Aging prediction and state of charge estimation of a LiFePO 4 battery using input time-delayed neural networks

Autor: Chinemerem Christopher Ibe-Ekeocha, Hicham Chaoui, Hamid Gualous
Přispěvatelé: Tennessee Tech University [Cookeville] (TTU), Laboratoire Universitaire des Sciences Appliquées de Cherbourg (LUSAC), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)
Rok vydání: 2017
Předmět:
Battery (electricity)
Engineering
State of charge (SOC)
State of health
020209 energy
Energy Engineering and Power Technology
02 engineering and technology
7. Clean energy
[SPI.MAT]Engineering Sciences [physics]/Materials
[SPI]Engineering Sciences [physics]
Hardware_GENERAL
Robustness (computer science)
Computer Science::Networking and Internet Architecture
0202 electrical engineering
electronic engineering
information engineering
Electronic engineering
Electrical and Electronic Engineering
Artificial neural network (ANN)
[PHYS]Physics [physics]
Artificial neural network
business.industry
[SPI.FLUID]Engineering Sciences [physics]/Reactive fluid environment
020208 electrical & electronic engineering
State of hearth (SOH)
Open circuit voltage (OCV)
input time-delayed neural network (ITDNN)
lithium iron phosphate (LifePo4)
Nonlinear system
Hysteresis
State of charge
[SDE]Environmental Sciences
Rot mean squared error (RMSE)
business
Voltage
Zdroj: Electric Power Systems Research
Electric Power Systems Research, Elsevier, 2017, 146, pp.189-197. ⟨10.1016/j.epsr.2017.01.032⟩
ISSN: 0378-7796
DOI: 10.1016/j.epsr.2017.01.032
Popis: International audience; This paper presents an intelligent state of charge (SOC) and state of health (SOH) estimation method for lithium-ion batteries using an input time-delayed neural network. Unlike other estimation strategies, this technique requires no prior knowledge of the battery's model or parameters. Instead, it uses ambient temperature variations and previous battery's voltage and current data to accurately predict its SOC and SOH. The presented method compensates for the nonlinear patterns in battery characteristics such as hysteresis, variance due to electrochemical properties, and battery degradation due to aging. This technique is evaluated using a LiFePO 4 battery and experimental results highlight its high accuracy, simplicity, and robustness.
Databáze: OpenAIRE
Externí odkaz: