SoC Estimation of Li-Ion Battery Pack for Light Electric Vehicles using Enhanced Coulomb Counting Algorithm

2019 ◽  
Author(s):  
Meriem Ben Lazreg ◽  
Ines Baccouche ◽  
Sabeur Jemmali ◽  
Bilal Manai ◽  
Mahmoud Hamouda
Keyword(s):  
Electric Vehicles ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Li Ion ◽  
Counting Algorithm

scholarly journals Li-Ion Battery Pack SoC Estimation for Electric Vehicles

2018 ◽  
Author(s):  
Kodjo Senou Rodolphe Mawonou ◽  
Akram Eddahech ◽  
Didier Dumur ◽  
Emmanuel Godoy ◽  
Dominique Beauvois ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Li Ion

A Methodological Approach for Supporting the Thermal Design of Li-Ion Battery for Customized Electric Vehicles

2014 ◽  
Author(s):  
Daniele Landi ◽  
Paolo Cicconi ◽  
Michele Germani
Keyword(s):  
Thermal Behavior ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Combustion Engine ◽  
Thermal Design ◽  
Battery Pack ◽  
Scale Production ◽  
Li Ion Battery ◽  
Suitable Alternative ◽  
Li Ion

An important issue in the mechanical industry is the reduction of the time to market, in order to meet quickly the customer needs. This goal is very important for SMEs that produce small lots of customized products. In the context of greenhouse gas emissions reduction, vehicles powered by electric motors seem to be the most suitable alternative to the traditional internal combustion engine vehicles. The market of customized electric vehicles is a niche market suitable for SMEs. Nowadays, the energy storage system of an electric vehicle powertrain consists of several Li-ion cells arranged in a container called battery pack. Particularly, the battery unit is considered as the most critical component in electric vehicle, because it impacts on performance and life cycle cost. Currently, the design of a battery pack mostly depends on the related market size. A longer design time is expected in the case of a large scale production. While a small customized production requires more agility and velocity in the design process. The proposed research focuses on a design methodology to support the designer in the evaluation of the battery thermal behavior. This work has been applied in the context of a customized small production. As test case, an urban electric light commercial vehicle has been analyzed. The designed battery layout has been evaluated and simulated using virtual prototyping tools. A cooling configuration has been analyzed and then prototyped in a physical vehicle. The virtual thermal behavior of a Li-ion battery has been validated at the test bench. The real operational conditions have been analyzed reproducing several ECE-15 driving cycles and many acceleration runs at different load values. Thermocouples have measured the temperature values during the physical experiments, in order to validate the analytical thermal profile evaluated with the proposed design approach.

A KBE Design Methodology to Support Li-Ion Battery Cooling for Hybrid and Electric Vehicles

2012 ◽  
Author(s):  
Daniele Landi ◽  
Paolo Cicconi ◽  
Michele Germani
Keyword(s):  
Electric Vehicles ◽  
Battery Pack ◽  
Electrochemical Reactions ◽  
Chemical Compositions ◽  
Main Question ◽  
Li Ion Battery ◽  
Knowledge Based ◽  
Medium Enterprise ◽  
Customized Production ◽  
Li Ion

In a scenario of a small and customized production of Electric Vehicles, it is important to have methodologies and tools able to guarantee high flexibility, good quality, and reduced time to market. The optimisation of electric motors and battery design stages is a key factor to achieve the expected results. Specific activities such as design automation, virtual prototyping and simulation are fundamental to obtain high-performance customised solutions. In this context the study of cooling systems for Li-Ion battery packs is one of the most important problems regarding EV and PHEV powertrain design. The proposed research presents a Knowledge Based methodology to support the cooling design of a battery pack and an analytical tool to evaluate the temperature and heat generation due to electrochemical reactions. All the research project is finalized to a definition of a Knowledge Based System to define a battery layout including engineering knowledge. The current strategies of battery pack design depend on the market size. In particular, the research activity is focalized on customized production of a SME (Small Medium Enterprise). The main question concerns the estimation of heat generated from electrochemical reactions in a single battery cell. In order to achieve these objectives, a preliminary phase for knowledge acquisition is necessary and a process of formalization has been carried out using the Knowledge Management methods. A first prototype of the Knowledge Based Engineering tool has been developed to determine the optimal cooling condition of a battery pack. The main module is based on an analytical approach which has been formulated to evaluate the average thermal flow generated by a standard LiFePO4 polymeric cell at different values of current and state of charge (SOC). This method can be used for different types of geometry and different chemical compositions. Finally, the proposed approach has been validated by experimental measures and numerical simulations in collaboration with a medium enterprise of electric energy storage systems and light ecological vehicles.

scholarly journals Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3532 ◽  
Author(s):  
Majid Astaneh ◽  
Jelena Andric ◽  
Lennart Löfdahl ◽  
Dario Maggiolo ◽  
Peter Stopp ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Large Scale ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Battery Packs ◽  
Li Ion ◽  
Optimization Methodology ◽  
Battery Cells

Large-scale introduction of electric vehicles (EVs) to the market sets outstanding requirements for battery performance to extend vehicle driving range, prolong battery service life, and reduce battery costs. There is a growing need to accurately and robustly model the performance of both individual cells and their aggregated behavior when integrated into battery packs. This paper presents a novel methodology for Lithium-ion (Li-ion) battery pack simulations under actual operating conditions of an electric mining vehicle. The validated electrochemical-thermal models of Li-ion battery cells are scaled up into battery modules to emulate cell-to-cell variations within the battery pack while considering the random variability of battery cells, as well as electrical topology and thermal management of the pack. The performance of the battery pack model is evaluated using transient experimental data for the pack operating conditions within the mining environment. The simulation results show that the relative root mean square error for the voltage prediction is 0.7–1.7% and for the battery pack temperature 2–12%. The proposed methodology is general and it can be applied to other battery chemistries and electric vehicle types to perform multi-objective optimization to predict the performance of large battery packs.

scholarly journals Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3733
Author(s):  
Benedikt Rzepka ◽  
Simon Bischof ◽  
Thomas Blank
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Electric Vehicles ◽  
Cell Types ◽  
Estimation Methods ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Filter Tuning ◽  
Li Ion ◽  
Structured Approach

The growing share of renewable energies in power production and the rise of the market share of battery electric vehicles increase the demand for battery technologies. In both fields, a predictable operation requires knowledge of the internal battery state, especially its state of charge (SoC). Since a direct measurement of the SoC is not possible, Kalman filter-based estimation methods are widely used. In this work, a step-by-step guide for the implementation and tuning of an extended Kalman filter (EKF) is presented. The structured approach of this paper reduces efforts compared with empirical filter tuning and can be adapted to various battery models, systems, and cell types. This work can act as a tutorial describing all steps to get a working SoC estimator based on an extended Kalman filter.

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