electric vehicle (EV) technologies, EVs have become important for smart grid economy. When EVs participate in demand response program which has real-time pricing signals, the charging cost can be greatly reduced by taking full advantage of these pricing signals. However, it is challenging to determine an optimal charging strategy due to the existence of randomness in traffic conditions, user's commuting behavior, and the pricing process of the utility. Conventional model-based approaches require a model of forecast on the uncertainty and optimization for the scheduling process. In this paper, we formulate this scheduling problem as a Markov Decision Process (MDP) with unknown transition probability. A model-free approach based on deep reinforcement learning is proposed to determine the optimal strategy for this problem. The proposed approach can adaptively learn the transition probability and does not require any system model information. The architecture of the proposed approach contains two networks: a representation network to extract discriminative features from the electricity prices and a Q network to approximate the optimal action-value function. Numerous experimental results demonstrate the effectiveness of the proposed approach.

Health-Aware Multiobjective Optimal Charging Strategy With Coupled Electrochemical-Thermal-Aging Model for Lithium-Ion Battery

Battery fast charging strategies have gained an increasing interest toward the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifespan, and charging time, in this article, we propose a novel health-aware multiobjective optimal charging strategy to simultaneously shorten the charging time and relieve the battery degradation. The multiobjective optimal charging problem is formulated based on a coupled electrochemical-thermal-aging battery model. Constraints are explicitly imposed on physically meaningful state variables to avoid hazardous operations. Charging duration and battery aging process are well traded-off. Strategies for minimum-time and health-aware fast charging are investigated using different input current bounds, subject to both side reaction and temperature constraints. The experimental results validate that the presented multiobjective health-aware optimal charging algorithm is capable of reducing the charging time from its benchmarks largely without sacrificing the state-of-health of the battery.

Fast charging optimization for lithium-ion batteries based on dynamic programming algorithm and electrochemical-thermal-capacity fade coupled model

Enabling fast charging of lithium-ion batteries may accelerate the commercial application of electric vehicles (EVs). The fast charging, however, could lead to capacity fade, lithium plating, and thermal runaway. This paper develops an optimal multi-stage charging protocol for lithium-ion batteries to minimize capacity fade due to the solid-electrolyte interphase (SEI) increase, to maximize the SEI potential to decrease the lithium plating, and to reduce the temperature rise to avoid a thermal runaway situation. An electrochemical-thermal-capacity fade coupled model is developed to monitor the battery internal state. The dynamic programming (DP) optimization algorithm is employed to search for the suboptimal charging current profiles. The optimization results illustrate that the optimized charging current profile varies with the state of charge (SOC) and the cycle number. As compared to the constant current charging protocol, each optimized charging strategy can reduce the capacity fade ratio by 4.6%, increase the SEI potential by 57%, and reduce the temperature rise by 16.3% for over 3300 charging-discharging cycles, respectively.

Effects of the tenants electricity law on energy system layout and landlord-tenant relationship in a multi-family building in Germany

Multi-family buildings (MFB) accommodate 53% of the German apartment stock. Although PV-systems on single-family buildings are widely implemented, the PV-potential on MFBs has barely been touched. Therefore, the German government introduced the Mieterstromgesetz, the tenants electricity law (TEL), in 2016. This law exempts electricity directly produced and consumed in a building from certain charges and taxes. Within the TEL framework, the landlord acts as the local electricity provider and can profit from selling electricity to the tenants and tenants can save electricity costs. This paper analyses the techno-economic effects of the TEL on the energy system layout of a MFB in Germany. Furthermore, it gives implications on how the TEL affects the tenant-landlord relationship. In this analyses, a MILP model is used to maximize the net present value (NPV) and determines the optimal layout and dispatch of the energy system. The model can choose to invest in PV, CHP and a battery storage system. Additionally, one to six electric vehicles (EVs) are integrated into the model. The novelty of this paper is the model-based analysis of the German Mieterstromgesetz considering EVs. The results show that the combination of PV and CHP is the most profitable system layout with NPVs up to 31.9ke. An optimized charging strategy increases the self-consumption rate and the NPV substantially compared to a fast-charging-strategy. Thus, the TEL can create a symbiotic relationship between landlords and tenants.

Research on adaptability of charging strategy for electric vehicle power battery

Charging is an important part in daily use and maintenance of power battery, while charging strategy will have different effects on performance and charging efficiency of power battery. In this paper, it use 18650 cell of Lithium nickel cobalt manganese oxide as the research object to explore effects of key parameters on battery charging and discharging capacity, charging time, charging temperature rise and energy efficiency in constant current charge, Constant current constant voltage charge, multi-stage constant current charge, intermittent charge Strategy with variable current, pulse current charge. At the same time, it compares differences of battery performance under different charging strategies. Based on data analysis, it selects three optimal charging strategies with significant advantages over conventional charging strategies. The results of this paper have a guiding significance for formulation of vehicle charging strategy.

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

Electro-mobility is increasing significantly in the urban public transport and continues to face important challenges. Electric bus fleets require high performance and extended longevity of lithium-ion battery at highly variable temperature and in different operating conditions. On the other hand, bus operators are more concerned about reducing operation and maintenance costs, which affects the battery aging cost and represents a significant economic parameter for the deployment of electric bus fleets. This paper introduces a methodological approach to manage overnight charging of an electric bus fleet. This approach identifies an optimal charging strategy that minimizes the battery aging cost (the cost of replacing the battery spread over the battery lifetime). The optimization constraints are related to the bus operating conditions, the electric vehicle supply equipment, and the power grid. The optimization evaluates the fitness function through the coupled modeling of electro-thermal and aging properties of lithium-ion batteries. Simulation results indicate a significant reduction in the battery capacity loss over 10 years of operation for the optimal charging strategy compared to three typical charging strategies.

Electric Vehicles as Flexibility Management Strategy for the Electricity System—A Comparison between Different Regions of Europe

This study considers whether electric vehicles (EVs) can be exploited as a flexibility management strategy to stimulate investments in and operation of renewable electricity under stringent CO2 constraints in four regions with different conditions for renewable electricity (Sweden, Germany, the UK, and Spain). The study applies a cost-minimisation investment model and an electricity dispatch model of the European electricity system, assuming three types of charging strategies for EVs. The results show that vehicle-to-grid (V2G), i.e., the possibility to discharging the EV batteries back to grid, facilitates an increase in investments and generation from solar photovoltaics (PVs) compare to the scenario without EVs, in all regions except Sweden. Without the possibility to store electricity in EV batteries across different days, which is a technical limitation of this type of model, EVs increase the share of wind power by only a few percentage points in Sweden, even if Sweden is a region with good conditions for wind power. Full electrification of the road transport sector, including also dynamic power transfer for trucks and buses, would decrease the need for investments in peak power in all four regions by at least 50%, as compared to a scenario without EVs or with uncontrolled charging of EVs, provided that an optimal charging strategy and V2G are implemented for the passenger vehicles.