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.

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.

State of Energy Estimation Based on AUKF for Lithium Battery Used on Pure Electric Vehicle

2012 ◽  
Vol 608-609 ◽  
pp. 1627-1630
Author(s):  
Hong Wei Liu ◽  
Hai Feng Wang ◽  
Chong Guo
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Unscented Kalman Filter ◽  
The State ◽  
Estimation Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Process Noise ◽  
Energy Estimation ◽  
Kalman Filter Algorithm

State of Energy can be used to predict the driving mileage of electric vehicles, design the control strategy of vehicle energy distribution, and improve the safety of electric vehicle. Accurate estimaion of state of energy is one of the key technologies in the study on battery management system of electric vehicle. In this paper, the State of Energy is estimated by using Unscented Kalman Filter, while the process noise and measurement noise is adjusted by using the Sage-Husa adaptive algorithm, as a result the estimation accuracy is improved. The result shows that the State of Energy estimation by using Adaptive Unscented Kalman Filter algorithm is satisfactory to electric vehicle.

Design on Electric Vehicle Battery Management System with FlexRay Bus

2015 ◽  
Vol 789-790 ◽  
pp. 784-790
Author(s):  
Jian Kun Peng ◽  
Hong Wen He ◽  
Deng Pan
Keyword(s):  
Electric Vehicle ◽  
Management System ◽  
Fault Tolerant ◽  
Estimation Method ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Electric Vehicle Battery ◽  
Bench Experiment ◽  
Li Ion

FlexRay bus is considered as a more promising bus in the future with the performance of real-time, scalable, and fault-tolerant. In this paper we will design an electric vehicle battery management system (BMS) based on FlexRay bus, including the hardware design, the SoC estimation method, FlexRay protocol design and software development. The test bench experiment results show the system design is reasonable and feasible.Keywords- FlexRay, BMS, Li-ion battery,SoC, Design

scholarly journals A robust state of charge estimation for multiple models of lead acid battery using adaptive extended Kalman filter

2020 ◽  
Vol 9 (1) ◽  
pp. 1-11
Author(s):  
Maamar Souaihia ◽  
Bachir Belmadani ◽  
Rachid Taleb
Keyword(s):  
Kalman Filter ◽  
Management System ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Battery Management ◽  
Lead Acid Battery ◽  
Soc Estimation ◽  
Lead Acid

An accurate estimation technique of the state of charge (SOC) of batteries is an essential task of the battery management system. The adaptive Kalman filter (AEKF) has been used as an obsever to investigate the SOC estimation effectiveness. Therefore, The SOC is a reflexion of the chemistry of the cell which it is the key parameter for the battery management system. It is very complex to monitor the SOC and control the internal states of the cell. Three battery models are proposed and their state space models have been established, their parameters were identified by applying the least square method. However, the SOC estimation accuracy of the battery depends on the model and the efficiency of the algorithm. In this paper, AEKF technique is presented to estimate the SOC of Lead acid battery. The experimental data is used to identify the parameters of the three models and used to build different open circuit voltage–state of charge (OCV-SOC) functions relationship. The results shows that the SOC estimation based-model which has been built by hight order RC model can effectively limit the error, hence guaranty the accuracy and robustness.

scholarly journals Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 446 ◽  
Author(s):  
Muhammad Umair Ali ◽  
Amad Zafar ◽  
Sarvar Hussain Nengroo ◽  
Sadam Hussain ◽  
Muhammad Junaid Alvi ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Management System ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Methods ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Li Ion

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

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.

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