Impact of sensor accuracy of battery management system on SOC estimation of electric vehicle based on EKF algorithm

Author(s):  
Yongqi Wang ◽  
Wei Yin ◽  
Qingzhong Yan ◽  
Yong Cheng
2014 ◽  
Vol 945-949 ◽  
pp. 1500-1506
Author(s):  
Zhong An Yu ◽  
Jun Peng Jian

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.


2015 ◽  
Vol 789-790 ◽  
pp. 784-790
Author(s):  
Jian Kun Peng ◽  
Hong Wen He ◽  
Deng Pan

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


Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 446 ◽  
Author(s):  
Muhammad Umair Ali ◽  
Amad Zafar ◽  
Sarvar Hussain Nengroo ◽  
Sadam Hussain ◽  
Muhammad Junaid Alvi ◽  
...  

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.


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