A cost effective accumulator management system for electric vehicles

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Suchitra D ◽  
Rajarajeswari R ◽  
Dhruv Singh Bhati
Keyword(s):  
Electric Vehicles ◽  
Management System ◽  
Lithium Ion ◽  
Cost Effective ◽  
Battery Management System ◽  
Battery Management ◽  
Cell Parameters ◽  
Battery Design ◽  
Battery Packs ◽  
Cost Efficient

AbstractAn accumulator or battery is an energy storage cramped in an adaptable stockade. Lithium-ion batteries are commonly used in hybrid electric vehicles (HEV) and battery operated electric vehicles (BOEV) due to its eco-friendliness and increased efficiency. To maintain lithium batteries in the safe operating region and also to perform tasks like cell balancing, preventing thermal runaway, maintain the state of health, an effective battery management system (BMS) is required. The BMS should also communicate effectively between host devices and battery packs. This paper proposes a reliable, modular and cost-efficient BMS, which will emanate an alert when a fault occurs and thus preventing the battery from damage. An efficient control strategy has been proposed for charging and discharging of the battery pack. The thermal analysis of the lithium-ion battery used in this work is simulated using battery design studio (BDS) with the inclusion of a self-discharging effect. The proposed hardware setup also provides a provision for on-board diagnosis (OBD) and logging in the accumulator management system (AMS) to constantly monitor the cell parameters like voltage, current, and temperature. The live data display of AMS working is also shown during abnormal and normal conditions. Also, an attempt is made to use the design of proposed AMS for HEV.

Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles

2019 ◽  
Vol 68 (5) ◽  
pp. 4110-4121 ◽  
Author(s):  
Rui Xiong ◽  
Yongzhi Zhang ◽  
Ju Wang ◽  
Hongwen He ◽  
Simin Peng ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Management System ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management

A Design Methodology for Lithium-Ion Battery Management System and its Application to an Autonomous Underwater Vehicle

2011 ◽  
Vol 383-390 ◽  
pp. 7175-7182 ◽  
Author(s):  
Chao Shen ◽  
Lei Wan
Keyword(s):  
Management System ◽  
Autonomous Underwater Vehicle ◽  
Lithium Ion ◽  
Underwater Vehicle ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Battery Packs ◽  
Non Linear System ◽  
Estimated Error

Battery management system (BMS) in autonomous underwater vehicle (AUV) not only can measure the main parameters of battery packs such as current, voltage, and temperature, but also estimate the state of charge (SOC) of battery packs. This paper proposes a broad approach for the design of battery management system. The new design can improve the cycle life and safety capability. With the model well designed, the parameters required are obtained and the SOC estimation is completed. Extended Kalman filter (EKF) was chosen to make the last estimate with the reliable battery model which was used to the non-linear system to estimate SOC and suitable for AUV applications. The experiments results prove that the data measured by battery management system have high precision and reliability. The estimated error of SOC was also small, which was better than other approaches for estimate.

scholarly journals A Survey of Wireless Battery Management System: Topology, Emerging Trends, and Challenges

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2193
Author(s):  
Akash Samanta ◽  
Sheldon S. Williamson
Keyword(s):  
Electric Vehicles ◽  
Management System ◽  
Lithium Ion ◽  
Future Research ◽  
Battery Management System ◽  
Battery Management ◽  
Implementation Cost ◽  
Future Developments ◽  
Emerging Trends ◽  
Depth Analysis

An effective battery management system (BMS) is indispensable for any lithium-ion battery (LIB) powered systems such as electric vehicles (EVs) and stationary grid-tied energy storage systems. Massive wire harness, scalability issue, physical failure of wiring, and high implementation cost and weight are some of the major issues in conventional wired-BMS. One of the promising solutions researchers have come up with is the wireless BMS (WBMS) architecture. Despite research and development on WBMS getting momentum more than a decade ago, it is still in a preliminary stage. Significant further upgradation is required towards developing an industry-ready WBMS, especially for high-power LIB packs. Therefore, an in-depth survey exclusively on WBMS architectures is presented in this article. The aim is to provide a summary of the existing developments as well as to present an informative guide to the research community for future developments by highlighting the issues, emerging trends, and challenges. In-depth analysis of the existing WBMS topologies will not only help the researchers to understand the existing challenges and future research scopes clearly but at the same time enthuse them to focus their research inclination in the domain of WBMS.

Distributed Battery Management System in Battery Electric Vehicle

2011 ◽  
Vol 201-203 ◽  
pp. 2427-2430
Author(s):  
Yuan Liao ◽  
Ju Hua Huang ◽  
Qun Zeng
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicle ◽  
Management System ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Electric Vehicle ◽  
Battery Management ◽  
Extended Kalman Filtering ◽  
Battery Packs ◽  
Master Module

According to the features of lithium ion battery packs, a distributed battery management system (BMS) for battery electric vehicle (BEV) is designed in this article. The BMS consists of a master module with several sampling modules. The kernel of master module is TMS320C2812 digital signal processor, and the kernel of sampling module is P87C591 singlechip. The main functions of master module include estimation of state of charge (SOC) and security management of lithium ion battery packs, and the main functions of sampling module include battery information collection and CAN bus based communication. SOC estimation method based on Extended Kalman filtering (EKF) theory is adopted in this article to precisely estimate the SOC of lithium ion battery packs.

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.

Battery Management System Based on Virtual Instrument

2013 ◽  
Vol 772 ◽  
pp. 725-730
Author(s):  
Xu Jun Li ◽  
Da Liu ◽  
Rui Yan ◽  
Yue Qiu Gong ◽  
Yong Pan
Keyword(s):  
Virtual Instrument ◽  
Management System ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
Security Metrics ◽  
Power Battery ◽  
Battery Packs ◽  
The Status ◽  
Control Switch

A battery management system (BMS) is described along with important features for protecting and optimizing the performance of large 18650 lithium power battery packs. Of particular interest is the selection of many cells, that is, according to the system needs to choose healthy cells into the system to run. In order to shorten the cycle of research, the paper proposed a BMS based on virtual instrument (VI) data acquisition system. It can monitor parameters such as monomer voltage, total voltage, current, temperature, estimating state of charge (SOC) etc, and it also can control switch when a parameter exceeds the allowed range, the corresponding monomer cell will be automatically cut off the switch and alarm. Experimental results are included for a pack of seven 2.2 Ah (amp-hour) 18650 lithium power cells. It can monitor the status of the lithium-ion battery pack according to the security metrics of 18650 power lithium cells. It can control other types of power batteries by means of modified index.

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