Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles

Sun, Fuchun; Hu, Xiaosong; Zou, Yuan; Li, Siguang

doi:10.1016/j.energy.2011.03.059

Cited by 508 publications

(186 citation statements)

References 37 publications

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“…Battery pack cells imbalance is a vital issue in the battery system life. The BMS performs several tasks such as measuring the system VIT, the cells' SoC, SoH, and RUL estimation as [50][51][52][53][54], protecting the cells, thermal management, controlling the charge/discharge procedure, data acquisition, communication with on-board/off-board modules, monitoring, storing historical data and most important task is the cell balancing [1].…”

Section: Bmss Architecture and Tasksmentioning

confidence: 99%

Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery

et al. 2014

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Lithium-based batteries are considered as the most advanced batteries technology, which can be designed for high energy or high power storage systems. However, the battery cells are never fully identical due to the fabrication process, surrounding environment factors and differences between the cells tend to grow if no measures are taken. In order to have a high performance battery system, the battery cells should be continuously balanced for maintain the variation between the cells as small as possible. Without an appropriate balancing system, the individual cell voltages will differ over time and battery system capacity will decrease quickly. These issues will limit the electric range of the electric vehicle (EV) and some cells will undergo higher stress, whereby the cycle life of these cells will be shorter. Quite a lot of cell balancing/equalization topologies have been previously proposed. These balancing topologies can be categorized into passive and active balancing. Active topologies are categorized according to the active element used for storing the energy such as capacitor and/or inductive component as well as controlling switches or converters. This paper proposes an intelligent battery management system (BMS) including a battery pack charging and discharging control with a battery pack thermal management system. The BMS user input/output interfacing. The battery balancing system is based on battery pack modularization architecture. The proposed modularized balancing system has different equalization systems that operate inside and outside the modules. Innovative single switched capacitor (SSC) control strategy is proposed to balance between OPEN ACCESSEnergies 2014, 7 2898 the battery cells in the module (inside module balancing, IMB). Novel utilization of isolated bidirectional DC/DC converter (IBC) is proposed to balance between the modules with the aid of the EV auxiliary battery (AB). Finally an experimental step-up has been implemented for the validation of the proposed balancing system.

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Section: Bmss Architecture and Tasksmentioning

confidence: 99%

Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery

et al. 2014

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“…This method usually performs SOC estimation based on an equivalent circuit battery model [11,12]. Many different state filtering methods have been investigated, such as extended Kalman filter (EKF) [13][14][15], sigma point Kalman filter (SPKF) [16][17][18], adaptive extended Kalman filter (AEKF) [19], adaptive unscented Kalman filter (AUKF) [20], particle filter (PF) [21] and others [22][23][24][25][26]. Plett [13][14][15][16][17] established the EKF and UKF based SOC estimation methods using different orders of equivalent circuit battery models for simultaneous state and parameters estimation of LiPB packs.…”

Section: Introductionmentioning

confidence: 99%

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Hua

et al. 2015

Energies

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Estimation of state of charge (SOC) is of great importance for lithium-ion (Li-ion) batteries used in electric vehicles. This paper presents a state of charge estimation method using nonlinear predictive filter (NPF) and evaluates the proposed method on the lithium-ion batteries with different chemistries. Contrary to most conventional filters which usually assume a zero mean white Gaussian process noise, the advantage of NPF is that the process noise in NPF is treated as an unknown model error and determined as a part of the solution without any prior assumption, and it can take any statistical distribution form, which improves the estimation accuracy. In consideration of the model accuracy and computational complexity, a first-order equivalent circuit model is applied to characterize the battery behavior. The experimental test is conducted on the LiCoO2 and LiFePO4 battery cells to validate the proposed method. The results show that the NPF method is able to accurately estimate the battery SOC and has good robust performance to the different initial states for both cells. Furthermore, the comparison study between NPF and well-established extended Kalman filter for battery SOC estimation indicates that the proposed NPF method has better estimation accuracy and converges faster.

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“…Therefore, adaptively updating rules for covariance values of the process and measurement noise are required in order to improve performance of the algorithm. In this paper, the idea of covariance matching based on the residual sequence of battery model output voltage proposed in [31,53] [ ]…”

Section: Adaptive Cubature Kalman Filter For Soc Estimationmentioning

confidence: 99%

“…Thus, modified KF algorithms have to be used in order to extend the application of KF in the nonlinear battery systems. Two commonly used types are extended Kalman filter (EKF) [15][16][17][18][19][20][21][22][23][24][25][26][27][28] and unscented Kalman filter (UKF) [29][30][31][32][33][34][35][36]. The EKF transforms a nonlinear system into a linear system by linearizing the nonlinear function on the basis of the first-order Taylor series expansion.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

et al. 2015

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Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms. OPEN ACCESSEnergies 2015, 8 5917

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Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles

Cited by 508 publications

References 37 publications

Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery

Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

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