scholarly journals A Fractional-Order Kinetic Battery Model of Lithium-Ion Batteries Considering a Nonlinear Capacity

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 394 ◽  
Author(s):  
Qi Zhang ◽  
Yan Li ◽  
Yunlong Shang ◽  
Bin Duan ◽  
Naxin Cui ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Fractional Order ◽  
Lithium Ion ◽  
Absolute Error ◽  
Battery Management System ◽  
Order Kinetic ◽  
Battery Management ◽  
Nonlinear Capacity ◽  
Battery Model ◽  
Parameter Identifications

Accurate battery models are integral to the battery management system and safe operation of electric vehicles. Few investigations have been conducted on the influence of current rate (C-rate) on the available capacity of the battery, for example, the kinetic battery model (KiBaM). However, the nonlinear characteristics of lithium-ion batteries (LIBs) are closer to a fractional-order dynamic system because of their electrochemical materials and properties. The application of fractional-order models to represent physical systems is timely and interesting. In this paper, a novel fractional-order KiBaM (FO-KiBaM) is proposed. The available capacity of a ternary LIB module is tested at different C-rates, and its parameter identifications are achieved by the experimental data. The results showed that the estimated errors of available capacity in the proposed FO-KiBaM were low over a wide applied current range, specifically, the mean absolute error was only 1.91%.

scholarly journals Empirical and physics-based approaches to estimate states of lithium-ion battery

2019 ◽  
Vol 21 (2) ◽  
pp. 259-268
Author(s):  
Anna A. Fedorova
Keyword(s):  
Embedded System ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
Market Demand ◽  
Huge Number ◽  
Battery Model ◽  
The Embedded System

Lithium-ion batteries are integral parts of our life due to the rapid increase of applications which require batteries for their exploitation. Thus, there is a market demand to produce lithium-ion batteries for a huge number of applications from electric vehicles to energy storages. Battery Management System (BMS) is developed to maintain safe battery exploitation conditions. Most BMSs are embedded systems that have physical memory limits. Therefore, battery model should be easy to simulate to be integrated into BMS for states estimation. In the present paper we intend to compare empirical and physics-based approaches to estimate lithium-ion battery states with respect to their possibility of implementation in the embedded system. We will use Kalman filter to estimate battery states by means of the mentioned models.

Modeling and Simulation Research on Lithium-Ion Battery in Electric Vehicles Based on Genetic Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 246-249
Author(s):  
Cheng Lin ◽  
Xiao Hua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Lithium Ion Battery ◽  
Stress Test ◽  
Lithium Ion ◽  
Model Parameters ◽  
Test Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Simulation Research ◽  
Battery Model

Based on the genetic algorithm (GA), a novel type of parameters identification method on battery model was proposed. The battery model parameters were optimized by genetic optimization algorithm and the other parameters were identified through the hybrid pulse power characterization (HPPC) test. Accuracy and efficiency of the battery model were validated with the dynamic stress test (DST). Simulation and experiment results shows that the proposed model of the lithium-ion battery with identified parameters was accurate enough to meet the requirements of the state of charge (SoC) estimation and battery management system.

scholarly journals Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5265
Author(s):  
Longxing Wu ◽  
Kai Liu ◽  
Hui Pang ◽  
Jiamin Jin
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Estimation Methods ◽  
Current Sensor ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Electrochemical Model ◽  
The Impact

State of Charge (SOC) is essential for a smart Battery Management System (BMS). Traditional SOC estimation methods of lithium-ion batteries are usually conducted using battery equivalent circuit models (ECMs) and the impact of current sensor bias on SOC estimation is rarely considered. For this reason, this paper proposes an online SOC estimation based on a simplified electrochemical model (EM) for lithium-ion batteries considering sensor bias. In EM-based SOC estimation structure, the errors from the current sensor bias are addressed by proportional–integral observer. Then, the accuracy of the proposed EM-based SOC estimation is validated under different operating conditions. The results indicate that the proposed method has good performance and high accuracy in SOC estimation for lithium-ion batteries, which facilitates the on-board application in advanced BMS.

scholarly journals Design of Prototype Battery Management System

2020 ◽  
Vol 9 (4) ◽  
pp. 2334-2338 ◽  
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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