State-of-charge estimation based on model-adaptive Kalman filters

Locorotondo, Edoardo; Lutzemberger, Giovanni; Pugi, Luca

doi:10.1177/0959651820965406

Cited by 11 publications

(9 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researches have shown that factors such as temperature, charge/discharge rate have a great influence on battery life. Pugi and coauthors 22 presented a set of algorithms utilizing the nonlinear Kalman filter for estimating the battery SOC. Wang 23 developed a semi-empirical model to describe the life loss of Li-ion batteries based on experimental data, but this model is limited to specific charge/discharge conditions (e.g.…”

Section: Vehicle Modelsmentioning

confidence: 99%

A multi-objective regenerative braking control strategy combining with velocity optimization for connected vehicles

Liu

Han

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

Deceleration is unavoidable owing to the traffic lights or obstacles during driving, which lead to great energy dissipation. To promote energy utilizing efficiency, regenerative braking is applied to convert kinetic energy into electricity. Previous researches about regenerative braking concentrate on the braking torque allocation optimization, which is undoubtedly important. Moreover, the control performance can be further improved combining with velocity optimization. With the emerging Connected Vehicle Technology, environmental information is available for vehicles. Therefore, the terminal braking distance and terminal velocity can be derived utilizing the information. Then a multi-objective regenerative braking control strategy based on the pesudospectral method is proposed with the terminal constraints. The control strategy optimizes both the velocity and braking torque allocation to reduce the energy dissipation and battery capacity loss simultaneously. Simulations are carried out under a speed bump scenario. Pareto front is obtained with different weights of the objectives and then analyzed to reveal the tradeoff between energy recovery and battery health, which assists in finding a desired balanced solution. Two representative Pareto optimal solutions are selected for comparison: the energy recovery priory strategy and the energy-life tradeoff strategy. Compared with the energy priory strategy, the energy-life tradeoff strategy decreases the battery capacity loss by 60.65%, but it leads to a 36.07% reduction in the energy recovery.

show abstract

Section: Vehicle Modelsmentioning

confidence: 99%

A multi-objective regenerative braking control strategy combining with velocity optimization for connected vehicles

Liu

Han

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…In the article, the forgetting factor algorithm is introduced to improve the accuracy of parameter identification and then improve the estimation accuracy. The equation of state for the system is shown in Equation (8).…”

Section: Soc and Soh Joint Estimation Based On Forgetting Factor Dual...mentioning

confidence: 99%

“…SOC can directly reflect the remaining power of the battery, which is a significant parameter to reflect the state of the battery 7 . For the estimation, the main methods taken advantage of are the ampere‐hour integration, 8 open‐circuit voltage (OCV), Kalman filter, and neural network methods 9 …”

Section: Introductionmentioning

confidence: 99%

“…6 SOC can directly reflect the remaining power of the battery, which is a significant parameter to reflect the state of the battery. 7 For the estimation, the main methods taken advantage of are the ampere-hour integration, 8 open-circuit voltage (OCV), Kalman filter, and neural network methods. 9 The ampere-hour integration method linearizes the nonlinear relationships inside the Li-ion power battery 10 and estimates the electric quantity data of the charge and discharge within a spell time through measurement of voltage and current 11 ; the SOC of the current moment is estimated according to the value of the battery at the previous moment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel co‐estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium‐ion battery

Ren

Wang

Huang

et al. 2021

Intl J of Energy Research

View full text Add to dashboard Cite

show abstract

“…The economy is another very important aspect that will determine whether they can survive in the market competition. Meanwhile, the real-time and accurate estimation of the state of charge (SOC) of the battery would have an impact on the economy of the hybrid electric vehicle [33,34]. Notwithstanding, considering the significant importance and wide range of applications, many challenges remain incapable in applications and cognizance to characterization and battery managements for lithium-ion battery technology [35].…”

Section: Introductionmentioning

confidence: 99%

A State of Charge Estimation of The Lithium-Ion Batteries Based on Reduced-Order Unscented Kalman Filtering Method

2021

JETP

View full text Add to dashboard Cite

State of Charge (SOC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of lithium-ion batteries. SOC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and distinct nonlinear behavior. This paper compares two of the basic methods and algorithms for SOC estimation of lithium-ion batteries (LIBs) focusing on the description of the two techniques in a test experiment and the elaboration of their differences for use in battery management systems (BMS) applications. A Reduced-order unscented Kalman filter method is used for estimation and tracking to realizer real-time high-precision estimation of lithium-ion battery state of charge. Experimental tests are carried out with a lithium-ion battery cell for model and state estimation validations. Many researchers have proposed different methods of estimating SOC that raised the challenge of establishing a relationship between the accuracy and robustness of the method. The experimental results of the OCV-SOC estimation method, Hybrid Pulse Power Characterization (HPPC) test, and Beijing Bus Dynamic Stress Test (BBDST) working condition method are analyzed. The error of SOC estimation based on the established Thevenin RC modeling using the Reduced-order-unscented Kalman filter is less than 0.3%. The result from the use of the reduced-order unscented Kalman filtering algorithm proves that it has high accuracy in the state of charge estimation of the lithium-ion batteries.

show abstract

State-of-charge estimation based on model-adaptive Kalman filters

Cited by 11 publications

References 41 publications

A multi-objective regenerative braking control strategy combining with velocity optimization for connected vehicles

A multi-objective regenerative braking control strategy combining with velocity optimization for connected vehicles

Novel co‐estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium‐ion battery

A State of Charge Estimation of The Lithium-Ion Batteries Based on Reduced-Order Unscented Kalman Filtering Method

Contact Info

Product

Resources

About