Charging Control Strategy Research Articles

CDDPG: A Deep-Reinforcement-Learning-Based Approach for Electric Vehicle Charging Control

Electric vehicle (EV) has become one of the most critical components in the smart grid with the applications of the Internet-of-Things (IoT) technologies. Real-time charging control is pivotal to ensure the efficient operation of EVs. However, the charging control performance is limited by the uncertainty of the environment. On the other hand, it is challenging to determine a charging control strategy that is able to optimize multiple objectives simultaneously. In this article, we formulate the EV charging control model as a Markov decision process (MDP) by constructing state, action, transition function, and reward. Then, we propose a deep-reinforcement-learning-based approach: charging control deep deterministic policy gradient (CDDPG) to learn the optimal charging control strategy for satisfying the user’s requirement of battery energy while minimizing the user’s charging expense. We utilize the long short-term memory (LSTM) network that extracts the information of previous energy price to determine the current charging control strategy. Moreover, Gaussian noise is added to the output of the actor network to prevent the agent from sticking into the nonoptimal strategy. In addition, we address the limitation of sparse rewards by using two replay buffers, of which one is used to store the rewards during the charging phase and another is used to store the rewards after charging is completed. The simulation results prove that the CDDPG-based approach outperforms the deep- $Q$ -learning-based approach (DQL) and the deep-deterministic-policy-gradient-based approach (DDPG) in satisfying the user’s requirement for the battery energy and reducing the charging cost.

DC power harvesting system using streaming electrification and an opposite charge extractor

Abstract Many studies on solid-liquid triboelectricity have used electrons in water as a means to generate electricity. However, for such applications researchers have utilized the same mechanism as that formalized for solid-solid triboelectricity. In this paper, a direct charge control method for generating liquid-solid triboelectricity is proposed. This mechanism suggests a new method for solid-liquid state triboelectricity using the inherent properties of a liquid. The proposed direct charge control strategy employs a liquid-solid triboelectricity to separate positive and negative charges in water. In the case of indirect charge control, external electrons from the ground or an electrode are used and thus, suitable electrical parts are needed for rectification. This can reduce the usable power. In contrast, direct charge control does not require additional electrical components for rectification, and available power is conserved. Upon overcoming certain limitations, normal operation of a system without rectification was realized, and a capacitor (100 μF) was charged to over 14 mV. In the relationship between the streaming electrification equation and the obtained results, the electronegativity of the material, tube length, and number of tubes affect the charging voltage. Here, the charged voltage increased above 100 mV as the tube length was increased. Such finding indicates that the output of direct charge control can be enhanced by various factors, thereby pioneering new areas for solid-liquid triboelectricity.

Optimal Anti-Lock Braking Control With Nonlinear Variable Voltage Charging Scheme for an Electric Vehicle

An optimal anti-lock braking control strategy using nonlinear variable voltage charging scheme for an electric-wheel vehicle is developed with aim of improving energy recovery efficiency on the premise of vehicle safety under the critical braking situation. A variable voltage charging control scheme is proposed with its operational principles to obtain maximum energy recovery as well as to balance the energy differences of each battery cell during the anti-lock braking. Given the nonlinearity of in-wheel motor, the nonlinear variable voltage charging control law is obtained on the basis of parameters fitting via experimental data. An ideal regenerative braking torque calculated by the nonlinear variable voltage charging control law is set as one of the disturbance vectors and an optimal sliding mode–anti-lock braking control strategy is developed to provide optimal hydraulic pressure for anti-lock braking system. In this manner, the torque allocation process is omitted by using a compensation control of the hydraulic braking torque. Simulation results corroborate the effectiveness of the proposed optimal anti-lock braking control strategy with higher energy recovery efficiency.

Reinforcement Learning-Based Distributed BESS Management for Mitigating Overvoltage Issues in Systems With High PV Penetration

High levels of penetration of distributed photovoltaic generators can cause serious overvoltage issues, especially during periods of high power generation and light loads. There have been many solutions proposed to mitigate the voltage problems, some of them using battery energy storage systems (BESS) at the PV generation sites. In addition to their ability to absorb extra power during the light load periods, BESS can also supply additional power under high load conditions. However, their capacity may not be sufficient to allow charging every time when power absorption is desired. Therefore, typical PV/BESS may not fully prevent over-voltage problems in power distribution grids. This work develops a cooperative state of charge control scheme to alleviate the BESS capacity problem through Monte Carlo tree search based reinforcement learning (MCTS-RL). The proposed intelligent method coordinates the distributed batteries from other regions to provide voltage regulation in a distribution network. Furthermore, the energy optimization process during the day hours and the simultaneous state of charge control are achieved using model predictive control (MPC). The proposed approach is demonstrated on two test cases, the IEEE 33 bus system and a practical medium size distribution system in Alberta Canada.

Uncertainty-aware state estimation for electrochemical model-based fast charging control of lithium-ion batteries

Fast charging capability is considered a critical factor for the widespread adoption of electric vehicles. High charging currents can, however, severely affect battery health due to the danger of metallic lithium deposition on the anode and consequent degradation reactions. The charging speed should therefore be limited with respect to battery temperature, state of charge, and cell design, governing the onset point of lithium plating. Electrochemical models are a suitable tool providing continuous estimates of the anode potential as the main lithium plating indicator while covering a wide operational range. In this article, we present a novel charging control scheme based on a real-time capable simulation framework with adjustable model resolution. A profound investigation of error sources and modeling uncertainties motivates online state corrections towards a lower bound of the anode potential, which are realized by selective adjustments of the lithium distribution within electrode particles based on the full cell voltage error. Simulations of controlled and conventional CC charging profiles indicate the importance of continuously adapting the charging power for different operating conditions. Further, simulated state and parameter distortions as they might result from initialization and parameterization errors show that our estimation strategy can mitigate the risk of unsafe control operations.

RETRACTED: Modeling and analysis of dynamic wireless charging for electric vehicles under different working scenarios

Dynamic wireless charging technology can solve the charging problem of electric vehicle. There is little research on the influence of dynamic wireless charging on electric vehicle. This paper establishes the energy model of the vehicle system by using Advisor and MATLAB. An energy control strategy for dynamic wireless charging of electric vehicles is designed. The influence of dynamic wireless power transfer on energy storage system loss and electric vehicle range under different operating scenarios and different minimum battery charge states is studied. The variation of state of charge, energy usage and the overall system efficiency are compared and analyzed. This paper analyzes the influence of SOC by vehicle parameters and external parameters in dynamic wireless charging, and the influence on electric vehicle driving range further.

Research on Coordinated Charging Control Strategy Load Optimization of Electric Vehicles in Residential Area

For the power utilization load encountered in the construction of electric vehicles (EVs) charging facilities in residential area, the coordinated charging control strategy for EV charging loads is studied. Based on the utilization electric load model, EV model, and distribution network information model of the residential area during the EV user using, the minimizing the fluctuation of the EV users charging cost, utilization power load and EV charging load is established. It is verified by an EV coordinated charging residential area, the sliding recursive algorithm employed can realize peak-load shifting, which can effectively reduce the effect of EV charging peak load and move the power peak to the second half of the night.

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.

Research on Dual Loop Control Strategy of Dynamic Wireless Charging for Electric Vehicle

This paper focuses on a dynamic wireless charging method of electric vehicles with segmented structure, which uses multiple transmitting coils at the transmitting end to realize wireless power supply for electric vehicles in motion. Based on the structure of the dynamic wireless charging system of the electric vehicle with magnetic coupling resonance, the system is modeled and analyzed by using the equivalent circuit theory, and a control strategy of the transmitting end is proposed, which can effectively solve the problem of coil deviation and dynamic characteristics when the receiving coil and the transmitting coil of the electric vehicle are in the charging process. A double loop controller is designed to adjust the power and current of the transmitting end, which allows the sequence and timely activation of the segmented transmitting coil. Under the condition of no-load and load, the current of the transmitting coil is controlled at the reference value to compensate the power transmission reduction caused by the lateral misalignment of the vehicle and prevent the overload at one time. The system is modeled and analyzed with Simulink software, and the feasibility of the control method is determined.

A Three-Phase Integrated Battery Charger for EVs Based on Six-Phase Open-End Winding Machine

This article presents a new configuration of onboard integrated battery charger for electric vehicles (EVs) using six-phase machine and three-phase ac source. The configuration proposes the utilization of symmetrical six-phase open-end winding machine, twelve-leg inverter, and a dc–dc converter. The charger circuit is constructed by connecting directly the three-phase grid to the middle point of machine's phase winding, where the grid currents split into equal portions and flow in opposite directions, without adding an extra mechanical switch (or relay). From this concept, the proposed system has the advantages of obtaining high-power density with a large value of inductance for grid-side filter during charging and avoiding an electromagnetic torque as well as providing a high fault tolerant capability. Moreover, through proper control strategy, the proposed system is able to operate under unity power factor (UPF) with total harmonic distortion (THD) below 5%. In this article, the possibility of vehicle-to-grid (V2G) mode is also investigated. Detail of the control strategy for charging and V2G are given. Furthermore, simulation and experimental results are provided to validate the theoretical analysis.

Research on the Combined Control Strategy of Low Temperature Charging and Heating of Lithium-Ion Power Battery Based on Adaptive Fuzzy Control

A low temperature environment will lead to the decrease of chemistry reaction rate and increase of the internal resistance of the lithium battery. In addition, the excessive charging current will cause the lithium to separate out and even the permanent attenuation of battery capacity. In order to solve these problems, this paper proposes a low-temperature charging heating combined control strategy, which takes the temperature acceptable charging current of the battery at low temperature as the charging current constraint and the maximum output power of the system as the power constraint. Firstly, a scheme of combined charging and heating control system is put forward. Secondly, the low temperature charging control strategy based on adaptive fuzzy control is established and then the model is simulated and analyzed in MATLAB software. At last, a Chroma 72,001 charge and discharge tester is used to conduct a low temperature test on 18,650 lithium iron phosphate battery monomers. The results show that the low-temperature charging control strategy proposed in this paper has a more stable temperature control effect on the battery, the constant current charging time of the battery is reduced by 14% compared with the traditional threshold control method, and the overall charging energy consumption is reduced by 5.6%.

A Control Strategy for Smart Energy Charging of Warehouse Material Handling Equipment

The common driver of the ‘green-warehouse’ strategy is based on the reduction of energy consumption. In warehouses with ‘picker-to-part’ operations the minimization of energy due to material handling activities can be achieved by means of different policies: by adopting smart automatic picking systems, by adopting energy-efficient material handling equipment (MHE) as well as by identifying flexible layouts. In most cases, these strategies require investments characterized by high pay-back times. In this context, management strategies focused on the adoption of available equipment allow to increase the warehouse productivity at negligible costs. With this purpose, an optimization model is proposed in order to identify an optimal control strategy for the battery charging of a fleet of electric mobile MHE (e.g., forklifts), allowing minimizing the economic and environmental impact of material handling activities in labor-intensive warehouses. The resulting scheduling problem is formalized as an integer programming (IP) problem aimed at minimizing the total cost, which is the sum of the penalty cost related to makespan over all the material handling activities and the total electricity cost for charging batteries of MHE. Numerical experiments are used to investigate and quantify the effects of integrating the scheduling of electric loads into the scheduling of material handling operations.Click here to enter text.

Research on Battery Charging and Discharging Control Strategy Based on AGC Scheduling

In this paper, a battery charging and discharging method for AGC scheduling is described. For the practical consideration of the northern region, due to the lack of abundant hydropower resources, it is greatly affected by the temperature in winter and can only be used as a fine-tuning function, so the hydropower effect during the heating period in winter is not considered. Thermal power and battery energy storage devices are established to participate in the AGC scheduling system. The system state is mainly divided into four control states: conventional power supply control state, battery discharge state, battery charging state, and clean energy control state. The working area is judged on the premise that all new energy is connected to the network, and the limit control strategy is used. The lower limit of output of thermal power unit and the limit of capacity of battery unit are critical conditions. Thermal power is the main power and battery is the auxiliary power stability.

Anode potential controlled charging prevents lithium plating

We report a novel anode potential controlled charging strategy for lithium-ion cells which eliminates lithium plating under most aggressive conditions, such as at low temperatures.

Optimal Fast Charging Control for Lithium-ion Batteries

Fast charging has gained an increasing interest in the convenient use of Lithium-ion batteries. This paper develops a constrained optimization based fast charging control strategy, which is capable of meeting needs in terms of charging time, energy loss, and safety-related charging constraints. To solve it with less computational effort, a two-layer optimization strategy is proposed, where a charging time region contraction method is utilized to search the minimum expected charging time in the top layer, and the bottom layer uses the barrier method to calculate the corresponding optimal charging current with the charging time given by the top layer. Through utilizing this two-layer optimization method, the optimal charging current can be obtained that leads to the shortest charging period while guaranteeing the charging constraints with relatively low computational complexity. Extensive simulation results are provided to validate the proposed optimal fast charging control strategy, which well outperforms the constant current-constant voltage method.

Constant Current Charging and Maximum System Efficiency Tracking for Wireless Charging Systems Employing Dual-Side Control

Wireless power transfer (WPT) systems have attracted much attention because of their safety, convenience, and environmental friendliness. For wireless supercapacitor charging, the system efficiency and charging current are highly dependent on the load that varies over a wide range. In this article, a simultaneous maximum system efficiency (MSE) tracking and constant current (CC) charging control scheme for a supercapacitor is proposed. For CC charging, a double-sided LCC topology is chosen due to its characteristic of providing a load-independent output current. Furthermore, the impact of the coil internal resistance on the system characteristics (especially the charging current) is investigated, so a semiactive rectifier is introduced on the secondary side to achieve accurate CC charging and improve the system robustness. Based on the variable-step perturbation and observation algorithm, the MSE is tracked by searching for the minimum system input dc current using a primary-side buck converter on the premise that the charging current reaches its target value. The abovementioned two control loops are independent, and mutual communication is unnecessary when they cooperate; thus, the system is simplified. The simulation and experimental results show great consistency with the theoretical analysis. The experimental system maintains the MSE of 86% during charging the supercapacitor from 20 to 50 V with 2 A.

An Autonomous Charge Controller for Electric Vehicles Using Online Sensitivity Estimation

Despite their sustainable benefits, large-scale adoption of electric vehicles (EVs) into the distribution system is challenging. Uncontrolled charging of EVs could increase voltage violations, system power losses, and feeder overloads. In this article, an autonomous EV charge control strategy is proposed to control EV charging in a way that mitigates their negative impacts. To ensure fair contribution from EVs connected to different nodes, both the local nodal voltage and sensitivity of voltage to load changes are used as inputs to the proposed controller. This is so because these two inputs have a complementary relationship. That is, nodes with lower voltages are generally more sensitive to load changes than those with higher voltages. In addition, a novel approach for locally estimating the voltage sensitivities online is proposed. This ensures the robustness of the proposed control strategy to changes in loading conditions and system configurations without the need for communication. An EV-rich test distribution system is modeled in the DIgSILENT PowerFactory environment and used to validate the proposed control strategy. The test results demonstrate the effectiveness and robustness of the proposed strategy considering different loading conditions, system reconfiguration, distributed generators, and voltage control devices.

Optimal Charging Control for Lithium-Ion Battery Packs: A Distributed Average Tracking Approach

Effective lithium-ion battery pack charging is of extreme importance for accelerating electric vehicle development. This article derives an optimal charging control strategy with a leader–followers framework for battery packs. Specifically, an optimal average state-of-charge (SOC) trajectory based on cells’ nominal model is first generated through a multiobjective optimization with consideration of both user demand and battery pack's energy loss. Then, a distributed charging strategy is proposed to make the cells’ SOCs follow the prescheduled trajectory, which can effectively suppress the violation of the safety-related charging constraints through online battery model bias compensation. This article highlights the superiorities of the proposed leader–followers-based charging framework that combines the offline scheduling and online closed-loop regulation for battery pack charging, which brings benefits to significantly reduce the computational burden for the charger controller as well as improve the robustness to suppress the negative impact caused by the cell's model bias. Extensive illustrative results demonstrate the effectiveness of the proposed optimal charging control strategy.

Assessment of flexible electric vehicle charging in a sector coupling energy system model – Modelling approach and case study

The transition of the motorized private transport sector from fossil fuel to electricity-based technologies is a widely discussed strategy that can contribute towards the achievement of the set climate protection targets. To accurately analyse the challenges and opportunities associated with this transition, the entire energy system needs to be considered in a holistic way. This study introduces two different methodological approaches for the assessment of battery electric vehicles. Both are implemented into the sector coupling, structure optimising model REMod and assessed for a future, nearly CO2 neutral German energy system.The results show that realistic driving profiles could lead to simultaneous vehicle charging, which result in higher peak loads and eventually require an increase in power plant capacity. The model shows that once vehicles are charged irrespective of the residual load, electricity-demanding technologies throughout all sectors are progressively replaced by hydrogen or other gas-based technologies, as these options do not lead to an increase in electricity demand. This development negatively impacts the deployment of electric heat pumps and battery electric vehicles, which would play a major role otherwise. The cost-optimal system configuration can be achieved through implementation of a controlled charging strategy, shaving power load and supply peaks, integrating more power from variable renewable energy sources and reducing the yearly overall system costs by several billion Euros.

A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid

Electric vehicles (EVs) are an eco-friendly alternative to vehicles with internal combustion engines. Despite their environmental benefits, the massive electricity demand imposed by the anticipated proliferation of EVs could jeopardize the secure and economic operation of the power grid. Hence, proper strategies for charging coordination will be indispensable to the future power grid. Coordinated EV charging schemes can be implemented as centralized, decentralized, and hierarchical systems, with the last two, referred to as distributed charging control systems. This paper reviews the recent literature of distributed charging control schemes, where the computations are distributed across multiple EVs and/or aggregators. First, we categorize optimization problems for EV charging in terms of operational aspects and cost aspects. Then under each category, we provide a comprehensive discussion on algorithms for distributed EV charge scheduling, considering the perspectives of the grid operator, the aggregator, and the EV user. We also discuss how certain algorithms proposed in the literature cope with various uncertainties inherent to distributed EV charging control problems. Finally, we outline several research directions that require further attention.