SoC Evaluation of Power Battery Remaining Capacity Based on Battery Model and Extended Kalman Filter

2014 ◽  
Vol 971-973 ◽  
pp. 1152-1155
Author(s):  
Min Wu ◽  
Hai Pu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Management System ◽  
New Method ◽  
Battery Management System ◽  
Battery Management ◽  
Traditional Methods ◽  
Battery Model ◽  
Power Battery ◽  
Remaining Capacity

The estimate of the remaining capacity of power battery is one of the most important function of battery management system. Using traditional methods is difficult to estamate the remaining capacity, this paper put forward a new method which is based on the battery model and extended Kalman filter.Then carries on the concrete analysis and discussion.

Study on Battery Management System Design of Electric Vehicle and Strategy of the SOC Estimation

2014 ◽  
Vol 945-949 ◽  
pp. 1500-1506
Author(s):  
Zhong An Yu ◽  
Jun Peng Jian
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Digital Signal Processor ◽  
Management System ◽  
Estimation Accuracy ◽  
Space Representation ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Battery Model

In order to improve the efficiency and service life of Lithium batteries for electric vehicle. A structure diagram of battery management system with the digital signal processor as the main controller was designed; in addition, some design modules were expatiated clearly, including the sample circuits of the batterys voltage, current and equalization circuit. The state-space representation of the battery model was established based on Thevenin battery model and extended Kalman filter (EKF) algorithm.According to the estimates and performance characteristics of battery, a new improved-way by amending the Kalman filter gain with the actual situation for raised the SOC estimation accuracy was proposed. The simulation and test results under the condition of simulated driving show that this new way really can increase the SOC accuracy; the equalization scheme can effectively compensate the performance inconsistency of battery pack.

scholarly journals Modeling and Simulation of Battery Management System (BMS) for Electric Vehicles

2021 ◽  
Vol 23 (06) ◽  
pp. 805-815
Author(s):  
Ravi P Bhovi ◽  
◽  
Ranjith A C ◽  
Sachin K M ◽  
Kariyappa B S ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Electric Vehicles ◽  
Management System ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Electric Cars

Electric cars have evolved into a game-changing technology in recent years. A Battery Management System (BMS) is the most significant aspect of an Electric Vehicle (EV) in the automotive sector since it is regarded as the brain of the battery pack. Lithium-ion batteries have a large capacity for energy storage. The BMS is in charge of controlling the battery packs in electric vehicles. The major role of the BMS is to accurately monitor the battery’s status, which assures dependable operation and prolongs battery performance. The BMS’s principal job is to keep track, estimate, and balance the battery pack’s cells. The major goals of this work are to keep track of battery characteristics, estimate SoC using three distinct approaches, and balance cells. Coulomb Counting, Extended Kalman Filter, and Unscented Kalman Filter are the three algorithms that will be implemented. Current is used as an input parameter to implement the coulomb counting method. In contrast to voltage and temperature, the current value is taken into account by the Extended and Unscented Kalman Filters. To calculate the state transition and measurement update matrix, these parameters are considered. This matrix will then be used to calculate SoC. Results of all the algorithms will be comparatively analyzed. MATLAB R2020a software is used for the simulation of different algorithms and SoC calculation. Three states of BMS are considered and they are Discharging phase, the Standby/resting phase, and the Charging phase. At the beginning of the Simulation, the SoC values of the cells were 80%. At the end of simulation maximum values of SoC of Coulomb counting, Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF) reached are 100%, 98.74%, and 98.46% respectively. After SoC Estimation, Cell balancing is also performed over 6 cells of the battery pack.

scholarly journals State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 66 ◽  
Author(s):  
Ning Ding ◽  
Krishnamachar Prasad ◽  
Tek Tjing Lie ◽  
Jinhui Cui
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Open Circuit ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Battery Model ◽  
Operation Conditions ◽  
Battery State Of Charge

The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.

A Battery Management System With a Lebesgue-Sampling-Based Extended Kalman Filter

2019 ◽  
Vol 66 (4) ◽  
pp. 3227-3236 ◽  
Author(s):  
Wuzhao Yan ◽  
Bin Zhang ◽  
Guangquan Zhao ◽  
Shijie Tang ◽  
Guangxing Niu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Management System ◽  
Battery Management System ◽  
Battery Management

Performance Evaluation of an Extended Kalman Filter for State Estimation of a Pseudo-2D Thermal-Electrochemical Lithium-Ion Battery Model

2015 ◽  
Author(s):  
Shi Zhao ◽  
Adrien M. Bizeray ◽  
Stephen R. Duncan ◽  
David A. Howey
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
Low Noise ◽  
Particle Model ◽  
Battery Management System ◽  
Battery Management ◽  
Voltage Measurement

Fast and accurate state estimation is one of the major challenges for designing an advanced battery management system based on high-fidelity physics-based model. This paper evaluates the performance of a modified extended Kalman filter (EKF) for on-line state estimation of a pseudo-2D thermal-electrochemical model of a lithium-ion battery under a highly dynamic load with 16C peak current. The EKF estimation on the full model is shown to be significantly more accurate (< 1% error on SOC) than that on the single-particle model (10% error on SOC). The efficiency of the EKF can be improved by reducing the order of the discretised model while maintaining a high level of accuracy. It is also shown that low noise level in the voltage measurement is critical for accurate state estimation.

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