Huachun Han

Interactive charging strategy of electric vehicles connected in Smart Grids

Electric vehicles (EVs) represent an important future load on the electric utility system which, if implemented in a large scale without control, may lead to grid problems such as power losses and voltage deviations. However, if properly managed, it could increase power plant utilization and reduce the average cost of generating electricity. This paper proposes and analyzes two novel interactive charging strategies for scales of electric vehicles connected in Smart Grid. The first decentralized approach is controlled directly by the electric vehicle charging controller. Six charging modes included in this strategy adjust schedule of electric vehicles charging operation automatically and ensure the charging orderly. The other is centralized charging strategy. With the aim of minimizing power losses and voltage deviations, the optimal algorithm in charging management system also takes into consideration not only the information like current SOC, the battery capacity and connecting time but also the electricity grid constraints. Both of them optimize EVs charging schedules and charging rates. In addition, a two-way communication network based on Zigbee and other communication technology is established to ensure the implement of the interactive charging control. The success of this charging strategy can be an important theoretical meaning and promotional value for EV.

Research of smart charging management system for Electric Vehicles based on wireless communication networks

With the rapid development of Electric Vehicles (EVs), large-scale charging requirement will heavily impact the existing power grid. So there is an urgent need for coordinating control to avoid it in smart grids. Demand side management reducing charging rate at peak load and rescheduling charging process at appropriate time such as low grid load can be used to solve this issue. This paper proposed a coordinating control charging management system for EVs charging station, which is comprised of single-node charging controllers and a centralized management system. Electric Vehicle Charging Controller (EVCC) implemented via PWM signal to control charging current and charging time. As a crucial component of centralized control, Smart Electric Vehicles Charging Management System (EVCMS) obtains charging data and transmits control instructions via ZigBee and GPRS wireless communication networks technology. For calculating the charging current that each EV needs, a model of battery State-Of-Charge (SOC) is included, and each EVCC gets initial battery SOC, battery capacity and other data through CAN-Bus. In the end, the design of ZigBee and GPRS is discussed, and this two-way wireless communication networks is low power consumption, small size, high reliability, and easy expansion.

Research of interactive charging strategy for electrical vehicles in smart grids

In this paper, an interactive charging strategy between Electric Vehicles (EVs) and grid is proposed to minimize the main grid load factor and improve the power quality of the system. This strategy optimizes charging schedule in Electric Vehicles to minimize cost, enhances grid stability, and sets the optimal battery charging rate within the electrical limits of the battery. In this interactive strategy, the paper adopts an interpolated FFT algorithm to measure the power system frequency and fuzzy control technology to realize the battery smart charging. In order to ensure the implement of the coordinated charging control, two-way wireless communication network based on ZigBee and GPRS technology is established. The success of this charging strategy can be an important theoretical meaning and promotional value for EV.

A new SOH prediction model for lithium-ion battery for electric vehicles

State of health (SOH) prediction for lithium-ion battery is a key technology, which is a foundation of ensuring electrical vehicle systems safety and reliability. The paper proposes a novel SOH prediction model of lithium-ion battery based on sample entropy (SampEn). The cell voltage sequence under the HPPC (hybrid pulse power characteristic) profile is used as the input of the proposed model. The prediction model is established by calculating the sample entropy, using Arrhenius formula, optimizing and fitting polynomial. The tests of other five batteries are used to verify the model, with the prediction relative errs within two percent.

Research of bi-directional smart metering system for EV charging station based on ZigBee communication

Smart meter is an essential part of the metering system in electrical vehicle (EV) charging station. In Smart Grid, EVs can work in charging and discharging status. So it is indispensable that smart meter has features of bi-directional metering and communication. This papers proposes a smart metering system (SMS) which is part of the electrical vehicles management system (EVMS). The SMS can bi-directionally exchange measurement information and control commands between SMS and EVMS based on ethernet and ZigBee network. Among the system, smart meter can measure parameters both in the process of grid to vehicle (G2V) and vehicle to grid (V2G).

Research of EV's charging and discharging control considering demand response

Large scale of Electric Vehicles(EVs) connected into the power grid will cause a significant impact on distribution network, such as network loss, voltage drop, etc. To solve the problems, this paper proposes a novel control system for EV charge-discharge response to Demand Side. This system adopts an algorithm based on dq0 coordinate transformation to measure grid frequency, adjusts the charge-discharge behaviors of EV dynamically according to the grid status and ultimately realizes EVs charge-discharge process responded to Demand Side. The simulation and experiment verify that the control system can maintain good accuracy and real-time performance.

Modeling for lithium-ion battery used in electric vehicles

This paper presents a comparative study of three equivalent circuit models for lithium -ion batteries used in electric vehicles(EVs). Model parameters are all obtained by cooperative particle swarm optimization algorithm on the Dynamic Stress Test data, not the traditional hybrid pulse power characteristic (HPPC) test procedure. A 1372s Federal Urban Driving Schedule test are used to validate and compare models performance. Compared with voltage err between simulation result and test data, it is concluded that all the three models can simulate the dynamics of battery well,while the first-order with one-state hysteresis seems to be the best choice for lithium iron phosphate battery used in electric vehicle.

State of Charge estimation of Li-ion battery in EVs based on second-order sliding mode observer

An accurate State of Charge (SoC) estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. A novel approach based on second-order sliding mode observer for battery state of charge (SOC) estimation has been proposed. The Thevenin equivalent circuit model is selected to model the li-ion battery and cooperative particle swarm optimization parameter identification technique is then utilized to estimate the parametersof the battery model. The performances of the algorithm are validated through some experiments and simulations. Validation results show that the proposed SOC estimation algorithm can achieve an acceptable accuracy within the error less than 2%.

Coordinated charging of plug-in electric vehicles in the charging station

This paper presents acoordinated charging strategy applied in large charging stations for PEVs(plug-in electric vehicles)using asmart algorithm, for the purpose of improving operational efficiency and meeting customer needs. We applied PSO (Particle Swarm Optimization) algorithm to solve the problem.Then wesimulated three kind of disorderedchargingin Matlab for comparati ve analysis. At last, simulation outcomes validated the coordinated charging strategy to be effective.

Research of orderly charging control system for Electrical Vehicles based on Zigbee and GPRS networks

China and lots of other countries invested heavily to the Electrical Vehicles (EVs) to reduce the air pollution and save energy. As a result, lots of EV charging stations came out. But this will impact the Power Grid a lot. For example, distribution network circuit overload, voltage drop, loss distribution network increased overload distribution transformers and other issues make the stability decrease. So ordered charging strategy is proposed to solve the problems. In this paper, the ordered charging strategy between Grid and Electrical Vehicles is proposed to minimize the impact on the Grid, reduce the unnecessary construction of generating capacity and Power Grid construction, improve the charging efficiency and minimize the cost for the Electrical Vehicles charging. This makes the electric vehicles and the grid develop coordinately. Two-way wireless communication network based on Zigbee and GPRS technology is established to ensure the implement of the ordered charging strategy.