Model predictive fast charging control by means of a real-time discrete electrochemical model

With the ever-increasing usage of lithium-ion batteries, especially in transportation applications, accurate estimation of battery state of charge (SOC) is of paramount importance. A majority of the current SOC estimation methods rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation under different operating conditions or when the battery ages. This paper presents a novel real-time SOC estimation of a lithium-ion battery by applying the particle swarm optimization (PSO) method to a detailed electrochemical model of a single cell. This work also optimizes both the single-cell model and PSO algorithm so that the developed algorithm can run on an embedded hardware with reasonable utilization of central processing unit (CPU) and memory resources while estimating the SOC with reasonable accuracy. A modular single-cell electrochemical model, as well as the proposed constrained PSO-based SOC estimation algorithm, was developed in Simulink©, and its performance was theoretically verified in simulation. Experimental data were collected for healthy and aged Li-ion battery cells in order to validate the proposed algorithm. Both simulation and experimental results demonstrate that the developed algorithm is able to accurately estimate the battery SOC for 1C charge and 1C discharge operations for both healthy and aged cells.

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Quantitative Analysis of Performance Decrease and Fast-Charging Limitation for Lithium-Ion Batteries at Low Temperature Based on the Electrochemical Model

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2020.3024801 ◽

2020 ◽

pp. 1-11

Author(s):

Haijun Ruan ◽

Bingxiang Sun ◽

Weige Zhang ◽

Xiaojia Su ◽

Xitian He

Keyword(s):

Quantitative Analysis ◽

Low Temperature ◽

Lithium Ion Batteries ◽

Lithium Ion ◽

Fast Charging ◽

Electrochemical Model ◽

Analysis Of Performance

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A broadcast mechanism for real-time charging control of a fleet of plug-in electric vehicles

2017 IEEE Manchester PowerTech ◽

10.1109/ptc.2017.7981054 ◽

2017 ◽

Author(s):

Vincenzo Luca Fiengo ◽

Madeleine Gibescu ◽

Sergio Grammatico

Keyword(s):

Real Time ◽

Electric Vehicles ◽

Charging Control

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Smart Electric Vehicle Charging via Adjustable Real-Time Charging Rates

Applied Sciences ◽

10.3390/app112210962 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10962

Author(s):

Theron Smith ◽

Joseph Garcia ◽

Gregory Washington

Keyword(s):

Fuzzy Logic ◽

Adverse Effects ◽

Real Time ◽

Electric Vehicle ◽

Control Algorithm ◽

Optimization Techniques ◽

Electric Vehicle Charging ◽

Real Time Scale ◽

Charging Control ◽

Real World Applications

This paper presents a plug-in electric vehicle (PEV) charging control algorithm, Adjustable Real-Time Valley Filling (ARVF), to improve PEV charging and minimize adverse effects from uncontrolled PEV charging on the grid. ARVF operates in real time, adjusts to sudden deviations between forecasted and actual baseloads, and uses fuzzy logic to deliver variable charging rates between 1.9 and 7.2 kW. Fuzzy logic is selected for this application because it can optimize nonlinear systems, operate in real time, scale efficiently, and be computationally fast, making ARVF a robust algorithm for real-world applications. In addition, this study proves that when the forecasted and actual baseload vary by more than 20%, its real-time capability is more advantageous than algorithms that use optimization techniques on predicted baseload data.

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