scholarly journals Review on the State of Charge Estimation Methods for Electric Vehicle Battery

2020 ◽  
Vol 11 (1) ◽  
pp. 23
Author(s):  
Mingyue Zhang ◽  
Xiaobin Fan
Keyword(s):  
Control Theory ◽  
Electric Vehicle ◽  
The State ◽  
State Of Charge ◽  
Estimation Methods ◽  
Battery Management ◽  
Electric Cars ◽  
Electric Vehicle Battery ◽  
Battery Technology ◽  
State Of Charge Estimation

Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time-varying and non-linear properties, which are extremely complex. Therefore, accurately estimating the state of charge is a challenging task. This paper reviews various representative patents and papers related to the state of charge estimation methods for an electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional methods based on the battery experiments, the modern methods based on control theory, and other methods based on the innovative ideas, especially focusing on the algorithms based on control theory. The results imply that the algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish a rich database, improve hardware technology, come up with a much better battery model, and give full play to the advantages of each algorithm.

A Review of Research on the State-of-Charge Estimation Methods for Electric Vehicle Battery

2019 ◽  
Vol 13 ◽  
Author(s):  
Mingyue Zhang ◽  
Xiaobin Fan ◽  
Jing Gan ◽  
Zeng Song ◽  
Bin Zhao
Keyword(s):  
Control Theory ◽  
Electric Vehicle ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Of Charge ◽  
Estimation Methods ◽  
Advantages And Disadvantages ◽  
Electric Vehicle Battery ◽  
State Of Charge Estimation

Background: Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time change and non-linear properties, which are extremely complex systems. Therefore, accurate estimating the state of charge is a challenging thing. Objective: The study aims to report the latest progress in the studies of the state-of-charge estimation methods for electric vehicle battery. Methods: This paper reviews various representative patents and papers related to the state of charge estimation methods for electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional estimation algorithm based on the battery experiment, the estimation algorithm based on modern control theory and other estimation algorithm based on the innovative ideas, especially focusing on the algorithms based on control theory. Results: The advantages and disadvantages, current and future developments of the state-of-charge estimation methods are finally provided and discussed. Conclusion: Each kind of state of charge estimation method has its own characteristics, suitable for different occasions. At present, algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish rich database, improve hardware technology, put up with more perfect battery model, and give full play to the advantages of each algorithm.

Comparative Analysis of the Model Based State of Charge Estimation Methods for Li Ion Batteries Used in Electric Vehicle Applications

2021 ◽  
Author(s):  
SHUVAM ROUTRAY ◽  
Kranthi Nidubrolu ◽  
Abhisha Chauhan ◽  
Chinmay Ashok Kirtane ◽  
Lohit Dhamija ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Electric Vehicle ◽  
State Of Charge ◽  
Estimation Methods ◽  
Li Ion Batteries ◽  
Model Based ◽  
Li Ion ◽  
State Of Charge Estimation

Estimation of Lead-Acid Battery SOC Based on Kalman Filtering Algorithm

2014 ◽  
Vol 651-653 ◽  
pp. 1064-1067
Author(s):  
Dong Zhao Chen ◽  
Li Jun Jia
Keyword(s):  
Electric Vehicle ◽  
Charge State ◽  
State Of Charge ◽  
Efficient Estimation ◽  
Normal Operation ◽  
Estimation Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Filtering Method ◽  
Electric Vehicle Battery

The key technology of electric vehicle battery management lies in the battery state of charge (SOC) estimation, accurate and efficient estimation of SOC can provide the reference data for the control system of the electric vehicle in time. On the basis of the Ah measurement method, Calman filtering method, open circuit voltage method to estimate method, the estimation procedure to estimate the charged battery optimized Calman filtering method, effectively shorten the calculation process, improve the estimation accuracy, in the normal operation of electric vehicles under the influence of comprehensive consideration about the discharge voltage, battery temperature to estimate the state of charge, the Calman filter optimization algorithm, and its application in the electric vehicle battery management system. The experimental results show that, the algorithm can calculate the real-time high charge state of surplus value, provides a set of effective estimation scheme of calculating the battery charge state.

Implementation of the Li-Ion Battery Management System Based on DS2438

2015 ◽  
Vol 733 ◽  
pp. 714-717 ◽  
Author(s):  
Ping Yang ◽  
Hou Yu Yu ◽  
Yong Gang Yan
Keyword(s):  
Management System ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Electric Cars ◽  
Power Battery ◽  
Electric Vehicle Battery ◽  
Li Ion

In order to ensure good performance and extend the lifetime of li-ion batteries in electric cars, effective real-time monitoring and management must be valued. This paper designs an electric vehicle battery management system based on a smart battery monitoring chip, DS2438. It integrates the measurement of battery's temperature, voltage, current, and power as a whole, which not only simplifies the circuit, but also saves system cost. The battery’s SOC (State Of Charge) can be easily estimated and displayed in this design. It improves the reliability of power battery pack and prolonged its life, which can be used as reference to battery management system design and application.

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