Maximizing Battery Life and Usable Capacity With Battery Management System in Electric Vehicles

2017 ◽  
Author(s):  
Zoltán Szeli ◽  
Gábor Szakállas ◽  
Ferenc Szauter
Keyword(s):  
Electric Vehicles ◽  
Management System ◽  
Active Cell ◽  
Battery Pack ◽  
Research Centre ◽  
Battery Life ◽  
Battery Management System ◽  
Battery Management ◽  
Cost Weight ◽  
Cell Balancing

In terms of the electric vehicles is an important issue of sizing a battery pack. The designer must take account of parameters such as cost, weight and durability. We can optimize these parameters with the help of a battery management system with integrated active cell balancing function. The article describes the development of a battery management system that developed by the Research Centre of Vehicle Industry at Széchenyi István University, Győr, Hungary.

scholarly journals Active cell balancing for electric vehicle battery management system

2020 ◽  
Vol 11 (2) ◽  
pp. 571
Author(s):  
Thiruvonasundari Duraisamy ◽  
Deepa Kaliyaperumal
Keyword(s):  
Management System ◽  
Lithium Ion ◽  
Petroleum Products ◽  
Battery Life ◽  
Battery Management System ◽  
Battery Management ◽  
Ion Chemistry ◽  
Electrical Vehicles ◽  
In Series ◽  
Cell Balancing

The shrink in accessibility of petroleum products and increment in asset request are eventual outcomes for Electrical Vehicles (EVs). The battery has an impact on the performance of electrical vehicles, the driving range. Lithium ion (Li-ion) chemistry is extremely sensitive to overcharge and deep discharge, which can harm the battery, shortening its period of time, and even inflicting risky things. The Battery Management System (BMS) comprises of the consequent parts: management, equalization and protection. Of the three components, equalization is that the most crucial with respect to the durability of the battery framework. The ability of the full pack diminishes rapidly amid the procedure which leads to degradation of the full battery framework. This condition is extreme once the battery incorporates a more number of cells in series and frequent charging is conveyed through the battery string. The cell imbalance during charging, discharging is a major issue in battery systems used in EVs. To circumvent the cell imbalance, cell balancing is used. Cell balancing enhances battery safety and extends battery life. This paper discusses about different active balancing method to increase the life span of the battery module. Based on the comparison, the inductor based balancing method for 60V battery system is implemented in the MATLAB/Simscape environment and the results are discussed.

scholarly journals Battery Management System for Electric Vehicle

2021 ◽  
pp. 318-323
Author(s):  
Mr. Omkar Santosh Chavan ◽  
Mr. Prajwal Prakash Haldankar ◽  
Mr. Prathamesh Kashinath Patil ◽  
Mr. Rohan Nandkishor Sakpal ◽  
Prof. Onkar Marathe
Keyword(s):  
Electric Vehicles ◽  
Management System ◽  
Large Scale ◽  
Fossil Fuels ◽  
Healthy Lifestyle ◽  
Good Choice ◽  
Point Of View ◽  
Battery Management System ◽  
Battery Management ◽  
Cell Balancing

The healthy system in any country is a sign of healthy lifestyle. The rapid depletion of fossil fuels and harmful effects on the environment are forcing the use of electric vehicles on a large scale. Now days the proper use of energy sources that can available in nature is very important and efficiency point of view electric vehicles are the very good choice. The battery is a basic component of electric vehicles that signals a step forward in the direction of stable mobility. Lithium chemistry is now known as the energy storage in electric vehicles. However, many research points are still open including the optimal selection of cell material and the development of electronic circuits and algorithms for more efficient use of batteries. This paper reviews battery performance and discusses the requirements and standards applicable to the battery management system(BMS). The architecture for implementation battery management and techniques for state of charge and cell balancing are reported.

scholarly journals Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 471-479
Author(s):  
Thiruvonasundari Duraisamy ◽  
Kaliyaperumal Deepa
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Least Square ◽  
Battery Life ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Renewable Energy Resources ◽  
Voltage Deviation ◽  
Cell Balancing

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

scholarly journals Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 471-479
Author(s):  
Thiruvonasundari Duraisamy ◽  
Kaliyaperumal Deepa
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Least Square ◽  
Battery Life ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Renewable Energy Resources ◽  
Voltage Deviation ◽  
Cell Balancing

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

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 Current Sensorless Microcontroller-Based Battery Management System with SOC and Active Cell Balancing

2021 ◽  
Vol 2 (1) ◽  
pp. 24-36
Author(s):  
Muhammad Fikri Ardiansyah ◽  
Adha Imam Cahyadi ◽  
Oyas Wahyunggoro
Keyword(s):  
Management System ◽  
Low Cost ◽  
Active Cell ◽  
Pulse Load ◽  
Battery Management System ◽  
Battery Management ◽  
Current Estimation ◽  
Important Research Topic ◽  
Current Sensorless ◽  
Cell Balancing

Battery management system (BMS) has become an important research topic following the trend and development of the electric vehicle. Although research on Active Cell Balancing, SOC, and current estimation has been carried out, the previous work mostly focused on comparing and developing methods. In this research, we demonstrate the process of designing BMS hardware using a low-cost microcontroller and without using a current sensor. The SOC simulation results produce an RMSE of 0.0832% for the 100% -10% SOC-OCV curve, and the current estimation simulation produces an RMSE of 0.2576 A, while for testing using a 6-ohm pulse load, the RMSE error value is 0.3960 A. The Active Cell Balancing method was successfully performed in simulation with Simulink. Furthermore, our simulation and test results suggest that complex battery models and multiple SOC-OCV curves can be used for better current and OCV estimation results. Our experimental results are also useful to develop a guideline to design a microcontroller-based BMS.

scholarly journals Lithium-Ion Battery Cell-Balancing Algorithm for Battery Management System Based on Real-Time Outlier Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Changhao Piao ◽  
Zhaoguang Wang ◽  
Ju Cao ◽  
Wei Zhang ◽  
Sheng Lu
Keyword(s):  
Lithium Ion Battery ◽  
Outlier Detection ◽  
Management System ◽  
Lithium Ion ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Cell ◽  
Online Clustering ◽  
Cell Balancing

A novel cell-balancing algorithm which was used for cell balancing of battery management system (BMS) was proposed in this paper. Cell balancing algorithm is a key technology for lithium-ion battery pack in the electric vehicle field. The distance-based outlier detection algorithm adopted two characteristic parameters (voltage and state of charge) to calculate each cell’s abnormal value and then identified the unbalanced cells. The abnormal and normal type of battery cells were acquired by online clustering strategy and bleeding circuits (R= 33 ohm) were used to balance the abnormal cells. The simulation results showed that with the proposed balancing algorithm, the usable capacity of the battery pack increased by 0.614 Ah (9.5%) compared to that without balancing.

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