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. batte...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.展开更多
A modular multilevel converter(MMC)integrated with split battery cells(BIMMCs)is proposed for the battery management system(BMS)and motor drive system.In order to reduce the switching losses,the state of charge(SOC)ba...A modular multilevel converter(MMC)integrated with split battery cells(BIMMCs)is proposed for the battery management system(BMS)and motor drive system.In order to reduce the switching losses,the state of charge(SOC)balancing strategy with a reduced switching-frequency(RSF)is proposed in this paper.The proposed RSF algorithm not only reduces the switching losses,but also features good balancing performance both in the unbalanced and balanced initial states.The results are verified by extensive simulations in MATLAB/Simulink surroundings.展开更多
This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 A...This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.展开更多
To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation alg...To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter(MVEKF)algorithm.Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods,the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter.Experiments including constant pulse discharging and the dynamic stress test(DST)demonstrate that compared with the EKF algorithm,the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5%at most under the complex charging and discharging conditions of batteries.In the charging process under the DST condition,the EKF produces a larger deviation and lacks stability,while the MVEKF algorithm can estimate SOC stably and has a strong robustness.Therefore,the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.展开更多
Battery storage systems are subject to frequent charging/discharging cycles,which reduce the operational life of the battery and reduce system reliability in the long run.As such,several Battery Management Systems(BMS...Battery storage systems are subject to frequent charging/discharging cycles,which reduce the operational life of the battery and reduce system reliability in the long run.As such,several Battery Management Systems(BMS)have been developed to maintain system reliability and extend the battery’s operative life.Accurate estimation of the battery’s State of Charge(SOC)is a key challenge in the BMS due to its non-linear characteristics.This paper presents a comprehensive review on the most recent classifications and mathematical models for SOC estimation.Future trends for SOC estimation methods are also presented.展开更多
The issues of energy shortage and environmental pollution have accelerated the electrification of construction ma-chinery(CM)industry globally.In China,the amount of electric construction machinery(ECM)has been growin...The issues of energy shortage and environmental pollution have accelerated the electrification of construction ma-chinery(CM)industry globally.In China,the amount of electric construction machinery(ECM)has been growing across the industry.The sales of ECM are estimated to reach 600000 vehicles by the end of 2025,while the total demand for battery power will reach 60 GWh.However,the development of ECM still faces critical challenges including reliable power supply and energy distribution among various components.In this review,we primarily focus on important technological breakthroughs and the difficulties faced by the CM industry in China.An overview of ECM including classification and characteristics is given at the beginning.Next,the selection of key components such as the electric motor and the energy storage units,and the control strategy in the pure electric drive system are discussed.The characteristics of the hybrid electric drive system such as structure design and power matching are analyzed in detail.The battery management system(BMS)is critical to ensure appropriate battery health for reliable power supply.Here,we extensively review technical developments in various BMSs.In addition,we roughly estimate the national total of CM emissions and the potential environmental benefits of employing ECMs in China.Finally,we set out future research directions and industrial development of ECM.展开更多
文摘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.
文摘A modular multilevel converter(MMC)integrated with split battery cells(BIMMCs)is proposed for the battery management system(BMS)and motor drive system.In order to reduce the switching losses,the state of charge(SOC)balancing strategy with a reduced switching-frequency(RSF)is proposed in this paper.The proposed RSF algorithm not only reduces the switching losses,but also features good balancing performance both in the unbalanced and balanced initial states.The results are verified by extensive simulations in MATLAB/Simulink surroundings.
文摘This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.
基金The National Natural Science Foundation of China(No.51375086)。
文摘To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter(MVEKF)algorithm.Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods,the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter.Experiments including constant pulse discharging and the dynamic stress test(DST)demonstrate that compared with the EKF algorithm,the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5%at most under the complex charging and discharging conditions of batteries.In the charging process under the DST condition,the EKF produces a larger deviation and lacks stability,while the MVEKF algorithm can estimate SOC stably and has a strong robustness.Therefore,the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.
基金This work was supported by research and innovation management center(RIMC)UNIMAS through Fundamental Research Grant Scheme FRGS/1/2017/TK10/UNIMAS/03/1,Ministry of Higher Education,Malaysia.
文摘Battery storage systems are subject to frequent charging/discharging cycles,which reduce the operational life of the battery and reduce system reliability in the long run.As such,several Battery Management Systems(BMS)have been developed to maintain system reliability and extend the battery’s operative life.Accurate estimation of the battery’s State of Charge(SOC)is a key challenge in the BMS due to its non-linear characteristics.This paper presents a comprehensive review on the most recent classifications and mathematical models for SOC estimation.Future trends for SOC estimation methods are also presented.
基金Project supported by the National Key R&D Program of China(No.2019YFB2004604)the National Natural Science Foundation of China(Nos.52075481 and 52075477)+2 种基金the Zhejiang Provincial Natural Science Foundation of China(No.LR19E050002)the Key R&D Program of Zhejiang Province(No.2020C01152)and the“Innovation 2025”Major Project of Ningbo(No.2020Z110),China。
文摘The issues of energy shortage and environmental pollution have accelerated the electrification of construction ma-chinery(CM)industry globally.In China,the amount of electric construction machinery(ECM)has been growing across the industry.The sales of ECM are estimated to reach 600000 vehicles by the end of 2025,while the total demand for battery power will reach 60 GWh.However,the development of ECM still faces critical challenges including reliable power supply and energy distribution among various components.In this review,we primarily focus on important technological breakthroughs and the difficulties faced by the CM industry in China.An overview of ECM including classification and characteristics is given at the beginning.Next,the selection of key components such as the electric motor and the energy storage units,and the control strategy in the pure electric drive system are discussed.The characteristics of the hybrid electric drive system such as structure design and power matching are analyzed in detail.The battery management system(BMS)is critical to ensure appropriate battery health for reliable power supply.Here,we extensively review technical developments in various BMSs.In addition,we roughly estimate the national total of CM emissions and the potential environmental benefits of employing ECMs in China.Finally,we set out future research directions and industrial development of ECM.
