scholarly journals Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhaona Lu ◽  
Junlong Wang ◽  
Chuanxing Wang ◽  
Guoqing Li
Keyword(s):  
Electric Vehicles ◽  
Time Integration ◽  
Estimation Method ◽  
Open Circuit ◽  
Battery Pack ◽  
Estimation Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Current Time ◽  
Power Battery

The state of charge estimation of a pure electric vehicle power battery pack is one of the important contents of the battery management system. Improving the estimation accuracy of the battery pack’s SOC is conducive to giving full play to its performance and preventing overcharge and discharge of a single battery. At present, the open-circuit voltage ampere-hour integral method is traditionally used to estimate the SOC value of the battery pack; however, this estimation method is not accurate enough to correct the initial value of SOC and cannot solve the problem of current time integration error between this correction and the next correction. As for the battery performance and characteristics of electric vehicles, it is pointed out that the size of the model value will affect the estimation accuracy of the Kalman signal value. Based on the analysis of the factors to be referred to in the calculation and estimation of SOC by Kalman for pure electric vehicles, the scheme is improved considering the change of battery model value, and the Kalman scheme is proposed. The feasibility and accuracy of the scheme are proved by several battery simulation experiments.

scholarly journals A ΔSOC-based equalization strategy applied to industry

2020 ◽  
Author(s):  
Maonan Wang ◽  
Chun Chang ◽  
Feng Ji
Keyword(s):  
Open Circuit ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Cell ◽  
New Strategy ◽  
The Difference ◽  
Relative Errors ◽  
The Relationship ◽  
Battery Cells

Abstract The voltage-based equalization strategy is widely used in the industry because the voltage (U) of the battery cell is very easy to obtain, but it is difficult to provide an accurate parameter for the battery management system (BMS). This study proposes a new equalization strategy, which is based on the difference between the state of charge (SOC) of any two battery cells in the battery pack, that is, a ΔSOC-based equalization strategy. The new strategy is not only as simple as the voltage-based equalization strategy, but it can also provide an accurate parameter for the BMS. Simply put, using the relationship between the open circuit voltage and the SOC of the battery pack, the proposed strategy can convert the difference between the voltage of the battery cells into ΔSOC, which renders a good performance. Additionally, the required parameters are all from the BMS, and no additional calculation is required, which makes the strategy as simple as the voltage-based balancing strategy. The four experiments show that the relative errors of ΔSOC estimated by the ΔSOC-based equalization strategy are 0.37%, 0.39%, 0.1% and 0.17%, and thereby demonstrate that the ΔSOC-based equalization strategy proposed in this study shows promise in replacing the voltage-based equalization strategy within the industry to obtain better performance.

scholarly journals Development of Life-cycle Detection and Extending Function for Reusing Automotive Lithium-ion Battery – Verified in 52V LiMnNiCoO2 Platform

2020 ◽  
Author(s):  
Wu-Yang Sean ◽  
Ana Pacheco
Keyword(s):  
Life Cycle ◽  
Lithium Ion Battery ◽  
Management System ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Cycle Detection

Abstract For reusing automotive lithium-ion battery, an in-house battery management system is developed. To overcome the issues of life cycle and capacity of reused battery, an online function of estimating battery’s internal resistance and open-circuit voltage based on adaptive control theory are applied for monitoring life cycle and remained capacity of battery pack simultaneously. Furthermore, ultracapacitor is integrated in management system for sharing peak current to prolong life span of reused battery pack. The discharging ratio of ultracapacitor is adjusted manually under Pulse-Width-Modulation signal in battery management system. In case study in 52V LiMnNiCoO2 platform, results of estimated open-circuit voltage and internal resistances converge into stable values within 600(s). These two parameters provide precise estimation for electrical capacity and life cycle. It also shows constrained voltage drop both in the cases of 25% to 75% of ultracapacitors discharging ratio compared with single battery. Consequently, the Life-cycle detection and extending functions integrated in battery management system as a total solution for reused battery are established and verified.

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 Performance Analysis of Commercial Passive Balancing Battery Management System Operation Using a Hardware-in-the-Loop Testbed

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8037
Author(s):  
Asadullah Khalid ◽  
Alexander Stevenson ◽  
Arif I. Sarwat
Keyword(s):  
Electric Vehicles ◽  
Second Life ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Hardware In The Loop ◽  
Simulation Environment ◽  
Simulink Model ◽  
Battery Management Systems ◽  
Constant Charge

With increased usage, individual batteries within the battery pack will begin to show disparate voltage and State of Charge (SOC) profiles, which will impact the time at which batteries become balanced. Commercial battery management systems (BMSs), used in electric vehicles (EVs) and microgrids, typically send out signals suggesting removal of individual batteries or entire packs to prevent thermal runaway scenarios. To reuse these batteries, this paper presents an analysis of an off-the-shelf Orion BMS with a constrained cycling approach to assess the voltage and SOC balancing and thermal performances of such near-to-second life batteries. A scaled-down pack of series-connected batteries in 6s1p and 6s2p topologies are cycled through a combination of US06 drive and constant charge (CC) profiles using an OPAL-RT real-time Hardware-in-the-loop (HIL) simulator. These results are compared with those obtained from the Matlab/Simulink model to present the error incurred in the simulation environment. Results suggest that the close-to-second life batteries can be reused if operated in a constrained manner and that a scaled-up battery pack topology reduces incurred error.

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 Smart Battery Pack for Electric Vehicles Based on Active Balancing with Wireless Communication Feedback

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3862 ◽  
Author(s):  
Mattia Ricco ◽  
Jinhao Meng ◽  
Tudor Gherman ◽  
Gabriele Grandi ◽  
Remus Teodorescu
Keyword(s):  
Life Cycle ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fault Tolerant ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Cell ◽  
Active Balancing ◽  
Battery Cells

In this paper, the concept of smart battery pack is introduced. The smart battery pack is based on wireless feedback from individual battery cells and is capable to be applied to electric vehicle applications. The proposed solution increases the usable capacity and prolongs the life cycle of the batteries by directly integrating the battery management system in the battery pack. The battery cells are connected through half-bridge chopper circuits, which allow either the insertion or the bypass of a single cell depending on the current states of charge. This consequently leads to the balancing of the whole pack during both the typical charging and discharging time of an electric vehicle and enables the fault-tolerant operation of the pack. A wireless feedback for implementing the balancing method is proposed. This solution reduces the need for cabling and simplifies the assembling of the battery pack, making also possible a direct off-board diagnosis. The paper validates the proposed smart battery pack and the wireless feedback through simulations and experimental results by adopting a battery cell emulator.

scholarly journals Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O2 Battery

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 324
Author(s):  
Haobin Jiang ◽  
Xijia Chen ◽  
Yifu Liu ◽  
Qian Zhao ◽  
Huanhuan Li ◽  
...  
Keyword(s):  
Stress Test ◽  
Robustness Analysis ◽  
Open Circuit ◽  
Combined Cycle ◽  
State Of Charge ◽  
Constant Current ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Liquid Dynamics

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.

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