Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Management of Charging Load of Electric Vehicles for Optimal Capacity Utilisation of Distribution Transformers

A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours and 06:00 hours of the next day and the Uncontrolled Charging Regime (UCR) between 06:00 hours and 16:30 hours of the same day. During the CCR, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The EVLC sorts the EVs batteries in ascending order of their states of charge (SoC) and sends command signals for charging to as many EVs as the transformer could allow at that interval based on the condition of the transformer as analysed by the Distribution Transformer Monitor (DTM). A real and typical urban LV area distribution network in Great Britain (GB) is used as the case study. The technique is applied on the LV area when its transformer is carrying the future load demand of the area on a typical winter weekday in the year 2050. To achieve the load management, load demand of the LV area network is decomposed into Non-EV load and EV load. The load on the transformer is managed by varying the EV load in an optimisation objective function which maximises the capacity utilisation of the transformer subject to operational constraints and non-disruption of daily trips of EV owners. Results show that with the proposed load management technique, LV distribution networks could accommodate high uptake of EVs without compromising the useful normal life expectancy of distribution transformers before the need for capacity reinforcement.

Battery Management System with State ofCharge Indicator for Electric Vehicles

Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.

A new state of charge determination method for battery management system

State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of over-voltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery management system by combining Ah-balance with an EMF estimation algorithm, which predicts the battery’s EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.

Communication-less Management Strategy for Electric Vehicle Charging in Droop-controlled Islanded Microgrids

Adopting high penetration levels of electric vehicles(EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links. Such techniques are far from realization due to(1) the lack of utility-grade communication systems in many cases such as secondary(low-voltage) power distribution systems to which EVs are connected, rural areas, remote communities, and islands, and(2) existing fears and concerns about the data privacy of EV users and cyber-physical security. For these cases, appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements. Accordingly, this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded microgrids. The proposed strategy is autonomous, as it is based on the measurement of system frequency and local bus voltages. The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed generators(DGs) are in short supply by allocating more system capacity to the EVs with less charging in the past. Furthermore, a novel communication-less EV load shedding scheme is incorporated into the management strategy to provide relief to the microgrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages. Numerical simulations demonstrate the superiority of the proposed strategy over the state-of-the-art controllers in modulating the EV charging demand to counteract microgrid instability.

Application of Power Electronics and Control for Dual Battery Packs Management with Voltage Balancing and State of Charge Estimation

Energy storage, such as lead acid batteries, is necessary for renewable energy sources’ autonomy because of their intermittent nature, which makes them more frequently used than traditional energy sources to reduce operating costs. The battery storage system has to be monitored and managed to prevent serious problems such as battery overcharging, over-discharging, overheating, battery unbalancing, thermal runaway, and fire dangers. For voltage balancing between batteries in the pack throughout the charging period and the SOC estimate, a modified lossless switching mechanism is used in this research’s suggested battery management system. The OCV state of charge calculation, in the beginning, was used in conjunction with the coulomb counting approach to estimate the SOC. The results reveal that correlation factor K has an average value of 0.3 volts when VM ≥ 12 V and an average value of 0.825 when VM ≤ 12 V. The battery monitoring system revealed that voltage balancing was accomplished during the charging process in park one after 80 seconds with a SOC difference of 1.4% between Batteries 1 and 2. On the other hand, the system estimates the state of charge during the discharging process in two packs, with a maximum DOD of 10.8 V for all batteries. The project’s objectives were met since the BMS estimated SOC and achieved voltage balance.

"Emperor's Power Far Away from the Countryside"and"Town in Charge of Village Affairs":The Extreme Grass-roots Management System is Not Conducive to the Healthy Development of the Countryside

This paper compares the two different rural management methods of"emperor’s power far away from the countryside"and"town in charge of village affairs",which shows that the extreme grass-roots management system is not conducive to rural development.This paper also points out that rural development needs to find a road of sustainable development in line with its own characteristics,which is the fundamental shortcut to change poverty and become rich for a long time.

Power management unit chip design for automobile active-matrix organic light-emitting diode display module

A power management unit （PMU） chip supplying dual panel supply voltage, which has a low electro-magnetic interference （EMI） characteristic and is favorable for miniaturization, is designed. A two-phase charge pump circuit using external pumping capacitor increases its pumping current and works out the charge-loss problem by using bulk-potential biasing circuit. A low-power start-up circuit is also proposed to reduce the power consumption of the band-gap reference voltage generator. And the ring oscillator used in the ELVSS power circuit is designed with logic devices by supplying the logic power supply to reduce the layout area. The PMU chip is designed with MagnaChip＇s 0.25 μ high-voltage process. The driving currents of ELVDD and ELVSS are more than 50 mA when a SPICE simulation is done.

Surface Reactivity in Tropical Highly Weathered Soils and Implications for Rational Soil Management

Highly weathered soils are distributed in the humid and wet-dry tropics, as well as in the humid subtropics. As a result of strong weathering, these soils are characterized by low activity clays, which develop variable surface charge and related specific properties. Surface reactions regarding base exchange and soil acidification, heavy metal sorption and mobility, and phosphorus sorption and availability of the tropical highly weathered soils are reviewed in this paper.Factors controlling surface reactivity towards cations and anions, including ion exchange and specific adsorption processes, are discussed with consideration on practical implications for rational management of these soils. Organic matter content and pH value are major basic factors that should be controlled through appropriate agricultural practices, in order to optimise favorable effects of colloid surface properties on soil fertility and environmental quality.

An Optimal Management System of a Wind Energy Supplier

In this paper, we propose a real time approach to optimize the supplied energy of a wind station. This station is used for electric energy storage in battery bank, water pumping, lighting and heating systems. The characterization of the wind generator allows us to estimate the available electrical wind energy of the platform. A data sheet of the required energy for each load (battery charging, wind pumping system, lighting system and heating system) is drawn up. Basing on the issued data sheet we develop and implement a management system for optimal energy distribution. To improve the proposed management system, we introduce a real time data acquisition (DAQ-S) and data processing (DP-S) subsystems. The DAQ-S measures all the required electrical parameters needed by the proposed DP-S to perform an optimal algorithm activating our smart decision system to allow a rational and effective use of the electrical wind power.

Deep learning-based battery state of charge estimation:Enhancing estimation performance with unlabelled training samples

The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.

Impact Analysis of EV Charging with Mixed Control Strategy

EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the high adoption of EVs will impose a burden on the power system, especially for neighborhood level network. In this paper, we propose a mixed control framework for EV charging scheduling to mitigate its impact on the power network. A metric for modeling customer＇s satisfaction is also proposed to compare the user satisfaction for different algorithms. The impacts of the proposed algorithms on EV charging cost, EV penetration and peak power reduction are evaluated with real data for a neighborhood level network. The simulation results demonstrate the effectiveness of the proposed algorithms.

Research on Fuzzy PID Charging Control of Battery Pack

Battery groups are widely used in production and life. Optimal charging can not only shorten the charge time, but also improve the performance and life of the battery pack. A constant current or constant voltage charging method is commonly used. This type of method cannot adjust the charge capacity in time according to the change of charging capacity of storage battery, and the charge performance is not high. This paper designs a fuzzy PID controller. In the case of variable load and interference, the battery group can still be charged by the optimal charging current. Through the simulation results, the fuzzy PID controller works well and verifies the feasibility of the charging controller.

Equalization Charging and Protection System for Electric Vehicle

A system is developed to improve the series battery packs uniformities and charging protection and the implementation of battery equalization charging and protection system is also introduced. The functions of equalization charging and overcharging protection are analyzed and the control model of series battery packs equalization charging is setup. The diverting-current and feedback bus voltage are measured during the series Li-ion battery packs equalization charging experiment. The field operation on Electric luxury transit bus BFC6100EV shows that the system betters the battery series charging uniformities and overcharging protection, improves the battery performance and extends the battery life.

State of Health Estimation of LiFePO_(4) Batteries for Battery Management Systems

When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

Regional anesthesia for acute pain management in elderly patients

Normal aging is a process that involves loss of functional reserve of most organ systems of the human body, most significantly: cardiovascular, pulmonary, renal and nervous systems. Advancements in both surgery and anesthesia have made it possible to operate more safely on the elderly population and those older patients with multiple severe co-morbidities that were not routinely possible in the recent past. Regional anesthesiologist have proven to be instrumental in this regard as regional anesthetic/analgesic techniques may now permit surgeons to operate on the elderly who were not ideal surgical candidates or unable to tolerate general anesthesia. In addition, regional techniques provide alternatives that may optimize acute pain control and reduce the incidence of devastating side effects during the perioperative period such as: myocardial infarction, pulmonary embolism, pneumonia, and also increases the opportunity to allow for early ambulation and shorter hospital stays. These anesthetic options now provide the elderly patient with better medical care alternatives, but also can show a significant financial impact on health care system resources. Further understanding on aging molecular biology, physiology and pathophysiology, together with technical improvements of regional anesthetic techniques will continue to make it safer and more efficacious to operate on the elderly population with evidence of reduced morbidity and mortality. Although there is only anecdotal evidence that regional anesthesia(RA) improves survival, there is little doubt that RA plays an important role in perioperative optimization of pain control and decreases pain management complications as well as a reduction in healthcare costs. Beyond traditional operating rooms, elderly patients may increasingly benefit from RA and acute pain management in Emergency Rooms, medical clinics and even within a patient's home. Therefore, the focus of this review is directed toward geriatric patients and beneficial effects of RA on outcomes in the elderly.

Evaluation of a PV Powered EV Charging Station and Its Buffer Battery

This research analyses the operation of a solar PV powered electric vehicle charging station with energy storage, that has been developed and demonstrated at the University of California--Davis, West Village, the largest planned zero-energy consumption community in the U.S. The intelligent energy management approach introduces solar PV electrical energy forecasting and EV （electric vehicle） charging demand projection to optimize the SOC （state of charge） of the buffer battery. The charging station has been operated continuously and routinely used by several EV users for a year. The actual operation shows that, a workplace charging station equipped with a buffer battery and with intelligent energy management can lower and reduce the station＇s peak power demand, and reduce the energy exchange with the utility grid by a factor of 2. The battery recharging power demand was shifted away from the on-peak time periods to the off-peak time periods, which will benefit the charging station owner from less energy use during peak periods when time-of-use rates are higher. The standard cell voltage deviation of the 220 cells was calculated to analyse the battery cell consistency during the resting, charging and discharging periods. The analysis shows that, the 220 50Ah cells show excellent voltage consistency with voltage deviation of less than 0.005 V within the battery SOC of 20%-80%. The voltage deviation doubles when the battery SOC reaches 90%. The comparison of cell voltage deviation at the beginning and after one year operation indicates that, the battery shows perfect cell voltage consistency and there is no obvious consistency deterioration during the battery resting, charging and discharging periods.

Model-Based Quantification of Load Shift Potentials and Optimized Charging of Electric Vehicles

Managing the charging process of a large number of electric vehicles to decrease the pressure on the local electricity grid is of high interest to the utilities. Using efficient mathematical optimization techniques, the charging behavior of electric vehicles shall be optimally controlled taking into account network, vehicle, and customer requirements. We developed an efficient algorithm for calculating load shift potentials defined as the range of all charging curves meeting the customer’s requirements and respecting all individual charging and discharging constraints over time. In addition, we formulated a mixed integer linear program (MIP) applying semi-continuous variables to find cost-optimal load curves for every vehicle participating in a load shift. This problem can be solved by e.g. branch-and-bound algorithms. Results of two scenarios of Germany in 2015 and 2030 based on mobility studies show that the load shifting potential of EV is significant and contribute to a necessary relaxation of the future grid. The maximum charging and discharging power and the average battery capacity are crucial to the overall load shift potential.

燃料电池增程式动力系统能量管理策略研究

以某款纯电动客车为研究对象,以增加车辆续驶里程为目的,提出燃料电池增程式混合动力系统结构,根据性能指标对动力系统各部件进行匹配计算和选型.提出了开关/功率跟随式能量管理策略,基于Cruise和Simulink分别搭建了整车动力系统模型和燃料电池及能量管理策略模型并进行联合仿真.结果表明,采用文中提出的能量管理策略车辆经济性相对于开关式和能量跟随式两种控制策略分别提高62%和31%,续驶里程相对于该两种控制策略分别提高41%和18%.