Battery energy storage systems(ESS) have been widely used in mobile base stations(BS) as the main backup power source. Due to the large number of base stations, massive distributed ESSs have largely stayed in idle and...Battery energy storage systems(ESS) have been widely used in mobile base stations(BS) as the main backup power source. Due to the large number of base stations, massive distributed ESSs have largely stayed in idle and very difficult to achieve high asset utilization. In recent years, the fast-paced development of digital energy storage(DES) technology has revolutionized the traditional operation and maintenance of ESSs by transforming them into digital assets, further enabling battery energy storage services, raising up a new way to achieve a much higher utilization of such kind of largely idle ESS resources. In this paper, the disruptive DES technology will be introduced and its application under the context of mobile BSs will be studied, and then a cloud-based energy storage(CES) platform is proposed based on a large scale distributed DESs to provide a new cyber-enabled energy storage service to the local utility company. A real-world case study shows the effectiveness and efficiency of the CES platform.展开更多
A metal electrode is a significant component of a zinc–air battery(ZAB),but the metal material is usually not elastic,which severely restricts the application of flexible and stretchable ZABs in the field of wearable...A metal electrode is a significant component of a zinc–air battery(ZAB),but the metal material is usually not elastic,which severely restricts the application of flexible and stretchable ZABs in the field of wearable electronic devices.Herein,we report a flexible and stretchable metal-coated textile prepared by a dynamic stretching–electroplating based on a wavy spandex textile substrate.Benefiting from the unique woven and wavy structure,the metal-coated textile shows a high stretchability of 100%and stable conductivity.In situ scanning electron microscope observation during stretching showed that the tensile strain of the metal-coated textile is mainly attributed to the deformation of the microfiber network at the bottom position of the wave structure.In addition,a sodium carboxymethyl cellulose–polyacrylic acid–potassium hydroxide composite hydrogel has been used as the electrolyte.This hydrogel shows excellent ionic conductivity,mechanical properties,and water retention properties,which makes it suitable for the semi-open system of ZAB.Furthermore,a flexible and stretchable sandwich-structure ZAB,assembled using the above-mentioned electrodes and electrolyte,operates stably even under rapid stretching/releasing cycle deformation.Because of its facile preparation and low cost,this flexible and stretchable ZAB is suitable for fabrication of large-area batteries to obtain higher output current and power to drive wearable electronic devices.展开更多
An electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles （HEV） is presented. The model has two RC networks characterizing battery activation and concentration polarization p...An electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles （HEV） is presented. The model has two RC networks characterizing battery activation and concentration polarization process. The parameters of the model are identified using combined experimental and extended Kalman filter （EKF） recursive methods. The open-circuit voltage and ohmic resistance of the battery are directly measured and calculated from experimental measurements, respectively. The rest of the coupled dynamic parameters, i.e. the RC network parameters, are estimated using the EKF method. Experimental and simulation results are presented to demonstrate the efficacy of the proposed circuit model and parameter identification techniques for simulating battery dynamics.展开更多
Statistical Energy Analysis(SEA) is one of the conventional tools for predicting vehicle high-frequency acoustic responses.This study proposes a new method that can provide customized optimization solutions to meet NV...Statistical Energy Analysis(SEA) is one of the conventional tools for predicting vehicle high-frequency acoustic responses.This study proposes a new method that can provide customized optimization solutions to meet NVH targets based on the specific needs of different project teams during the initial project stages.This approach innovatively integrates dynamic optimization,Radial Basis Function(RBF),and Fuzzy Design Variables Genetic Algorithm(FDVGA) into the optimization process of Statistical Energy Analysis(SEA),and also takes vehicle sheet metal into account in the optimization of sound packages.In the implementation process,a correlation model is established through Python scripts to link material density with acoustic parameters,weight,and cost.By combining Optimus and VaOne software,an optimization design workflow is constructed and the optimization design process is successfully executed.Under various constraints related to acoustic performance,weight and cost,a globally optimal design is achieved.This technology has been effectively applied in the field of Battery Electric Vehicle(BEV).展开更多
Data-Driven approaches for State of Charge(SOC)prediction have been developed considerably in recent years.However,determining the appropriate training dataset is still a challenge for model development and validation...Data-Driven approaches for State of Charge(SOC)prediction have been developed considerably in recent years.However,determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material,types of battery cells,and operation conditions.This work focuses on optimization of the training data set by using simple measurable data sets,which is important for the accuracy of predictions,reduction of training time,and application to online esti-mation.It is found that a randomly generated data set can be effectively used for the training data set,which is not necessarily the same format as conventional predefined battery testing protocols,such as constant current cycling,Highway Fuel Economy Cycle,and Urban Dynamometer Driving Schedule.The randomly generated data can be successfully applied to various dynamic battery operating conditions.For the ML algorithm,XGBoost is used,along with Random Forest,Artificial Neural Network,and a reduced-order physical battery model for comparison.The XGBoost method with the optimal training data set shows excellent performance for SOC prediction with the fastest learning time within 1 s,a short running time of 0.03 s,and accurate results with a 0.358%Mean Absolute Percentage Error,which is outstanding compared to other Data-Driven approaches and the physics-based model.展开更多
Dynamic voltage scaling （DVS） is an efficient approach to maximize the battery life of portable devices. A novel overall planning strategy （OPS II） balancing slack supply and demand for DVS is proposed. An OPS II-...Dynamic voltage scaling （DVS） is an efficient approach to maximize the battery life of portable devices. A novel overall planning strategy （OPS II） balancing slack supply and demand for DVS is proposed. An OPS II-based slack-nibbling overall planning strategy （SNOPS） algorithm is also proposed, which iteratively nibbles slacks for appropriate tasks selected by an overall planning dynamic priority function to perform DVS until the slack is exhausted and an optimum voltage setting is obtained. For a high-load task set, SNOPS manages to recover battery overload while maintaining schedulability. For random variable-load task sets, SNOPS achieves a saving of 29.51% battery capacity on average, the suboptimal gap is 27.84% narrower than that of our previously proposed OPS-based algorithm, and 92.10% narrower than that of the algorithm proposed by Chowdhury et al. Results indicate that OPS n manages to save battery to various extents while maintaining schedulability, and demonstrates good load compatibility and close-to-optimal performance on average.展开更多
To minimize battery consumption for portable devices, the prescheduling policy of battery-aware scheduling was improved by optimizing slack distribution. A battery-aware compound task scheduling （BACTS） algorithm co...To minimize battery consumption for portable devices, the prescheduling policy of battery-aware scheduling was improved by optimizing slack distribution. A battery-aware compound task scheduling （BACTS） algorithm considering various aspects including task deadline, current and execution time was proposed and evaluated with the previously prevailing earliest deadline first （EDF） algorithm. The results indicate the proposed BACTS algorithm manages to figure out a feasible schedule （if available） in battery-aware task scheduling even for disorganized connected task graphs beyond the solving ability of EDF. Its schedule achieves better performance with lower charge consumption after prescheduling, and also lower or equal optimum charge consumption after voltage scaling.展开更多
The main purpose of this paper is to design and model a water-pumping system using a submersible multi-stage centrifugal pump driven by a three-phase induction motor. The system is intended for pumping water to the su...The main purpose of this paper is to design and model a water-pumping system using a submersible multi-stage centrifugal pump driven by a three-phase induction motor. The system is intended for pumping water to the surface from a deep well using three power supply systems: a general network, a photo-voltaic (PV) system, and a PV system with a battery bank. These systems are used to compare two three-phase induction motors—namely, a motor with a drive and another one without a drive. The systems dynamic models are simulated in MATLAB/Simulink and the results compared with the manufacturer’s data for validation purposes. The simulation results generally show system dynamics and expected performance over a range of operation.展开更多
基金partly supported by the National Key R&D Program of China under the granted No. 2018YFC1902202.
文摘Battery energy storage systems(ESS) have been widely used in mobile base stations(BS) as the main backup power source. Due to the large number of base stations, massive distributed ESSs have largely stayed in idle and very difficult to achieve high asset utilization. In recent years, the fast-paced development of digital energy storage(DES) technology has revolutionized the traditional operation and maintenance of ESSs by transforming them into digital assets, further enabling battery energy storage services, raising up a new way to achieve a much higher utilization of such kind of largely idle ESS resources. In this paper, the disruptive DES technology will be introduced and its application under the context of mobile BSs will be studied, and then a cloud-based energy storage(CES) platform is proposed based on a large scale distributed DESs to provide a new cyber-enabled energy storage service to the local utility company. A real-world case study shows the effectiveness and efficiency of the CES platform.
基金National Natural Science Foundation of China and Guangdong Province,Grant/Award Number:U1601216National Natural Science Foundation for Excellent Young Scholar,Grant/Award Number:51722403+3 种基金National Youth Talent Support Program“131”First Level Innovative Talents Training Project in TianjinNational Natural Science Foundation for Distinguished Young Scholar,Grant/Award Number:52125404Tianjin Natural Science Foundation for Distinguished Young Scholar,Grant/Award Number:18JCJQJC46500。
文摘A metal electrode is a significant component of a zinc–air battery(ZAB),but the metal material is usually not elastic,which severely restricts the application of flexible and stretchable ZABs in the field of wearable electronic devices.Herein,we report a flexible and stretchable metal-coated textile prepared by a dynamic stretching–electroplating based on a wavy spandex textile substrate.Benefiting from the unique woven and wavy structure,the metal-coated textile shows a high stretchability of 100%and stable conductivity.In situ scanning electron microscope observation during stretching showed that the tensile strain of the metal-coated textile is mainly attributed to the deformation of the microfiber network at the bottom position of the wave structure.In addition,a sodium carboxymethyl cellulose–polyacrylic acid–potassium hydroxide composite hydrogel has been used as the electrolyte.This hydrogel shows excellent ionic conductivity,mechanical properties,and water retention properties,which makes it suitable for the semi-open system of ZAB.Furthermore,a flexible and stretchable sandwich-structure ZAB,assembled using the above-mentioned electrodes and electrolyte,operates stably even under rapid stretching/releasing cycle deformation.Because of its facile preparation and low cost,this flexible and stretchable ZAB is suitable for fabrication of large-area batteries to obtain higher output current and power to drive wearable electronic devices.
文摘An electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles （HEV） is presented. The model has two RC networks characterizing battery activation and concentration polarization process. The parameters of the model are identified using combined experimental and extended Kalman filter （EKF） recursive methods. The open-circuit voltage and ohmic resistance of the battery are directly measured and calculated from experimental measurements, respectively. The rest of the coupled dynamic parameters, i.e. the RC network parameters, are estimated using the EKF method. Experimental and simulation results are presented to demonstrate the efficacy of the proposed circuit model and parameter identification techniques for simulating battery dynamics.
文摘Statistical Energy Analysis(SEA) is one of the conventional tools for predicting vehicle high-frequency acoustic responses.This study proposes a new method that can provide customized optimization solutions to meet NVH targets based on the specific needs of different project teams during the initial project stages.This approach innovatively integrates dynamic optimization,Radial Basis Function(RBF),and Fuzzy Design Variables Genetic Algorithm(FDVGA) into the optimization process of Statistical Energy Analysis(SEA),and also takes vehicle sheet metal into account in the optimization of sound packages.In the implementation process,a correlation model is established through Python scripts to link material density with acoustic parameters,weight,and cost.By combining Optimus and VaOne software,an optimization design workflow is constructed and the optimization design process is successfully executed.Under various constraints related to acoustic performance,weight and cost,a globally optimal design is achieved.This technology has been effectively applied in the field of Battery Electric Vehicle(BEV).
基金The authors gratefully acknowledge financial support from the National Science Foundation(Award Nos.1538415 and 1610396)。
文摘Data-Driven approaches for State of Charge(SOC)prediction have been developed considerably in recent years.However,determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material,types of battery cells,and operation conditions.This work focuses on optimization of the training data set by using simple measurable data sets,which is important for the accuracy of predictions,reduction of training time,and application to online esti-mation.It is found that a randomly generated data set can be effectively used for the training data set,which is not necessarily the same format as conventional predefined battery testing protocols,such as constant current cycling,Highway Fuel Economy Cycle,and Urban Dynamometer Driving Schedule.The randomly generated data can be successfully applied to various dynamic battery operating conditions.For the ML algorithm,XGBoost is used,along with Random Forest,Artificial Neural Network,and a reduced-order physical battery model for comparison.The XGBoost method with the optimal training data set shows excellent performance for SOC prediction with the fastest learning time within 1 s,a short running time of 0.03 s,and accurate results with a 0.358%Mean Absolute Percentage Error,which is outstanding compared to other Data-Driven approaches and the physics-based model.
基金Supported by the National High Technology Research and Development Program of China (863 Program) (2002AA1Z1490)the Spe-cialized Research Fund for the Doctoral Program of Higher Education of China (20040486049)
文摘Dynamic voltage scaling （DVS） is an efficient approach to maximize the battery life of portable devices. A novel overall planning strategy （OPS II） balancing slack supply and demand for DVS is proposed. An OPS II-based slack-nibbling overall planning strategy （SNOPS） algorithm is also proposed, which iteratively nibbles slacks for appropriate tasks selected by an overall planning dynamic priority function to perform DVS until the slack is exhausted and an optimum voltage setting is obtained. For a high-load task set, SNOPS manages to recover battery overload while maintaining schedulability. For random variable-load task sets, SNOPS achieves a saving of 29.51% battery capacity on average, the suboptimal gap is 27.84% narrower than that of our previously proposed OPS-based algorithm, and 92.10% narrower than that of the algorithm proposed by Chowdhury et al. Results indicate that OPS n manages to save battery to various extents while maintaining schedulability, and demonstrates good load compatibility and close-to-optimal performance on average.
基金Supported by the National High Technology Research and Development Program of China (863 Program) (2002AA1Z1490)the Spe-cialized Research Fund for the Doctoral Program of Higher Education of China (20040486049)
文摘To minimize battery consumption for portable devices, the prescheduling policy of battery-aware scheduling was improved by optimizing slack distribution. A battery-aware compound task scheduling （BACTS） algorithm considering various aspects including task deadline, current and execution time was proposed and evaluated with the previously prevailing earliest deadline first （EDF） algorithm. The results indicate the proposed BACTS algorithm manages to figure out a feasible schedule （if available） in battery-aware task scheduling even for disorganized connected task graphs beyond the solving ability of EDF. Its schedule achieves better performance with lower charge consumption after prescheduling, and also lower or equal optimum charge consumption after voltage scaling.
文摘The main purpose of this paper is to design and model a water-pumping system using a submersible multi-stage centrifugal pump driven by a three-phase induction motor. The system is intended for pumping water to the surface from a deep well using three power supply systems: a general network, a photo-voltaic (PV) system, and a PV system with a battery bank. These systems are used to compare two three-phase induction motors—namely, a motor with a drive and another one without a drive. The systems dynamic models are simulated in MATLAB/Simulink and the results compared with the manufacturer’s data for validation purposes. The simulation results generally show system dynamics and expected performance over a range of operation.
