In this paper,battery aging diversity among independent cells was studied in terms of available capacity degradation.During the aging process of LiFePO_(4)batteries,the phenomenon of aging diversity can be observed.Wh...In this paper,battery aging diversity among independent cells was studied in terms of available capacity degradation.During the aging process of LiFePO_(4)batteries,the phenomenon of aging diversity can be observed.When batteries with same specification were charged and discharged repeatedly under the same working conditions,the available capacity of different cell decreased at different rates along the cycle number.In this study,accelerated aging tests were carried out on multiple new LiFePO_(4)battery samples of different brands.Experimental results show that under the same working conditions,the actual available capacity of all cells decreased as the number of aging cycle increased,but an obvious aging diversity was observed even among different cells of same brand with same specification.This aging diversity was described and analysed in detail,and the common aging features of different cells beneath this aging diversity was explored.Considering this aging diversity,a probability density concept was adopted to estimate battery’s state of health(SOH).With this method,a relationship between battery SOH and its aging feature parameter was established,and a dynamic sliding window optimization technique was designed to ensure the optimal quality of aging feature extraction.Finally,the accuracy of this SOH estimation method was verified by random test.展开更多
Path tracking performed by multiple chassis actuators remains a significant yet challenging issue in intelligent vehicles.This paper presents a robust optimal path tracking control for intelligent vehicles equipped wi...Path tracking performed by multiple chassis actuators remains a significant yet challenging issue in intelligent vehicles.This paper presents a robust optimal path tracking control for intelligent vehicles equipped with a novel wheel module.A wheel module,named corner drive system(CDS),is proposed by integrating drive,brake,steering,and suspension subsystems.In light of the multi-actuator-integrate characteristic of the CDS,a reconfigurable concept is adopted to carry out the vehicle dynamics modeling.To realize an invariance system for both parametric and nonparametric uncertainties,an integral sliding mode control(ISMC)scheme is devised for desired path tracking by combining the advantages of linear quadratic regulator(LQR)in optimization and SMC in robustness.The chattering problem of the ISMC is analyzed and two continuous controllers for chattering elimination are proposed.A control allocation strategy is proposed to dynamically assign the virtual inputs generated by ISMC among four wheels to optimize vehicle stability and minimize energy dissipation.The superiority of the proposed control scheme is demonstrated under different chassis layouts,road friction conditions,and benchmark controllers on a CarSim-based high-fidelity vehicle model with simulation comparison.Subsequently,the Hardware-in-the-Loop(HiL)test is carried out to further evaluate the feasibility and real-time control performance.The quantitative results demonstrate the path tracking performance of the proposed robust optimal controller under both parametric and nonparametric uncertainties.展开更多
Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid h...Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.51877187)the Key Program of University Technology Plan of Hebei Province(Grant No.ZD2017081).
文摘In this paper,battery aging diversity among independent cells was studied in terms of available capacity degradation.During the aging process of LiFePO_(4)batteries,the phenomenon of aging diversity can be observed.When batteries with same specification were charged and discharged repeatedly under the same working conditions,the available capacity of different cell decreased at different rates along the cycle number.In this study,accelerated aging tests were carried out on multiple new LiFePO_(4)battery samples of different brands.Experimental results show that under the same working conditions,the actual available capacity of all cells decreased as the number of aging cycle increased,but an obvious aging diversity was observed even among different cells of same brand with same specification.This aging diversity was described and analysed in detail,and the common aging features of different cells beneath this aging diversity was explored.Considering this aging diversity,a probability density concept was adopted to estimate battery’s state of health(SOH).With this method,a relationship between battery SOH and its aging feature parameter was established,and a dynamic sliding window optimization technique was designed to ensure the optimal quality of aging feature extraction.Finally,the accuracy of this SOH estimation method was verified by random test.
基金supported by the“Beijing Natural Science Foundation”under Grant L233039“Pioneer and Leading Goose R&D Program of Zhejiang”under Grant 2023C01133“Key R&D Program of Ningbo”under Grant 2023Z014.
文摘Path tracking performed by multiple chassis actuators remains a significant yet challenging issue in intelligent vehicles.This paper presents a robust optimal path tracking control for intelligent vehicles equipped with a novel wheel module.A wheel module,named corner drive system(CDS),is proposed by integrating drive,brake,steering,and suspension subsystems.In light of the multi-actuator-integrate characteristic of the CDS,a reconfigurable concept is adopted to carry out the vehicle dynamics modeling.To realize an invariance system for both parametric and nonparametric uncertainties,an integral sliding mode control(ISMC)scheme is devised for desired path tracking by combining the advantages of linear quadratic regulator(LQR)in optimization and SMC in robustness.The chattering problem of the ISMC is analyzed and two continuous controllers for chattering elimination are proposed.A control allocation strategy is proposed to dynamically assign the virtual inputs generated by ISMC among four wheels to optimize vehicle stability and minimize energy dissipation.The superiority of the proposed control scheme is demonstrated under different chassis layouts,road friction conditions,and benchmark controllers on a CarSim-based high-fidelity vehicle model with simulation comparison.Subsequently,the Hardware-in-the-Loop(HiL)test is carried out to further evaluate the feasibility and real-time control performance.The quantitative results demonstrate the path tracking performance of the proposed robust optimal controller under both parametric and nonparametric uncertainties.
基金supported by the National Natural Science Foundation of China(61701270)the Young Doctor Cooperation Foundation of Qilu University of Technology(Shandong Academy of Sciences)(2017BSHZ008)。
文摘Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements.
