IDENTIFICATION OF END OF LIFE ON LITHIUM-ION BATTERIES CELLS THROUGH SUPPORT VECTOR MACHINES FOR IN-CHARGER BATTERY MANAGEMENT SYSTEM STRATEGY

Battery-powered Electric Vehicles (BEVs) such as electric cars, use the battery as the main power source to drive the motor, in addition to lighting, horn, and other functions. Currently, Balai Besar Bahan dan Barang Teknik (B4T) has been conducting research in Lithium-ion (Li-ion) battery prototype for an electric vehicle. However, the management system in accordance with the electrical characteristics of the battery prototype is still not available. Thus, to integrate the battery prototype with electrical components of the electric vehicle, it is necessary to design Battery Management System (BMS). Two important battery parameters observed are State of Charge (SOC) and State of Health (SOH). The method used for SOC was Coulomb Counting. SOH was determined using a combination between Support Vector Machine (SVM) and Relevance Vector Machine (RVM). Based on the experiments by using BMS, the battery performance could be more controlled and produces a linear curve of SOC and SOH.Keywords: Battery, electric vehicle, Battery Management System (BMS), Lithium-ion (Li-ion).

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Design of Prototype Battery Management System

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d7848.049420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 2334-2338 ◽

Cited By ~ 1

Keyword(s):

Lithium Ion Batteries ◽

Management System ◽

Lithium Ion ◽

High Energy ◽

High Energy Density ◽

Battery Management System ◽

Battery Management ◽

Public And Private ◽

Electrical Vehicles ◽

Electrical Vehicle

The electrical Vehicle (EV) is already on the roadmap of each necessary automaker and is seen because the answer to a a lot of property transport system, contributive to a discount of the gas Emissions. the utilization of inexperienced energy is turning into {increasingly progressively more and a lot of} more necessary in today’s world. Therefore, electrical vehicles are presently the most effective alternative for the setting in terms of public and private transportation. Lithium-ion batteries are commonly used in electric vehicles, bec ause of their high energy density. Sadly, lithium-ion batteries are unsafe unless they are run in the Safety Operation space (SOA). Therefore, A battery management system (BMS) should be employed in each metal particle battery, particularly for those employed in electrical vehicles. Thus, it plays a very important role in coming up with the safer electrical Vehicles.

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(Invited) Model-Based Battery Management System of Lithium-Ion Batteries

ECS Meeting Abstracts ◽

10.1149/ma2019-01/22/1129 ◽

2019 ◽

Keyword(s):

Lithium Ion Batteries ◽

Management System ◽

Lithium Ion ◽

Battery Management System ◽

Battery Management ◽

Model Based

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A Novel Temperature–Hysteresis Model for Power Battery of Electric Vehicles with an Adaptive Joint Estimator on State of Charge and Power

Energies ◽

10.3390/en12193621 ◽

2019 ◽

Vol 12 (19) ◽

pp. 3621

Author(s):

Xu Lei ◽

Xi Zhao ◽

Guiping Wang ◽

Weiyu Liu

Keyword(s):

Lithium Ion Batteries ◽

Management System ◽

Stress Test ◽

Lithium Ion ◽

State Of Charge ◽

Temperature Hysteresis ◽

Battery Management System ◽

Battery Management ◽

Hysteresis Model ◽

Ambient Temperatures

The battery state of charge (SOC) and state of power (SOP) are two essential parameters in the battery management system. For power lithium-ion batteries, temperature variation and the hysteresis effect are two of the main negative contributions to the accuracy of model-based SOC and SOP estimation. Thereby, a reliable circuit model is established herein to accurately estimate the working state of batteries. Considering the effect that temperature and hysteresis have on the electrical system, a unique fully-coupled temperature–hysteresis model is proposed to describe the interrelationship among capacity, hysteresis voltage, and temperature comprehensively. The key parameters of the proposed model are identified by experiments operated on lithium-ion batteries under varying ambient temperatures. Then we build a multi-state joint estimator to calculate the SOC and SOP on the basis of the temperature–hysteresis model. The effectiveness of the advanced model is verified by experiments at different temperatures. Moreover, the proposed joint estimator is verified by the improved dynamic stress test. The experimental results indicate that the proposed estimator making use of the temperature–hysteresis model can estimate SOC and SOP accurately and robustly. Our results also prove invaluable in terms of the construction of a flexible battery management system for applications in the actual industrial field.

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