scholarly journals An Evaluation of the Moving Horizon Estimation Algorithm for Online Estimation of Battery State of Charge and State of Health

2014 ◽  
Author(s):  
Bibin Pattel ◽  
Hoseinali Borhan ◽  
Sohel Anwar
Keyword(s):  
Initial Conditions ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Of Charge ◽  
Convergence Time ◽  
Moving Horizon Estimation ◽  
State Of Health ◽  
Estimation Problems ◽  
Comparison Of The Results

Moving Horizon Estimation (MHE) has emerged as a powerful technique for tackling the estimation problems of the state of dynamic systems in the presence of constraints, nonlinearities and disturbances. In this paper, the Moving Horizon Estimation approach is applied in estimating the State of Charge (SoC) and State of Health (SoH) of a battery and the results are compared against those for the traditional estimation method of Extended Kalman Filter (EKF). The comparison of the results show that MHE provides improvement in performance over EKF in terms of different state initial conditions, convergence time, and process and sensor noise variations.

A Review of Research on the State-of-Charge Estimation Methods for Electric Vehicle Battery

2019 ◽  
Vol 13 ◽  
Author(s):  
Mingyue Zhang ◽  
Xiaobin Fan ◽  
Jing Gan ◽  
Zeng Song ◽  
Bin Zhao
Keyword(s):  
Control Theory ◽  
Electric Vehicle ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Of Charge ◽  
Estimation Methods ◽  
Advantages And Disadvantages ◽  
Electric Vehicle Battery ◽  
State Of Charge Estimation

Background: Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time change and non-linear properties, which are extremely complex systems. Therefore, accurate estimating the state of charge is a challenging thing. Objective: The study aims to report the latest progress in the studies of the state-of-charge estimation methods for electric vehicle battery. Methods: This paper reviews various representative patents and papers related to the state of charge estimation methods for electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional estimation algorithm based on the battery experiment, the estimation algorithm based on modern control theory and other estimation algorithm based on the innovative ideas, especially focusing on the algorithms based on control theory. Results: The advantages and disadvantages, current and future developments of the state-of-charge estimation methods are finally provided and discussed. Conclusion: Each kind of state of charge estimation method has its own characteristics, suitable for different occasions. At present, algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish rich database, improve hardware technology, put up with more perfect battery model, and give full play to the advantages of each algorithm.

A Strategy for Estimating State-of-Charge and State-of-Health of Li-Ion Batteries in Electric and Hybrid Electric Vehicles

2012 ◽  
Author(s):  
Xiaowei Zhao ◽  
Guoyu Zhang ◽  
Lin Yang
Keyword(s):  
Equivalent Circuit ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Circuit Model ◽  
State Of Charge ◽  
State Of Health ◽  
Li Ion Batteries ◽  
Soc Estimation ◽  
Kaiman Filtering ◽  
Li Ion

A task that has to be solved for the application of batteries in vehicles with an electric drive train is the determination of the actual state-of-health (SOH) and state-of-charge (SOC) of the battery cells. In this paper, an on board strategy for estimating SOC and SOH of Li-ion batteries is proposed. The equivalent circuit model is used for both SOC and SOH estimations. In SOH algorithm, the estimated value of battery capacity not only reflects the aging degree of battery pack, but also provides information for SOC estimation. Meanwhile, the extended Kaiman filtering is used in SOC estimation. Because the performance of the equivalent circuit model will be better at small currents than at high currents, extended Kaiman filtering is substituted by Ampere-Hour counting when the absolute value of current is greater than a calibration value. The Digatron battery tester was used to evaluate the proposed estimation method, and results show that the estimation method has high accuracy and efficiency at ordinary temperatures.

Sampled-Data Observer for Estimating the State of Charge, State of Health, and Temperature of Batteries

2021 ◽  
pp. 1-13
Author(s):  
Khawla Gaouzi ◽  
Hassan El Fadil ◽  
Aziz Rachid ◽  
Abdellah Lassioui ◽  
Zakariae El Idrissi ◽  
...  
Keyword(s):  
Charge State ◽  
The State ◽  
State Of Charge ◽  
State Of Health ◽  
Sampled Data

scholarly journals Procedure for Selecting a Transmission Mode Dependent on the State-of-charge and State-of-health of a Lithium-ion Battery in Wireless Sensor Networks with Energy Harvesting Devices

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Vanessa Quintero ◽  
Aramis Perez ◽  
Francisco Jaramillo ◽  
Claudio Estevez ◽  
Marcos Orchard
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Wireless Sensor ◽  
State Of Health ◽  
Medium Access Control Protocols ◽  
Energy Harvesting Devices

Diverse methods and considerations have been proposed to manage the available energy in an efficient manner in Wireless Sensor Networks. By incorporating Energy Harvesting Devices in these type of networks it is possible to reduce the dependency of the availability of the Energy Storage Devices, particularly the lithium-ion battery. Recently, the State-of-Charge and State-of-Health of the battery have been considered as inputs for the design of the Medium- Access-Control protocols for Wireless Sensor Networks. In this article, different guidelines are proposed for the design of Medium-Access-Control protocols used in Wireless Sensor Networks with Energy Harvesting Devices considering the State-of-Charge and State-of-Health as indicators for the estimation of the transmission time of the sensor node. The proposed guidelines consider different currents used during the transmission to estimate the State-of-Charge and Stateof- Health of the battery. The incorporation of these indicators aim to improve the decision-making process of the sensor node when transmitting.

scholarly journals Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2828
Author(s):  
Sara Luciani ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Andrea Tonoli
Keyword(s):  
Lithium Ion Batteries ◽  
Real Time ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Data Driven ◽  
Raspberry Pi ◽  
Hardware In The Loop

This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (SOC) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion battery that is numerically modeled via an RC equivalent circuit model that can also consider the environmental temperature influence. The proposed estimation technique relies on nonlinear autoregressive with exogenous input (NARX) artificial neural networks (ANNs) that are properly trained with multiple datasets. Those datasets include modeled current and voltage data, both for charge-sustaining and charge-depleting working conditions. The investigated method is then experimentally validated using a Raspberry Pi 4B card-sized board, on which the estimation algorithm is actually deployed, and real-time hardware, on which the battery model is developed, namely a Speedgoat baseline platform. These hardware platforms are used in a hardware-in-the-loop architecture via the UPD communication protocol, allowing the system to be validated in a proper testing environment. The resulting estimation algorithm can estimate the battery SOC in real-time, with 2% accuracy during real-time hardware testing.

scholarly journals Performance analysis of supercapacitors for transportation industry

2019 ◽  
Vol 13 (3) ◽  
pp. 1031
Author(s):  
Vinoth Jonathan Nagarajah ◽  
Hui Jing Lee ◽  
King Guan Tan ◽  
Nathawat Khunprasit
Keyword(s):  
Energy Storage ◽  
Performance Analysis ◽  
Storage System ◽  
Energy Storage System ◽  
The State ◽  
State Of Charge ◽  
Monitoring Device ◽  
State Of Health ◽  
Transportation Industry ◽  
Online Platform

<span>Monitoring device is essential to ensure a reliable and a healthy lifespan of the energy storage system. Hence, a monitoring device is needed to monitor the state of health and state of charge of a Supercapacitor. This project aims to demonstrate a method to monitor Supercapacitors using a microcontroller in both hardware and software approaches. The data was successfully collected by an online platform called ThingSpeak.</span>

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