State of Charge estimation via extended Kalman filter designed for electrochemical equations

Afshar, Sepideh; Morris, Kirsten; Khajepour, Amir

doi:10.1016/j.ifacol.2017.08.269

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…Extended Kalman filter Extended Kalman filter is used when the transition and the measurement equations are not linear. A linearization process is applied at every time step to approximate the nonlinear system with a linear timevarying system (LTV) [46].The algorithm was widely used for battery modelling and states estimation [47,48] thanks to its accuracy and speed.…”

Section: Fig 3 Process Of Kalman Filtermentioning

confidence: 99%

Critical Review of Ageing Mechanisms and State of Health Estimation Methods for Battery Performance

Saqli

Bouchareb

Oudghiri

et al. 2019

Sustainability in Energy and Buildings

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Battery Management System (BMS) is an essential component for lithium-ion battery-based devices. It provides a variety of functionalities that help improve the overall lifespan of the battery, including states estimation algorithms. An accurate state of health is one of the essential features an advanced BMS provides, in order to track long term performance and ensure reliable operation of the battery. This paper aims to provide a structured review of ageing mechanisms that affect the battery performance and also some of the most relevant algorithms that were used in the recent literature to predict the actual health of the battery. Finally, and based on the different approaches that were studied evaluation criteria are proposed to help judge the performance of State Of Health (SOH) estimation algorithm.

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Section: Fig 3 Process Of Kalman Filtermentioning

confidence: 99%

Critical Review of Ageing Mechanisms and State of Health Estimation Methods for Battery Performance

Saqli

Bouchareb

Oudghiri

et al. 2019

Sustainability in Energy and Buildings

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show abstract

“…Also, the increasing number of mechatronic systems increases engine controllability and observability, yet it makes the controller design and calibration procedures time-consuming, tedious, and laborious. By recruiting powerful observers and estimators, however, scientists circumvent the mentioned flaw [7][8][9][10]. Nikzadfar and Shamekhi developed a novel model-based calibration procedure incorporated with a multi-objective Genetic Algorithm (GA) to optimize the performance and emissions of a diesel engine [11].…”

Section: Introductionmentioning

confidence: 99%

Development of an Extended Mean Value Model for Control-Oriented Modeling of Gasoline Engines Equipped with Continuously Variable Valve Timing

Bakhshinezhad¹,

Garivani²,

Sadeghi³

et al. 2022

SAE Technical Paper Series

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Model-based control system design is a well-established method for advanced engine control systems. These control systems maintain engine operation at levels that meet stringent environmental regulations on vehicular emissions. However, the models required for model-based design need to be accurate enough for design and pre-calibration and fast enough for optimization and implementation purposes. On the other hand, the variable valve timing (VVT) technology significantly affects the dynamic performance of internal combustion engines. This study aims at developing a control-oriented, extended mean-value model (EMVM) of a gasoline engine, taking into account the effects of VVT on the dynamic model. The developed model analyzes the engine's performance characteristics in transient and steady-state regimes. The engine model incorporates four peripheral, nonlinear, dynamic subsystems: manifold, fuel injection, wall-film adhesion, and evaporation processes. Moreover, lying at the core of the developed model is a nonlinear, static, in-cylinder process (ICP) model which simulates gas exchange and combustion processes based on the cylinder's boundary conditions. Based on the experimental data obtained from the engine test setup, an artificial neural network has been trained to predict the in-cylinder processes as a single model. The ICP model was integrated into the dynamic peripheral models to form the final EMVM model. The results of the developed model were compared to the engine experimental tests for two test scenarios: half-throttle and full-throttle cases. It was observed that the developed model could accurately simulate the engine speed, inlet air pressure, aspirated air mass, and exhaust temperature. Moreover, the EMVM model could successfully predict the effects of VVT in the performance of ICEs.

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“…Following this, for the nonlinear system, a series of algorithms such as the Extended Kalman Filter (EKF) and Adaptive Kalman Filter (AEKF) were derived. The core idea of these algorithms is to derive an optimal estimation of the output state of the battery system in the sense of minimum variance [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Model improvement and SOC estimation based on aluminium ion batteries

et al. 2021

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The main work of this paper can be divided into three parts: (1) An aluminium ion battery is made, the battery capacity is calibrated under a constant current, and the battery’s voltage and current data under pulse conditions is collected. (2) The influence of the hysteresis effect on the SOC estimation of aluminium ion batteries is fully considered. A model suitable for aluminium ion batteries is established based on the model that the parameter identification and verification was carried out with. (3) The error with the EKF and AEKF algorithms in estimating the SOC of aluminium ion batteries is compared. The experimental results show that the model established in this paper can describe the working state of aluminium ion batteries very well. Compared with the EKF algorithm, the AEKF algorithm is more accurate in estimating the SOC of aluminium ion batteries.

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State of Charge estimation via extended Kalman filter designed for electrochemical equations

Cited by 8 publications

References 29 publications

Critical Review of Ageing Mechanisms and State of Health Estimation Methods for Battery Performance

Critical Review of Ageing Mechanisms and State of Health Estimation Methods for Battery Performance

Development of an Extended Mean Value Model for Control-Oriented Modeling of Gasoline Engines Equipped with Continuously Variable Valve Timing

Model improvement and SOC estimation based on aluminium ion batteries

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