Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electro...Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electrochemical energy storage system. A novel failure prediction modeling method of lithium ion battery based on distributed parameter estimation and single particle model is proposed in this work. Lithium ion concentration in the anode of lithium ion battery is an unmeasurable distributed variable. Failure prediction system can estimate lithium ion concentration online, track the failure residual which is the difference between the estimated value and the ideal value. The precaution signal will be triggered when the failure residual is beyond the predefined failure precaution threshold, and the failure countdown prediction module will be activated. The remaining time of the severe failure threshold can be estimated by the failure countdown prediction module according to the changing rate of the failure residual. A simulation example verifies that lithium ion concentration in the anode of lithium ion battery can be estimated exactly and effectively by the failure prediction model. The precaution signal can be triggered reliably, and the remaining time of the severe failure can be forecasted accurately by the failure countdown prediction module.展开更多
With the rapid adoption of electric vehicles(EVs),more charging and battery swapping facilities are needed to meet growing demand.However,a single type of charging or swapping facility cannot simultaneously and effici...With the rapid adoption of electric vehicles(EVs),more charging and battery swapping facilities are needed to meet growing demand.However,a single type of charging or swapping facility cannot simultaneously and efficiently satisfy the power supply requirements of diverse vehicle types.In order to solve this problem,a joint planning method of charging piles and charging-battery swapping stations(CBSSs)is proposed in this paper.In this method,the influence of geospatial constraints on the layout scale of charging piles is considered,and the Monte Carlo simulation method is used to predict the spatiotemporal distribution of charging and battery swapping demands of private electric vehicles(PEVs)and the battery swapping demands of taxi electric vehicles(TEVs)respectively.On this basis,the layout scale of charging piles of each functional area is determined during the maximum charging demand period in a day to meet the demands of PEVs for charging convenience.Then,an operating state model of CBSS is established for calculation of the objective function.At the same time,a planning model of CBSSs is established to minimize the annual social comprehensive cost,which takes into account the economy of CBSSs and the battery swapping convenience of EVs.The planning of CBSSs can meet the demands of TEVs and some PEVs for a rapid power supply.Finally,using the urban transportation network of Changchun and IEEE 33-node system as an example,the planning of charging piles and CCBSs in direct charging mode and peak shifting mode are simulated and analyzed.The simulation results show that the proposed method enables PEVs and TEVs to access convenient and rapid power supply,and the planning result of CBSSs in direct charging mode is more economical,while peak shifting mode is more conducive to the safe operation of distribution networks.展开更多
Voltage imbalance(VI)is caused by the difference in connected single-phase load or generation in a low voltage distribution network(DN).VI increase in a smart distribution grid is due to the current practice of increa...Voltage imbalance(VI)is caused by the difference in connected single-phase load or generation in a low voltage distribution network(DN).VI increase in a smart distribution grid is due to the current practice of increasing single-phase distributed generators such as photovoltaic(PV)systems.This paper proposes a decentralized control method to mitigate VI using distributed batteries included in smart grid interfaced residential PV systems.To mitigate VI using the batteries in this way,five challenges must be overcome,i.e.,equalizing all battery stress currents within the DN,mitigating VI in abnormal conditions such as signal loss among bus controllers,being immune from the distorted feedback measurements,minimizing the steady-state error at different loads,and overcoming the insufficient number or capacity of the distributed batteries at the same bus.Three fuzzy logic controllers(FLC)are proposed at each bus to overcome these five tasks based on a decentralized control scheme.The proposed decentralized control based on FLC is compared with centralized control based on a PI controller.The proposed control method is tested and verified using simulations in the MATLAB/Simulink software,and the results validate the ability of the scheme to alleviate VI on a smart distribution network under both normal and abnormal conditions.展开更多
基金This work was supported by the Fundamental Research Funds for the Central Universities （No.2017JBM003）, the National Natural Science Foundation of China （No.61575053, No.61504008）, and the Specialized Research Fund for the Doctoral Program of Higher Education of China （No.20130009120042）.
文摘Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electrochemical energy storage system. A novel failure prediction modeling method of lithium ion battery based on distributed parameter estimation and single particle model is proposed in this work. Lithium ion concentration in the anode of lithium ion battery is an unmeasurable distributed variable. Failure prediction system can estimate lithium ion concentration online, track the failure residual which is the difference between the estimated value and the ideal value. The precaution signal will be triggered when the failure residual is beyond the predefined failure precaution threshold, and the failure countdown prediction module will be activated. The remaining time of the severe failure threshold can be estimated by the failure countdown prediction module according to the changing rate of the failure residual. A simulation example verifies that lithium ion concentration in the anode of lithium ion battery can be estimated exactly and effectively by the failure prediction model. The precaution signal can be triggered reliably, and the remaining time of the severe failure can be forecasted accurately by the failure countdown prediction module.
基金supported by the Major Science and Technology Special Project of Jilin Province(No.20240204001SF).
文摘With the rapid adoption of electric vehicles(EVs),more charging and battery swapping facilities are needed to meet growing demand.However,a single type of charging or swapping facility cannot simultaneously and efficiently satisfy the power supply requirements of diverse vehicle types.In order to solve this problem,a joint planning method of charging piles and charging-battery swapping stations(CBSSs)is proposed in this paper.In this method,the influence of geospatial constraints on the layout scale of charging piles is considered,and the Monte Carlo simulation method is used to predict the spatiotemporal distribution of charging and battery swapping demands of private electric vehicles(PEVs)and the battery swapping demands of taxi electric vehicles(TEVs)respectively.On this basis,the layout scale of charging piles of each functional area is determined during the maximum charging demand period in a day to meet the demands of PEVs for charging convenience.Then,an operating state model of CBSS is established for calculation of the objective function.At the same time,a planning model of CBSSs is established to minimize the annual social comprehensive cost,which takes into account the economy of CBSSs and the battery swapping convenience of EVs.The planning of CBSSs can meet the demands of TEVs and some PEVs for a rapid power supply.Finally,using the urban transportation network of Changchun and IEEE 33-node system as an example,the planning of charging piles and CCBSs in direct charging mode and peak shifting mode are simulated and analyzed.The simulation results show that the proposed method enables PEVs and TEVs to access convenient and rapid power supply,and the planning result of CBSSs in direct charging mode is more economical,while peak shifting mode is more conducive to the safe operation of distribution networks.
文摘Voltage imbalance(VI)is caused by the difference in connected single-phase load or generation in a low voltage distribution network(DN).VI increase in a smart distribution grid is due to the current practice of increasing single-phase distributed generators such as photovoltaic(PV)systems.This paper proposes a decentralized control method to mitigate VI using distributed batteries included in smart grid interfaced residential PV systems.To mitigate VI using the batteries in this way,five challenges must be overcome,i.e.,equalizing all battery stress currents within the DN,mitigating VI in abnormal conditions such as signal loss among bus controllers,being immune from the distorted feedback measurements,minimizing the steady-state error at different loads,and overcoming the insufficient number or capacity of the distributed batteries at the same bus.Three fuzzy logic controllers(FLC)are proposed at each bus to overcome these five tasks based on a decentralized control scheme.The proposed decentralized control based on FLC is compared with centralized control based on a PI controller.The proposed control method is tested and verified using simulations in the MATLAB/Simulink software,and the results validate the ability of the scheme to alleviate VI on a smart distribution network under both normal and abnormal conditions.
