scholarly journals Fuzzy Logic Implementation for Accurate Electric Car Battery SoC Measurement

2021 ◽  
Vol 10 (3) ◽  
pp. 257-264
Author(s):  
Muhammad Dzaky Ashidqi ◽  
Miftahul Anwar ◽  
Chico Hermanu B.A. ◽  
Agus Ramelan ◽  
Feri Adriyanto
Keyword(s):  
Fuzzy Logic ◽  
Equilibrium Constant ◽  
Chemical Equilibrium ◽  
Estimation Error ◽  
Logic Program ◽  
Previous Method ◽  
Influence Of Temperature ◽  
Soc Estimation ◽  
Electric Car ◽  
Arduino Microcontroller

Changes in temperature can affect the accuracy of the estimated SoC value based on voltage. In this study, fuzzy logic was implemented to correct the SoC estimation error caused by the influence of temperature. The system acquired data through sensors and then processed it using the Arduino microcontroller. Parameters in the form of voltage, temperature, and current were processed by Arduino with a fuzzy logic program which was uploaded into it and produced the output of the estimated SoC value. From the observations, it was found that the estimated SoC value from this method had better accuracy with a smaller error than the SoC estimation based on voltage alone. Using the RMSE method, the errors calculated in this method in the process of charging and discharging without running were 2.26 and 7.74, while the SoC estimation error based on voltage alone reached 4.88 and 12.8. In the discharging process with a running car, the SoC estimation results using fuzzy logic also showed accurate results. There was only 1% of SoC value increasing pattern during the discharging process, which the value trend should continue to decrease and should not be an increase. In addition, compared to the previous method applied to the same research object, namely the chemical equilibrium constant method, this method also showed more accurate results.

scholarly journals State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2491 ◽  
Author(s):  
Wenhui Zheng ◽  
Bizhong Xia ◽  
Wei Wang ◽  
Yongzhi Lai ◽  
Mingwang Wang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Sliding Mode ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Sliding Mode Observer ◽  
State Of Charge ◽  
Battery Management System ◽  
Test Results ◽  
Soc Estimation

State of charge (SOC) estimation is of vital importance for the battery management system in electric vehicles. This paper proposes a new fuzzy logic sliding mode observer for SOC estimation. The second-order resistor-capacitor equivalent circuit model is used to describe the discharging/charging behavior of the battery. The exponential fitting method is applied to determine the parameters of the model. The fuzzy logic controller is introduced to improve the performance of sliding mode observer forming the fuzzy logic sliding mode observer (FLSMO). The Federal Urban Driving Schedule (FUDS), the West Virginia Suburban Driving Schedule (WUBSUB), and the New European Driving Cycle (NEDC) schedule test results show that the average SOC estimation error of FLSMO algorithm is less than 1%. When the initial SOC estimation error is 20%, the FLSMO algorithm can converge to 3% error boundary within 2400 s. Comparison test results show that the FLSMO algorithm has better performance than the sliding mode observer and the extended Kalman filter in terms of robustness against measurement noise and parameter disturbances.

Chemical equilibrium and chemical kinetics

2018 ◽  
Author(s):  
Dennis Sherwood ◽  
Paul Dalby
Keyword(s):  
Free Energy ◽  
Equilibrium Constant ◽  
Gibbs Free Energy ◽  
Chemical Kinetics ◽  
Chemical Equilibrium ◽  
First Principles ◽  
Effect Of Temperature ◽  
Total System ◽  
Free Energies

Building on the previous chapter, this chapter examines gas phase chemical equilibrium, and the equilibrium constant. This chapter takes a rigorous, yet very clear, ‘first principles’ approach, expressing the total Gibbs free energy of a reaction mixture at any time as the sum of the instantaneous Gibbs free energies of each component, as expressed in terms of the extent-of-reaction. The equilibrium reaction mixture is then defined as the point at which the total system Gibbs free energy is a minimum, from which concepts such as the equilibrium constant emerge. The chapter also explores the temperature dependence of equilibrium, this being one example of Le Chatelier’s principle. Finally, the chapter links thermodynamics to chemical kinetics by showing how the equilibrium constant is the ratio of the forward and backward rate constants. We also introduce the Arrhenius equation, closing with a discussion of the overall effect of temperature on chemical equilibrium.

Optimal model-free fuzzy logic control for autonomous unmanned aerial vehicle

2021 ◽  
pp. 095441002110253
Author(s):  
Hossam E Glida ◽  
Latifa Abdou ◽  
Abdelghani Chelihi ◽  
Chouki Sentouh ◽  
Gabriele Perozzi
Keyword(s):  
Fuzzy Logic ◽  
Unmanned Aerial Vehicle ◽  
Fuzzy Logic Control ◽  
Estimation Error ◽  
Design Parameters ◽  
Multiple Model ◽  
Optimal Model ◽  
Model Free ◽  
Logic Control ◽  
Aerial Vehicle

This article deals with the issue of designing a flight tracking controller for an unmanned aerial vehicle type of quadrotor based on an optimal model-free fuzzy logic control approach. The main design objective is to perform an automatic flight trajectory tracking under multiple model uncertainties related to the knowledge of the nonlinear dynamics of the system. The optimal control is also addressed taking into consideration unknown external disturbances. To achieve this goal, we propose a new optimal model-free fuzzy logic–based decentralized control strategy where the influence of the interconnection term between the subsystems is minimized. A model-free controller is firstly designed to achieve the convergence of the tracking error. For this purpose, an adaptive estimator is proposed to ensure the approximation of the nonlinear dynamic functions of the quadrotor. The fuzzy logic compensator is then introduced to deal with the estimation error. Moreover, the optimization problem to select the optimal design parameters of the proposed controller is solved using the bat algorithm. Finally, a numerical validation based on the Parrot drone platform is conducted to demonstrate the effectiveness of the proposed control method with various flying scenarios.

scholarly journals State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Kai Wang ◽  
Chunli Liu ◽  
Jianrui Sun ◽  
Kun Zhao ◽  
Licheng Wang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Kalman Filtering ◽  
Lithium Batteries ◽  
Estimation Error ◽  
Storage System ◽  
Energy Storage System ◽  
Least Square ◽  
Recursive Least Square ◽  
Soc Estimation ◽  
Composite Energy

This paper studies the state of charge (SOC) estimation of supercapacitors and lithium batteries in the hybrid energy storage system of electric vehicles. According to the energy storage principle of the electric vehicle composite energy storage system, the circuit models of supercapacitors and lithium batteries were established, respectively, and the model parameters were identified online using the recursive least square (RLS) method and Kalman filtering (KF) algorithm. Then, the online estimation of SOC was completed based on the Kalman filtering algorithm and unscented Kalman filtering algorithm. Finally, the experimental platform for SOC estimation was built and Matlab was used for calculation and analysis. The experimental results showed that the SOC estimation results reached a high accuracy, and the variation range of estimation error was [−0.94%, 0.34%]. For lithium batteries, the recursive least square method is combined with the 2RC model to obtain the optimal result, and the estimation error is within the range of [−1.16%, 0.85%] in the case of comprehensive weighing accuracy and calculation amount. Moreover, the system has excellent robustness and high reliability.

Chemical Equilibrium; Determination of an Equilibrium Constant

1966 ◽  
pp. 215-218
Author(s):  
FRANK BRESCIA ◽  
JOHN ARENTS ◽  
HERBERT MEISLICH ◽  
AMOS TURK
Keyword(s):  
Equilibrium Constant ◽  
Chemical Equilibrium

A Machine Learning Method for State of Charge Estimation in Lead-Acid Batteries for Heavy-Duty Vehicles

2021 ◽  
Author(s):  
Sara Luciani ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Andrea Tonoli ◽  
Nicola Amati ◽  
...  
Keyword(s):  
Machine Learning ◽  
Estimation Error ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Heavy Duty ◽  
Soc Estimation ◽  
Lead Acid Batteries ◽  
Heavy Duty Vehicles ◽  
Lead Acid

Abstract In the automotive framework, an accurate assessment of the State of Charge (SOC) in lead-acid batteries of heavy-duty vehicles is of major importance. SOC is a crucial battery state that is non-observable. Furthermore, an accurate estimation of the battery SOC can prevent system failures and battery damage due to a wrong usage of the battery itself. In this context, a technique based on machine learning for SOC estimation is presented in this study. Thus, this method could be used for safety and performance monitoring purposes in electric subsystem of heavy-duty vehicles. The proposed approach exploits a Genetic Algorithm (GA) in combination with Artificial Neural Networks (ANNs) for SOC estimation. Specifically, the training parameters of a Nonlinear Auto-Regressive with Exogenous inputs (NARX) ANN are chosen by the GA-based optimization. As a consequence of the GA-based optimization, the ANN-based SOC estimator architecture is defined. Then, the proposed SOC estimation algorithm is trained and validated with experimental datasets recorded during real driving missions performed by a heavy-duty vehicle. An equivalent circuit model representing the retained lead-acid battery is used to collect the training, validation and testing datasets that replicates the recorded experimental data related to electrical consumers and the cabin systems or during overnight stops in heavy-duty vehicles. This article illustrates the architecture of the proposed SOC estimation algorithm along with the identification procedure of the ANN parameters with GA. The method is able to estimate SOC with a low estimation error, being suitable for deployment on common on-board Battery Management Systems (BMS).

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