The Experts below are selected from a list of 461535 Experts worldwide ranked by ideXlab platform
Qishui Zhong - One of the best experts on this subject based on the ideXlab platform.
-
an soc Estimation Approach based on adaptive sliding mode observer and fractional order equivalent circuit model for lithium ion batteries
Communications in Nonlinear Science and Numerical Simulation, 2015Co-Authors: Fuli Zhong, Hui Li, Shouming Zhong, Qishui ZhongAbstract:Abstract A state of charge (SOC) Estimation Approach based on an adaptive sliding mode observer (SMO) and a fractional order equivalent circuit model (FOECM) for lithium-ion batteries is proposed in this paper. In order to design the adaptive sliding mode observer (SMO) for the SOC Estimation, the state equations based on a FOECM of battery are derived. A new self-adjusting strategy for the observer gains is presented to adjust the observer in the estimating process, which helps to reduce chattering and convergence time. Furthermore, a continuous and smooth function called hyperbolic tangent function is applied to balance the chattering affection and the disturbance. At last, a battery simulation model is established to test the SOC Estimation performance of the designed SMOs, and the results show the proposed Approach is feasible and effective.
Brigitte D'andrea-novel - One of the best experts on this subject based on the ideXlab platform.
-
LPV/H ∞ suspension robust control adaption of the dynamical lateral load transfers based on a differential algebraic Estimation Approach
IFAC-PapersOnLine, 2016Co-Authors: Soheib Fergani, Lghani Menhour, Luc Dugard, Olivier Sename, Brigitte D'andrea-novelAbstract:Novel. LPV/H ∞ suspension robust control adaption of the dynamical lateral load transfers based on a differential algebraic Estimation Approach. Abstract: This paper is concerned with a new global chassis strategy combining the LPV/H ∞ control framework and the differential algebraic Estimation Approach. The main objective is to enhance the vehicle performances by adapting its control to the dynamical lateral load transfers using a very efficient algebraic dynamical behaviour Estimation strategy. Indeed, the lateral load transfers influence considerably the vehicle dynamical behaviour, stability and safety especially in dangerous driving situations. It is important to emphasize that the dynamical load transfers are different from the static ones generated mainly by the bank of the road. The computation of these dynamics must be based on the effective lateral acceleration and roll behaviour of the car. Such effective data cannot be given directly by the hardware sensors (which give correlated measures). The information on the real dynamical lateral load transfers is very important to ensure a good adaptation of the vehicle control and performances to the considered driving situation. A very interesting differential algebraic Estimation Approach allows to provide the effective needed measures for the control strategy using only sensors available on most of commercial cars. It is based on the differential flatness property of nonlinear systems in an algebraic context. Then, thanks to this Estimation Approach, the dynamical lateral load transfers can be calculated and used to adapt the vertical performances of the vehicle using the LPV/H ∞ for suspension systems control. Simulations performed on non linear vehicle models with data collected on a real car are used to validate the proposed Estimation and control Approaches. Results show the efficiency of this vehicle control strategy.
-
LPV/H ∞ suspension robust control adaption of the dynamical lateral load transfers based on a differential algebraic Estimation Approach
2016Co-Authors: Soheib Fergani, Lghani Menhour, Luc Dugard, Olivier Sename, Brigitte D'andrea-novelAbstract:This paper is concerned with a new global chassis strategy combining the LPV/H ∞ control framework and the differential algebraic Estimation Approach. The main objective is to enhance the vehicle performances by adapting its control to the dynamical lateral load transfers using a very efficient algebraic dynamical behaviour Estimation strategy. Indeed, the lateral load transfers influence considerably the vehicle dynamical behaviour, stability and safety especially in dangerous driving situations. It is important to emphasize that the dynamical load transfers are different from the static ones generated mainly by the bank of the road. The computation of these dynamics must be based on the effective lateral acceleration and roll behaviour of the car. Such effective data cannot be given directly by the hardware sensors (which give correlated measures). The information on the real dynamical lateral load transfers is very important to ensure a good adaptation of the vehicle control and performances to the considered driving situation. A very interesting differential algebraic Estimation Approach allows to provide the effective needed measures for the control strategy using only sensors available on most of commercial cars. It is based on the differential flatness property of nonlinear systems in an algebraic context. Then, thanks to this Estimation Approach, the dynamical lateral load transfers can be calculated and used to adapt the vertical performances of the vehicle using the LPV/H ∞ for suspension systems control. Simulations performed on non linear vehicle models with data collected on a real car are used to validate the proposed Estimation and control Approaches. Results show the efficiency of this vehicle control strategy.
-
LPV/H∞ suspension robust control adaption of the dynamical lateral load transfers based on a differential algebraic Estimation Approach
IFAC-PapersOnLine, 2016Co-Authors: Soheib Fergani, Lghani Menhour, Luc Dugard, Olivier Sename, Brigitte D'andrea-novelAbstract:This paper is concerned with a new global chassis strategy combining the LPV/H ∞ control framework and the differential algebraic Estimation Approach. The main objective is to enhance the vehicle performances by adapting its control to the dynamical lateral load transfers using a very efficient algebraic dynamical behaviour Estimation strategy. Indeed, the lateral load transfers influence considerably the vehicle dynamical behaviour, stability and safety especially in dangerous driving situations. It is important to emphasize that the dynamical load transfers are different from the static ones generated mainly by the bank of the road. The computation of these dynamics must be based on the effective lateral acceleration and roll behaviour of the car. Such effective data cannot be given directly by the hardware sensors (which give correlated measures). The information on the real dynamical lateral load transfers is very important to ensure a good adaptation of the vehicle control and performances to the considered driving situation. A very interesting differential algebraic Estimation Approach allows to provide the effective needed measures for the control strategy using only sensors available on most of commercial cars. It is based on the differential flatness property of nonlinear systems in an algebraic context. Then, thanks to this Estimation Approach, the dynamical lateral load transfers can be calculated and used to adapt the vertical performances of the vehicle using the LPV/H ∞ for suspension systems control. Simulations performed on non linear vehicle models with data collected on a real car are used to validate the proposed Estimation and control Approaches. Results show the efficiency of this vehicle control strategy.
Fuli Zhong - One of the best experts on this subject based on the ideXlab platform.
-
an soc Estimation Approach based on adaptive sliding mode observer and fractional order equivalent circuit model for lithium ion batteries
Communications in Nonlinear Science and Numerical Simulation, 2015Co-Authors: Fuli Zhong, Hui Li, Shouming Zhong, Qishui ZhongAbstract:Abstract A state of charge (SOC) Estimation Approach based on an adaptive sliding mode observer (SMO) and a fractional order equivalent circuit model (FOECM) for lithium-ion batteries is proposed in this paper. In order to design the adaptive sliding mode observer (SMO) for the SOC Estimation, the state equations based on a FOECM of battery are derived. A new self-adjusting strategy for the observer gains is presented to adjust the observer in the estimating process, which helps to reduce chattering and convergence time. Furthermore, a continuous and smooth function called hyperbolic tangent function is applied to balance the chattering affection and the disturbance. At last, a battery simulation model is established to test the SOC Estimation performance of the designed SMOs, and the results show the proposed Approach is feasible and effective.
Zhao Zhen-yu - One of the best experts on this subject based on the ideXlab platform.
-
a baseline load Estimation Approach for residential customer based on load pattern clustering
Energy Procedia, 2017Co-Authors: Kangping Li, Zengqiang Mi, Zheng Wang, Bo Wang, Fei Wang, Zhao Zhen-yuAbstract:Abstract Demand response (DR) is a key technology enabling reliable and flexible power system operation more economically and environment-friendly than conventional manners from supply side. Customer baseline load (CBL) Estimation is an important issue in the implementation of DR programs for assessing the performance of DR programs and designing economic compensation mechanisms. The accurate Estimation of CBL is critical to the success of DR programs because it involves the interests of multi-stakeholders including utilities and customers. Motivated by the inaccuracy of existing CBL methods, this paper proposes a residential CBL Estimation Approach based on load pattern (LP) clustering to improve the accuracy of CBL Estimation. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to extract typical load patterns (TLPs) of each individual customer in order to avoid the adverse effects from aggregating many dissimilar LPs together as the real TLP. Second, K-means clustering is utilized to segment residential customers into several different clusters based on the similarity of LPs. Finally, CBLs for DR participants are estimated based on the actual load of non-participants at the same cluster during DR event periods. The proposed methods are compared with some traditional methods on a smart metering dataset from Ireland. The results show that the proposed methods have a better performance on accuracy than averaging and regression methods.
Fei Wang - One of the best experts on this subject based on the ideXlab platform.
-
a baseline load Estimation Approach for residential customer based on load pattern clustering
Energy Procedia, 2017Co-Authors: Kangping Li, Zengqiang Mi, Zheng Wang, Bo Wang, Fei Wang, Zhao Zhen-yuAbstract:Abstract Demand response (DR) is a key technology enabling reliable and flexible power system operation more economically and environment-friendly than conventional manners from supply side. Customer baseline load (CBL) Estimation is an important issue in the implementation of DR programs for assessing the performance of DR programs and designing economic compensation mechanisms. The accurate Estimation of CBL is critical to the success of DR programs because it involves the interests of multi-stakeholders including utilities and customers. Motivated by the inaccuracy of existing CBL methods, this paper proposes a residential CBL Estimation Approach based on load pattern (LP) clustering to improve the accuracy of CBL Estimation. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to extract typical load patterns (TLPs) of each individual customer in order to avoid the adverse effects from aggregating many dissimilar LPs together as the real TLP. Second, K-means clustering is utilized to segment residential customers into several different clusters based on the similarity of LPs. Finally, CBLs for DR participants are estimated based on the actual load of non-participants at the same cluster during DR event periods. The proposed methods are compared with some traditional methods on a smart metering dataset from Ireland. The results show that the proposed methods have a better performance on accuracy than averaging and regression methods.
