Charging Algorithm Research Articles

Quantum Chemical Calculations for up to One Hundred Million Atoms Using Dcdftbmd Code on Supercomputer Fugaku

Dcdftbmd developed in our group was an efficient quantum chemical calculation code based on the divide-and-conquer density functional tight-binding (DC-DFTB) method. The bottleneck for its applications to over tens of million atoms was the required memory size. Herein, we optimized array setting and a newly implemented direct self-consistent charge (SCC) algorithm. Using 6144 nodes of the supercomputer Fugaku, the modified code enabled energy calculations at the SCC and non-SCC levels for approximately 5.4 × 107 and 1.1 × 108 atoms, respectively.

Dynamic Collaborative Charging Algorithm for Mobile and Static Nodes in Industrial Internet of Things

Industrial Internet of Things inevitably leads to the implementation of highly data-intensive devices, where the associated sensing nodes accelerate the energy consumption rate, which ultimately produces an energy bottleneck. To address this issue, this article proposes a <i>dynamic collaborative charging algorithm</i> that acts on both the mobile nodes and the static nodes in a sensing node network. The proposed scheme is to design a collaborative group of charging robots that can rendezvous with the sensing nodes. The group includes aerial charging vehicles (ACVs)&#x2014;able to charge the underpowered mobile nodes, and terrestrial charging vehicles (TCVs), which charge their targeted static nodes. The aim of this study is to optimize the charging effect and the energy cost in the rendezvous process. This approach consists of two subalgorithms: 1) a charging algorithm for mobile nodes (CAMNs) and 2) a charging algorithm for static nodes (CASNs). The CAMNs is designed so that each underpowered mobile node can be charged by a dedicated ACV. For this purpose, a deep learning model is trained to divide the underpowered mobile nodes into appropriate clusters, each of which is equipped with a mobile base station. The rendezvous process is then constructed as a mixed continuous/discrete optimization problem, which is solved by using the firefly algorithm. In addition, the CASNs ensures that the TCVs traverse their routes, charging static nodes as they proceed. This traversing process was formulated as a multiobjective optimization problem, solved by using genetic algorithm. Through various experiments and case studies, the results have demonstrated both the feasibility and the efficiency of the proposed algorithms.

Adaptive Congestion Control for Electric Vehicle Charging in the Smart Grid

This article proposes an adaptive control algorithm for plug-in electric vehicle charging without straining the power system. This control algorithm is decentralized and merely relies on congestion signals generated by sensors deployed across the network, e.g., distribution-level phasor measurement units. To dynamically adjust the parameter of this congestion control algorithm, we cast the problem as multi-agent reinforcement learning where each charging point is an independent agent which learns this parameter using an off-policy actor-critic deep reinforcement learning algorithm. Simulation results on a test distribution network with 33 primary distribution nodes, 1760 low voltage end nodes, and 500 electric vehicles corroborate that the proposed algorithm tracks the available capacity of the network in real-time, prevents transformer overloading and voltage limit violation problems for an extended period of time, and outperforms other decentralized feedback control algorithms proposed in the literature. These results also verify that our control method can adapt to changes in the distribution network such as transformer tap changes and feeder reconfiguration.

Electric vehicle charging network in Europe: An accessibility and deployment trends analysis

If coupled with a low-carbon electricity mix, electric vehicles (EVs) can represent an important technology for transport decarbonization and local pollutants abatement. Yet, to ensure large-scale EVs adoption, an adequate charging stations network must be developed. This paper provides the first comprehensive bottom-up analysis of the EV charging network in Europe. Combining a crowd-sourced database of charging stations with accessibility data and algorithms, we produce maps of the travel time to the most accessible EV charging station across Europe, we evaluate the charging points density and the number of active operators in different areas. We find that although recent years have witnessed a notable expansion of the EV charging network, stark inequalities persist across and within countries, both in terms of accessibility and of the charging points available to users. Our results allow for a better understanding of some of the key challenges ahead for ensuring mass EVs adoption throughout Europe and thus potentially reducing the environmental impact of the transport sector. • The paper presents a comprehensive analysis of the EV charging network in Europe. • We combine crowd sourced charging stations data, accessibility data and algorithms. • We produce maps of the travel time to the nearest charging station. • Competition in local charging markets is assessed based on the number of operators. • Results show that stark inequalities persist across and within countries.

State of Charge Influence on the Harmonic Distortion From Electric Vehicle Charging

There is a general interest from manufacturers, users, regulation institutions, and research around electric vehicles development and its relation with the grid infrastructure. The penetration of electric vehicles in distribution networks brings considerable challenges in terms of power quality, particularly harmonic emissions. Such challenges are more evident in scenarios with high penetration of electric vehicles which consider the clustering effect in the charging process. Methodologies such as hosting capacity model electric vehicles as a constant power load with constant harmonic distortion along the entire charging process. This article presents the relationship between the charging algorithms of the electric vehicle chargers, the state of charge of the batteries, and the harmonic current distortion generated during the charging process. This model proposes that the harmonic current distortion depends on the state of charge of the battery and the charging algorithm of the vehicle. As this study would change meaningfully, the current studies of power quality related to electric vehicles, practical measurements in two charging stations, and offline simulations of the identified on-board chargers are shown to support the content of this article.

A Joint Energy Replenishment and Data Collection Strategy in Heterogeneous Wireless Rechargeable Sensor Networks.

In wireless rechargeable sensor networks, mobile vehicles (MVs) combining energy replenishment and data collection are studied extensively. To reduce data overflow, most recent work has utilized more vehicles to assist the MV to collect buffered data. However, the practical network environment and the limitations of the vehicle in the data collection are not considered. UAV-enabled data collection is immune to complex road environments in remote areas and has higher speed and less traveling cost, which can overcome the lack of the vehicle in data collection. In this paper, a novel framework joining the MV and UAV is proposed to prolong the network lifetime and reduce data overflow. The network lifetime is correlated with the charging order; therefore, we first propose a charging algorithm to find the optimal charging order. During the charging period of the MV, the charging time may be longer than the collecting time. An optimal selection strategy of neighboring clusters, which could send data to the MV, was found to reduce data overflow. Then, to further reduce data overflow, an algorithm is also proposed to schedule the UAV to assist the MV to collect buffered data. Finally, simulation results verified that the proposed algorithms can maximize network lifetime and minimize the data loss simultaneously.

A Novel Optimal Charging Algorithm for Lithium-Ion Batteries Based on Model Predictive Control

Lithium-ion (Li-ion) batteries play a substantial role in portable consumer electronics, electric vehicles and large power energy storage systems. For Li-ion batteries, developing an optimal charging algorithm that simultaneously takes rises in charging time and charging temperature into account is essential. In this paper, a model predictive control-based charging algorithm is proposed. This study uses the Thevenin equivalent circuit battery and transforms it into the state-space equation to develop the model predictive controller. The usage of such models in the battery optimal control context has an edge due to its low computational cost, enabling the realization of the proposed technique using a low-cost Digital Signal Processor (DSP). Compared with the widely employed constant current-constant voltage charging method, the proposed charging technique can improve the charging time and the average temperature by 3.25% and 0.76%, respectively.

Dynamic planning for simultaneous recharging and relocation of shared electric taxies: A sequential MILP approach

Short driving range, limited chargers, and long charging times challenge the profitability of electric taxi operations. In this paper, a charging algorithm is developed to accompany a taxi service with online trip requests, which uses a private charging infrastructure for both slow and fast charging. The vehicle charging curves are assumed to be piece-wise linear functions. The proposed algorithm uses historical operation data to generate a pro-active planning that avoids queuing of vehicles. The algorithm is built upon three sequential, iterative, finite-horizon Mixed Integer Linear Programs. This iterative process, in which the three MILPs are solved sequentially, allows the current time-step to be optimized, while taking future time-steps into account. This is achieved by optimizing over multiple time-steps, but only implementing the current time-step in each iteration. The sequential aspect of the algorithm allows the vast amount of information over time and space to be exploited for charging trip decisions in real time, while maintaining a tractable computation time. The first level with the longest horizon is an aggregated, daily problem, that plans the charging duration required for the fleet. The second level has a horizon of up-to three hours and is an aggregated, zone-based problem for determining charger selection and empty vehicle relocations. The third level translates the outputs of the first two problems to executable decisions for individual vehicles based on their real-time location, state of charge, and assigned passengers. The first level is the most computationally expensive and is solved using Column Generation. The performance of the first two levels is then independent of the fleet size, which makes the algorithm highly scalable. A case study with travel data for the city of Barcelona is used to test the model. Results show that the proposed method can utilize the full capacity of the charging infrastructure, and improve the number of accepted requests by 14% compared to employing a naive charging rule.

Research on variable parameter power differential charge–discharge strategy of energy storage system in isolated island operating microgrid

Abstract This paper proposed an improved particle swarm optimization (PSO) algorithm for the variable parameter power difference charging and discharging strategy of battery energy storage system (BESS). The charge and discharge power of the BESS under different load intervals and state of charge (SOC) intervals are distributed, and the objective functions of peak shaving and valley filling standard deviation and minimum SOC off-line rate are established. The simulation model of peak shaving and valley filling is built, and the results show that the standard deviation of peak shaving and valley filling reduced by 22.58% compared with PSO. The effectiveness and feasibility of the improved PSO are verified.

Adaptive Task Planning for Multi-Robot Smart Warehouse

Using autonomous mobile robots is now a necessity for today’s large e-commerce warehouses to save time and energy, and to prevent human-based errors. Robotic Mobile Fulfillment System (RMFS) controls these robots as well as all other resources and tasks in a warehouse. There are challenges in the management of an RMFS-based smart warehouse because of the high dynamics in the system. Limited resources such as robots, stations, totes, and item spaces should be managed efficiently after tracking their status continuously. In this study, we propose a centralized task management approach that is adaptive to the system dynamics. We describe a novel task conversion algorithm that generates tasks from a batch of orders and provides a high pile-on value. Then we propose an adaptive heuristic approach to assign generated tasks to robots, considering system dynamics such as the location of robots and pods, utilization of totes, and age of the tasks. To evaluate the proposed algorithms, we perform an extensive set of simulations in a highly realistic environment including robot charging, replenishment process, and path planning algorithms. We show that the proposed task planning approach significantly reduces order completion time even for a high number of stock-keeping units (SKU). It also provides a balanced workload among robots. We analyze the optimal value of order batch size and the effect of important system parameters such as robot count, order count, and SKU. The obtained results shade light on how to design a smart warehouse system with high efficiency.

An Optimal Battery Charging Algorithm in Electric Vehicle-Assisted Battery Swapping Environments

In battery swapping environments, electric vehicles (EVs) can play roles as battery providers as well as consumers. In this paper, we propose an optimal battery charging algorithm (OBCA) where a battery swapping station (BSS) charges batteries in its storage with the consideration of the profile of the electricity price and the arrival rates of EVs. To maximize the net profit of BSS while maintaining the battery changing probability above a certain level (i.e., maintaining high quality of service (QoS) of BSS), we formulate a constraint Markov decision process (CMDP) problem and the optimal charging schedule for batteries in BSS is obtained by a linear programming (LP). Evaluation results demonstrate that OBCA with the optimal policy can improve the net profit of BSS up to 418&#x0025; compared to an electric price-aware scheme while maintaining high QoS of BSS.

Improvements to Multi-Scale, Mechano-Electrochemical Modeling Methods

Automotive battery manufacturers are working to improve individual cell and overall pack design by increasing their performance, durability, and range, while reducing cost; and active material volume change is one of the more complex aspects that needs to be considered during this process. Designers try to control or mitigate the effects of volume change using a variety of methods: inserting foam layers into battery packs, programming specific charging algorithms, using multiple active materials, etc. However, designers must resort to extensive testing to fully develop these methods for each new battery design because of the complexity in measuring and understanding the links between active material volume change’s mechano-electrochemical effects on the electrode and cell level. A multi-scale modeling approach shows promise to comprehensively account for active material volume change. In the work shown here, a mechano-electrochemical model was employed [1-4] to explore mechano-electrochemical relationships: (1) understanding the effects of realistic mechanical behavior of each cell component including foam packaging, (2) understanding the effects of thermodynamically non-ideal volume change behavior, and (3) relative lithiation (thus, volume change) behavior for electrodes with multiple active materials (such as a silicon-graphite composite anode). The unique ties between active material voltage, active material volume, and N:P capacity ratio will be discussed as well. References T. R. Garrick, K. Higa, S.-L. Wu, Y. Dai, X. Huang, V. Srinivasan and J. W. Weidner, Journal of The Electrochemical Society, 164, E3592 (2017).T. R. Garrick, X. Huang, V. Srinivasan and J. W. Weidner, Journal of The Electrochemical Society, 164, E3552 (2017).T. R. Garrick, K. Kanneganti, X. Huang and J. W. Weidner, Journal of The Electrochemical Society, 161, E3297 (2014).D. J. Pereira, J. W. Weidner and T. R. Garrick, Journal of The Electrochemical Society, 166, A1251 (2019).D. J. Pereira, M. A. Fernandez, K. C. Streng, X. X. Hou, X. Gao, J. W. Weidner, and T. R. Garrick, Journal of The Electrochemical Society, 167, 080515 (2020).

Solar PV parking lots to maximize charge operator profit for EV charging with minimum grid power purchase.

ABSTRACT The paper proposed the charging algorithm for the rooftop solar photovoltaic (PV) parking lots. The aim of the paper is to promote the Solar PV usage for the electric vehicle (EV) parking with the profit maximization to the system operator along with the quality service to the customer. Although various studies exist in the literature for profit maximization to the system operator depending upon the grid tariff rate, the charging strategy to maximize the usage from the renewable power availability with reduced grid power purchase has been proposed for the given work. The real-time coordination of dynamic arrival and departure time of the EV is done with the dispatch algorithm, to ensure the required charging of the EV at the time of departure. Although the installation cost of the Solar PV at the parking lot may be a problem concern for the commercial acceptance but the reduced carbon emission, profit margin, and the low payback period are tempting factors for the need for tomorrow.

Open Source Algorithm for Smart Charging of Electric Vehicle Fleets

Smart charging assigns charging capacities between vehicles in limited charging infrastructures. Smart charging solutions are becoming widespread with increasing numbers of commercial offerings. However, commercial solutions are intransparent regarding algorithm implementation. In contrast, open source solutions are transparent and open for collaborative development and scientific research. This article presents an open source package with a smart charging algorithm. The algorithm schedules heterogeneous fleets of vehicles for charging while ensuring a fair share for each vehicle. We present implementation aspects of the smart charging algorithm including data structures and representational state transfer application programming interfaces. Additionally, the smart charging algorithm was validated in a one-year field test. Experimental results of the field test show elcetronic vehicles (EVs) at six charging stations can be scheduled for charging when the grid connection only allows two EVs to charge concurrently. Runtime measurements demonstrate the smart charging algorithm is applicable in real-time and scales to large fleet sizes.

Development of Modular DC-DC Converters for Low-Speed Electric Vehicles Fast Chargers

To increase the utilization of Low-Speed Electric Vehicles (LS-EVs), rapid recharging of the EV’s battery pack turn out to be essential. This permits reduced charging times, greater vehicle utility, and broader adoption of LS-EVs. This paper presents a modular Input-Series Output-Parallel (ISOP) DC-DC converter for LS-EVs fast chargers. A generalized small-signal analysis applicable for any multimodule connection (Input-Series Input-Parallel Output-Series Output-Parallel (ISIP-OSOP) is introduced. The employed topology is a multimodule DC-DC converter based on Dual Active Bridge (DAB). Nonetheless, a single bridge is utilized at the primary side, and the modularity concept is applied to the high-frequency transformer and the second bridge where the connection of the modules is ISOP. In the presented system, 3-modules are employed where each module is rated at 1.5kWto achieve the desired power rating, which is 4.5kW. The charging process is achieved from a single-phase outlet. However, due to the high output current, a modular approach is required to avoid high losses. Uniform power-sharing is achieved through a direct output current sharing control, ensuring stability without the need for input voltage sharing loops, unlike the conventional ISOP converters. This is due to the fact that the proposed configuration uses only a single capacitor at the input side, avoiding the inherent instability problem caused by the output current sharing control. The controller is examined using a 3-module ISOP DC-DC converter, where the controlled current is following the reflex charging algorithm. Simulation results using the Matlab/Simulink platform are provided to elucidate the presented concept considering parameter mismatches, where the input voltage and the output current are equally shared among the three modules.

Carbon efficient smart charging using forecasts of marginal emission factors

Battery electric vehicles do not emit CO2 from an internal combustion engine but can cause emissions while charging electricity generated by remote fossil power plants. Smart charging offers the possibility to reduce carbon dioxide emissions (CO2) by shifting the charging sessions to moments with lower emissions in power generation. However, this requires the battery electric vehicle user to accept postponed charging and a system to shift charging towards times with lower CO2 emissions. This requires a forecast of the marginal emission factors of the energy system. While marginal emission factors are often used to evaluate CO2 saving potentials of smart charging in long-term scenarios, there is little insight on the saving potentials of individual charging sessions in the short run. We derive marginal emission factors for Germany in 2017 using an established regression model and generate short-term predictions of marginal emission factors using a multilayer perception (MLP). The forecasts can provide feedback to drivers and allow scheduling for CO2 efficient smart charging. Comparison between this approach and immediate charging throughout a year in the German power system allows calculating the CO2 saving potentials of the system. The results show that marginal emission factors in Germany depend on the system’s load with peak load levels having the smallest marginal emission factors. Using average instead of marginal emission factors, can result in misinformation and even increase emissions. We propose forecasts of marginal emission factors that rely on short-term load forecasts and are accurate enough to obtain substantial saving in CO2 emissions from 1% to 10% depending on the charging parameters. While using this approach as a smart charging algorithm would result in increasing peak loads, the methodology might be a way to provide BEV users with real-time feedback of their charging behaviour and increase their willingness to postpone charging.

An efficient on-demand charging schedule method in rechargeable sensor networks

Nowadays, wireless energy charging (WEC) is emerging as a promising technology for improving the lifetime of sensors in wireless rechargeable sensor networks (WRSNs). Using WEC, a mobile charger (MC) reliably supplies electric energy to the sensors. However, finding an efficient charging schedule for MC to charge the sensors is one of the most challenging issues. The charging schedule depends on remaining energy, geographical and temporal constraints, etc. Therefore, in this article, a novel efficient charging algorithm is proposed, such that the lifetime of the sensors in WRSN are increased. The proposed algorithm uses a multi-node MC that can charge multiple sensors at the same time. In this algorithm, the charging requests of the low-energy sensors are received by the MC. Then, a reduced number of visiting points are determined for the MC to visit them. The visiting points are within the charging range of one or more requesting sensors. Thereafter, an efficient charging schedule is determined using an adaptive fuzzy model. Sugeno-fizzy inference method (S-FIS) is being used as a fuzzy model. It takes remaining energy, node density, and distance to MC, as network inputs for making real-time decisions while scheduling. Through simulation experiments, it is finally shown that the proposed scheme has higher charging performance comparing to base-line charging schemes in terms of survival ratio, energy utilization efficiency, and average charging latency. In addition, ANOVA tests are conducted to verify the reported results.

Agent-based bidirectional charging algorithms for battery electric vehicles in renewable energy communities

This paper introduces a framework for agent based autonomous charging and discharging of Battery Electric Vehicle (BEV) at local energy communities. Agents are programmed to control the bidirectional charging according to green energy utilisation incentives, based on load and generation forecasts. The optimization is achieved within a group of independent prosumers following a fully distributed approach using multiple self-organising agents. No central instance is needed for communication, billing or decision making. To demonstrate the ecological benefits of the system, simulations for a car pool, with a Photovoltaic (PV) plant, in a residential neighborhood were performed. The simulations resulted in an increased community PV self-consumption value of 48% compared to 29% in case of uncontrolled charging processes.

ESS의 안정성 증대를 위한 Li-ion 배터리의 충전 알고리즘에 대한 특성 검토

This paper examines methods for Li-ion battery charging algorithm used in ESS(Energy Storage System). Recently, it is necessary to study how to restore the stability and reliability of ESS because of the lithium ion battery fires. Therefore, in this paper, the battery charging algorithm studied to date has been simulated with respect to CC-CV method, MCC-CV method, BC and PC methods, etc. In addition, comparison and analysis were performed for the charging time for each algorithm. As a result, the charging algorithm of the BC method using a high C-rate charged the battery fastest. it is considered that the consideration of C-rate is essential for stable charging of ESS.

An optimal charging algorithm to minimise solid electrolyte interface layer in lithium-ion battery

This article presents a novel control algorithm for online optimal charging of lithium-ion battery by explicitly incorporating degradation mechanism into control, to reduce the degradation process. The health of battery directly relates to degradation and capacity fade in cycles of charging. We mainly focus on the growth of the solid electrolyte interface (SEI) layer, which is the primary source of degradation of batteries. This article addresses the challenge of minimising SEI layer growth during charging by incorporating the first-order SEI layer growth rate model into a non-linear model predictive control approach. A single particle model (SPM) is used for optimal charging using orthogonal projection-based model reformulation. Gauss pseudo-spectral method is used for the optimisation of charging trajectories. Results of the optimal algorithm are compared with the traditional constant current constant voltage (CCCV) approach without considering SEI layer growth. It is ensured that overpotential caused by lithium plating remains in a healthy regime which is another feature of the proposed strategy. Simulation results are presented to demonstrate the advantages of the proposed charging method.