A Self-Adaptive BMS Based on CAN-Bus for Power Li-Ion Battery

2011 ◽  
Vol 130-134 ◽  
pp. 3553-3556
Author(s):  
Meng Wang ◽  
Jian Qiang Wu ◽  
Xiao Hua Zhang
Keyword(s):  
Can Bus ◽  
The State ◽  
Temperature Management ◽  
Battery Management System ◽  
Li Ion Battery ◽  
Main Road ◽  
Battery Lifetime ◽  
Li Ion ◽  
Master Module ◽  
Self Adaptive

A self-adaptive battery management system (BMS) based on CAN-bus for power Li-ion battery was designed in this paper. It is designed distributed and composed of sampling modules, a master module and some aid devices. The sampling module is used to sampling the voltage of the cells and the temperature in the pack. And it keeps the temperature in safe range by controlling the fans in the pack. The master module receives the information from the sampling modules, samples the current of the main road, estimates the state of charge (SOC), the state of health (SOH), controls the main road relays. The modules communicate with each other by CAN-bus. The fault and temperature management are performed adaptively. It is shown that this self-adaptive BMS extend the battery lifetime and guarantee safe operation. And the self-power consumption is very low.

scholarly journals Bus-to-Route and Route-to-Bus Approaches in Hybrid Electric Buses Fleet for Li-ion Battery Lifetime Extension

2019 ◽  
Vol 1 (2) ◽  
pp. 15-22
Author(s):  
Jon Ander López ◽  
Victor Isaac Herrera Perez ◽  
Aitor Milo ◽  
Haizea Gaztañaga ◽  
Haritza Camblong
Keyword(s):  
Energy Management ◽  
The State ◽  
Route Optimization ◽  
State Of Health ◽  
Li Ion Battery ◽  
Energy Management Strategy ◽  
Battery Lifetime ◽  
Li Ion ◽  
Points Of View ◽  
Hybrid Electric

The aim of this paper is to propose a methodology for managing the Li-ion battery lifetime of a whole fleet with the aim to improve the total cost of ownership of hybrid electric buses. This approach has been addressed from two points of view the bus-to-route and route-to-bus approaches. The bus-to-route optimization is focused on the energy management strategy generation of each bus of the fleet. A techno-economic, route energetic evaluation and battery aging analysis of the fleet have been performed. From the outcome of this analysis, the buses have been grouped, according the state of health of each bus. Based on the analysis and classification, the route-to-bus approach is applied. This technique lies on both, a re-evaluation of the energy management system and/or the re-organization of the buses according to the state of health of each bus. Increases of BT lifetimes up to 10.7% are obtained with the proposed approach.

scholarly journals Estimation of State of Charge (SoC) Using Modified Coulomb Counting Method With Open Circuit Compensation For Battery Management System (BMS)

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Puspita Ningrum ◽  
Novie Ayub Windarko ◽  
Suhariningsih Suhariningsih
Keyword(s):  
Management System ◽  
Voltage Divider ◽  
Open Circuit ◽  
Voltage Sensor ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Control Center ◽  
Li Ion

Abstract— Battery is one of the important components in the development of renewable energy technology. This paper presents a method for estimating the State of Charge (SoC) for a 4Ah Li-ion battery. State of Charge (SoC) is the status of the capacity in the battery in the form of a percentage which makes it easier to monitor the battery during use. Coulomb calculations are widely used, but this method still contains errors during integration. In this paper, SoC measurement using Open Circuit Voltage Compensation is used for the determination of the initial SoC, so that the initial SoC reading is more precise, because if the initial SoC reading only uses a voltage sensor, the initial SoC reading is less precise which affects the next n second SoC reading. In this paper, we present a battery management system design or commonly known as BMS (Battery Management System) which focuses on the monitoring function. BMS uses a voltage sensor in the form of a voltage divider circuit and an ACS 712 current sensor to send information about the battery condition to the microcontroller as the control center. Besides, BMS is equipped with a protection relay to protect the battery. The estimation results of the 12volt 4Ah Li-ion battery SoC with the actual reading show an error of less than 1%.Keywords—Battery Management System, Modified Coulomb Counting, State of Charge.

Internal Resistance Based Assessment Model for the Degradation of Li-Ion Battery Pack

2020 ◽  
Author(s):  
Iffandya Popy Wulandari ◽  
Min-Chun Pan
Keyword(s):  
Internal Resistance ◽  
Remaining Useful Life ◽  
Assessment Model ◽  
Battery Pack ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Battery Management System ◽  
Li Ion Battery ◽  
Battery Packs ◽  
Li Ion

Abstract As one pioneer means for energy storage, Li-ion battery packs have a complex and critical issue about degradation monitoring and remaining useful life estimation. It induces challenges on condition characterization of Li-ion battery packs such as internal resistance (IR). The IR is an essential parameter of a Li-ion battery pack, relating to the energy efficiency, power performance, degradation, and physical life of the li-ion battery pack. This study aims to obtain reliable IR through applying an evaluation test that acquires data such as voltage, current, and temperature provided by the battery management system (BMS). Additionally, this paper proposes an approach to predict the degradation of Li-ion battery pack using support vector regression (SVR) with RBF kernel. The modeling approach using the relationship between internal resistance, different SOC levels 20%–100%, and cycle at the beginning of life 1 cycle until cycle 500. The data-driven method is used here to achieve battery life prediction.based on internal resistance behavior in every period using supervised machine learning, SVR. Our experiment result shows that the internal resistance was increasing non-linear, approximately 0.24%, and it happened if the cycle rise until 500 cycles. Besides, using SVR algorithm, the quality of the fitting was evaluated using coefficient determination R2, and the score is 0.96. In the proposed modeling process of the battery pack, the value of MSE is 0.000035.

Impact of Cell Replacement on the State-of-Health for Parallel Li-Ion Battery Pack

2014 ◽  
Author(s):  
P.L. Huynh ◽  
O. Abu Mohareb ◽  
M. Grimm ◽  
H.J. Maurer ◽  
A. Richter ◽  
...  
Keyword(s):  
The State ◽  
Battery Pack ◽  
State Of Health ◽  
Li Ion Battery ◽  
Cell Replacement ◽  
Li Ion

scholarly journals Battery management system for Li-ion battery

2017 ◽  
Vol 2017 (13) ◽  
pp. 1437-1440 ◽  
Author(s):  
Fangfang Zhu ◽  
Guoan Liu ◽  
Cai Tao ◽  
Kangli Wang ◽  
Kai Jiang
Keyword(s):  
Management System ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Li Ion
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