Paul Takyi-Aninakwa | Southwest University of Science and Technology (original) (raw)

Paul Takyi-Aninakwa

I am currently a student with good knowledge in sliding friction reduction and states estimation of lithium-ion batteries.

less

Uploads

Papers by Paul Takyi-Aninakwa

Research paper thumbnail of Battery lumped fractional‐order hysteresis thermoelectric coupling model for state of charge estimation adaptive to time‐varying core temperature conditions

International journal of circuit theory and applications, Jun 23, 2024

Research paper thumbnail of A review of data-driven whole-life state of health prediction for lithium-ion batteries: data preprocessing, aging characteristics, algorithms, and future challenges

Journal of Energy Chemistry/Journal of energy chemistry, Jun 1, 2024

Research paper thumbnail of Improved Multiple Feature-Electrochemical Thermal Coupling Modeling of Lithium-Ion Batteries at Low-Temperature with Real-Time Coefficient Correction

Protection and control of modern power systems, May 1, 2024

Research paper thumbnail of Critical Review on Improved Electrochemical Impedance Spectroscopy-Cuckoo Search-Elman Neural Network Modeling Methods for Whole-Life-Cycle Health State Estimation of Lithium-Ion Battery Energy Storage Systems

Protection and control of modern power systems, Mar 1, 2024

Research paper thumbnail of Improved particle swarm optimization–long short-term memory model with temperature compensation ability for the accurate state of charge estimation of lithium-ion batteries

Journal of energy storage, Apr 1, 2024

Research paper thumbnail of An enhanced lithium-ion battery state-of-charge estimation method using long short-term memory with an adaptive state update filter incorporating battery parameters

Engineering applications of artificial intelligence, Jun 1, 2024

Research paper thumbnail of An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries

Journal of Energy Storage, Feb 29, 2024

Research paper thumbnail of Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data

Applied energy, Jun 1, 2024

Research paper thumbnail of Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference

Journal of Energy Storage, Nov 30, 2023

Research paper thumbnail of State of charge estimation of ternary lithium-ion batteries at variable ambient temperatures

International Journal of Electrochemical Science

Research paper thumbnail of A hybrid-aided approach with adaptive state update for estimating the state-of-charge of LiFePO4 batteries considering temperature uncertainties

Journal of Energy Storage

Research paper thumbnail of A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

International Journal of Energy Research

Research paper thumbnail of Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-current variations

Research paper thumbnail of An ASTSEKF optimizer with nonlinear condition adaptability for accurate SOC estimation of lithium-ion batteries

Journal of Energy Storage

Research paper thumbnail of An improved sliding window - long short-term memory modeling method for real-world capacity estimation of lithium-ion batteries considering strong random charging characteristics

Journal of Energy Storage

Research paper thumbnail of A Novel Seasonal Autoregressive Integrated Moving Average Method for the Accurate Lithium-ion Battery Residual Life Prediction

International Journal of Electrochemical Science

Lithium-ion batteries are widely used in electric vehicles (EVs), unmanned aerial vehicles (UAVs)... more Lithium-ion batteries are widely used in electric vehicles (EVs), unmanned aerial vehicles (UAVs), and smart devices because of their high specific energy, long service life, and environmental-friendly. The remaining useful life (RUL) prediction is extremely important for the evaluation of the state of health (SOH) of the battery. Also, it is an important indicator to improve its safety in a variety of applications. In this study, a novel seasonal autoregressive integrated moving average (SARIMA) prediction model is proposed. The proposed model adds periodic parameter optimization to fit the nonlinear characteristics of the battery, including maximum likelihood estimation (MLE) and Akaike information criterion (AIC) to filter the parameters more accurately. So, to effectively solve the shortcomings of the traditional prediction methods, such as complex parameter acquisition, low prediction accuracy, and a large amount of sample data. The method proposed in this paper simplifies the remaining useful life prediction process, ensures high accuracy, and improves the safe operation and reliability of lithium-ion batteries. The model can give the prediction confidence bounds, and the maximum prediction error under complex working conditions is 4.62%.

Research paper thumbnail of Improved Fixed Range Forgetting Factor-Adaptive Extended Kalman Filtering (FRFF-AEKF) Algorithm for the State of Charge Estimation of High-Power Lithium-Ion Batteries

International Journal of Electrochemical Science

The lithium-ion battery is perhaps the most powerful energy storage media available today and is ... more The lithium-ion battery is perhaps the most powerful energy storage media available today and is used in virtually all electronic devices, especially electric and hybrid electric vehicles. The battery industry is growing rapidly in battery technology, development, and production to meet future demands. The difficulty in estimating battery states such as the state of charge (SOC) has led to the discovery of several methods and techniques. The use of improved algorithms coupled with a combination of methods and models has contributed immensely toward the accurate estimation of battery states. In this paper, the state of charge of the high-power lithium-ion battery is estimated based on an improved Fixed Range Forgetting Factor-Adaptive Extended Kalman filtering (FRFF-AEKF) algorithm. The interference of system noise is overcome with the use of the fixed range forgetting factor and the Saga-Husa adaptive filter (SHAF) to calculate the SOC more accurately. The experiments performed for the acquisition of data, parameterization, and verification of results, the methods employed and the use of the improved algorithm were all done to accurately estimate the SOC. Two other algorithms, the Adaptive extended Kalman filtering (AEKF) algorithm, and the Adaptive Unscented Kalman filtering (AUKF) algorithm are used as benchmarks for verifying the performance of the improved FRFF-AEKF algorithm. The improved FRFF-AEKF algorithm achieved 99.74 % estimation accuracy under Hybrid Pulse Power Characterization (HPPC) test working conditions and 99.44 % under Beijing Bus Dynamic stress test (BBDST) working conditions. The estimation accuracy of the AEKF algorithm under HPPC and BBDST conditions was 98.37% and 99.27% respectively, and the estimation accuracy of the AUKF algorithm under HPPC and BBDST conditions was 97.97% and 99.07% respectively. The verification experiment proved that the method was successful and can accurately estimate the state of charge of the high-power lithium-ion battery.

Research paper thumbnail of A NARX network optimized with an adaptive weighted square-root cubature Kalman filter for the dynamic state of charge estimation of lithium-ion batteries

Journal of Energy Storage

Research paper thumbnail of Dynamic adaptive square-root unscented Kalman filter and rectangular window recursive least square method for the accurate state of charge estimation of lithium-ion batteries

Journal of Energy Storage

Research paper thumbnail of Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Journal of The Electrochemical Society

Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. Sta... more Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. State of charge (SOC) estimation is one of the key functions of the battery management system (BMS). Accurate SOC estimation helps to determine the driving range and effective energy management of electric vehicles (EVs). However, due to complex electrochemical reactions and nonlinear battery characteristics, accurate SOC estimation is challenging. Therefore, this review examines the existing methods for estimating the SOC of LIBs and analyzes their respective advantages and disadvantages. Subsequently, a systematic and comprehensive analysis of the methods for constructing LIB models is conducted from various aspects such as applicability and accuracy. Finally, the advantages of particle filtering (PF) over the Kalman filter (KF) series algorithm for estimating SOC are summarized, and various improved PF algorithms for estimating the SOC of LIBs are compared and discussed. Additionally, th...

Research paper thumbnail of Battery lumped fractional‐order hysteresis thermoelectric coupling model for state of charge estimation adaptive to time‐varying core temperature conditions

International journal of circuit theory and applications, Jun 23, 2024

Research paper thumbnail of A review of data-driven whole-life state of health prediction for lithium-ion batteries: data preprocessing, aging characteristics, algorithms, and future challenges

Journal of Energy Chemistry/Journal of energy chemistry, Jun 1, 2024

Research paper thumbnail of Improved Multiple Feature-Electrochemical Thermal Coupling Modeling of Lithium-Ion Batteries at Low-Temperature with Real-Time Coefficient Correction

Protection and control of modern power systems, May 1, 2024

Research paper thumbnail of Critical Review on Improved Electrochemical Impedance Spectroscopy-Cuckoo Search-Elman Neural Network Modeling Methods for Whole-Life-Cycle Health State Estimation of Lithium-Ion Battery Energy Storage Systems

Protection and control of modern power systems, Mar 1, 2024

Research paper thumbnail of Improved particle swarm optimization–long short-term memory model with temperature compensation ability for the accurate state of charge estimation of lithium-ion batteries

Journal of energy storage, Apr 1, 2024

Research paper thumbnail of An enhanced lithium-ion battery state-of-charge estimation method using long short-term memory with an adaptive state update filter incorporating battery parameters

Engineering applications of artificial intelligence, Jun 1, 2024

Research paper thumbnail of An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries

Journal of Energy Storage, Feb 29, 2024

Research paper thumbnail of Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data

Applied energy, Jun 1, 2024

Research paper thumbnail of Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference

Journal of Energy Storage, Nov 30, 2023

Research paper thumbnail of State of charge estimation of ternary lithium-ion batteries at variable ambient temperatures

International Journal of Electrochemical Science

Research paper thumbnail of A hybrid-aided approach with adaptive state update for estimating the state-of-charge of LiFePO4 batteries considering temperature uncertainties

Journal of Energy Storage

Research paper thumbnail of A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

International Journal of Energy Research

Research paper thumbnail of Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-current variations

Research paper thumbnail of An ASTSEKF optimizer with nonlinear condition adaptability for accurate SOC estimation of lithium-ion batteries

Journal of Energy Storage

Research paper thumbnail of An improved sliding window - long short-term memory modeling method for real-world capacity estimation of lithium-ion batteries considering strong random charging characteristics

Journal of Energy Storage

Research paper thumbnail of A Novel Seasonal Autoregressive Integrated Moving Average Method for the Accurate Lithium-ion Battery Residual Life Prediction

International Journal of Electrochemical Science

Lithium-ion batteries are widely used in electric vehicles (EVs), unmanned aerial vehicles (UAVs)... more Lithium-ion batteries are widely used in electric vehicles (EVs), unmanned aerial vehicles (UAVs), and smart devices because of their high specific energy, long service life, and environmental-friendly. The remaining useful life (RUL) prediction is extremely important for the evaluation of the state of health (SOH) of the battery. Also, it is an important indicator to improve its safety in a variety of applications. In this study, a novel seasonal autoregressive integrated moving average (SARIMA) prediction model is proposed. The proposed model adds periodic parameter optimization to fit the nonlinear characteristics of the battery, including maximum likelihood estimation (MLE) and Akaike information criterion (AIC) to filter the parameters more accurately. So, to effectively solve the shortcomings of the traditional prediction methods, such as complex parameter acquisition, low prediction accuracy, and a large amount of sample data. The method proposed in this paper simplifies the remaining useful life prediction process, ensures high accuracy, and improves the safe operation and reliability of lithium-ion batteries. The model can give the prediction confidence bounds, and the maximum prediction error under complex working conditions is 4.62%.

Research paper thumbnail of Improved Fixed Range Forgetting Factor-Adaptive Extended Kalman Filtering (FRFF-AEKF) Algorithm for the State of Charge Estimation of High-Power Lithium-Ion Batteries

International Journal of Electrochemical Science

The lithium-ion battery is perhaps the most powerful energy storage media available today and is ... more The lithium-ion battery is perhaps the most powerful energy storage media available today and is used in virtually all electronic devices, especially electric and hybrid electric vehicles. The battery industry is growing rapidly in battery technology, development, and production to meet future demands. The difficulty in estimating battery states such as the state of charge (SOC) has led to the discovery of several methods and techniques. The use of improved algorithms coupled with a combination of methods and models has contributed immensely toward the accurate estimation of battery states. In this paper, the state of charge of the high-power lithium-ion battery is estimated based on an improved Fixed Range Forgetting Factor-Adaptive Extended Kalman filtering (FRFF-AEKF) algorithm. The interference of system noise is overcome with the use of the fixed range forgetting factor and the Saga-Husa adaptive filter (SHAF) to calculate the SOC more accurately. The experiments performed for the acquisition of data, parameterization, and verification of results, the methods employed and the use of the improved algorithm were all done to accurately estimate the SOC. Two other algorithms, the Adaptive extended Kalman filtering (AEKF) algorithm, and the Adaptive Unscented Kalman filtering (AUKF) algorithm are used as benchmarks for verifying the performance of the improved FRFF-AEKF algorithm. The improved FRFF-AEKF algorithm achieved 99.74 % estimation accuracy under Hybrid Pulse Power Characterization (HPPC) test working conditions and 99.44 % under Beijing Bus Dynamic stress test (BBDST) working conditions. The estimation accuracy of the AEKF algorithm under HPPC and BBDST conditions was 98.37% and 99.27% respectively, and the estimation accuracy of the AUKF algorithm under HPPC and BBDST conditions was 97.97% and 99.07% respectively. The verification experiment proved that the method was successful and can accurately estimate the state of charge of the high-power lithium-ion battery.

Research paper thumbnail of A NARX network optimized with an adaptive weighted square-root cubature Kalman filter for the dynamic state of charge estimation of lithium-ion batteries

Journal of Energy Storage

Research paper thumbnail of Dynamic adaptive square-root unscented Kalman filter and rectangular window recursive least square method for the accurate state of charge estimation of lithium-ion batteries

Journal of Energy Storage

Research paper thumbnail of Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Journal of The Electrochemical Society

Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. Sta... more Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. State of charge (SOC) estimation is one of the key functions of the battery management system (BMS). Accurate SOC estimation helps to determine the driving range and effective energy management of electric vehicles (EVs). However, due to complex electrochemical reactions and nonlinear battery characteristics, accurate SOC estimation is challenging. Therefore, this review examines the existing methods for estimating the SOC of LIBs and analyzes their respective advantages and disadvantages. Subsequently, a systematic and comprehensive analysis of the methods for constructing LIB models is conducted from various aspects such as applicability and accuracy. Finally, the advantages of particle filtering (PF) over the Kalman filter (KF) series algorithm for estimating SOC are summarized, and various improved PF algorithms for estimating the SOC of LIBs are compared and discussed. Additionally, th...

Log In