Estimation of lithium‐ion battery state of charge for electric vehicles using a nonlinear state observer

Sakile, Rajakumar; Sinha, Umesh Kumar

doi:10.1002/est2.290

Cited by 5 publications

(4 citation statements)

References 41 publications

(79 reference statements)

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“…To model the electric performance of the LIB as well as the thermal losses, the equivalent circuit model (ECM) shown in Figure 7 was used. The ECM consists of a voltage source, resistors representing ohmic and polarization resistances, and capacitors representing polarization capacitances within the battery cell [38]. Measurements on a battery cell were carried out and used to fit the ECM.…”

Section: Modeling Of the Batterymentioning

confidence: 99%

A Data-Driven Approach to Ship Energy Management: Incorporating Automated Tracking System Data and Weather Information

Ünlübayir,

Mierendorff,

Börner

et al. 2023

JMSE

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This research paper presents a data-based energy management method for a vessel that predicts the upcoming load demands based on data from weather information and its automated tracking system. The vessel is powered by a hybrid propulsion system consisting of a high-temperature fuel cell system to cover the base load and a battery system to compensate for the fuel cell’s limited dynamic response capability to load fluctuations. The developed energy management method predicts the load demand of the next time steps by analyzing physical relationships utilizing operational and positional data of a real vessel. This allows a steadier operation of the fuel cell and reduces stress factors leading to accelerated aging and increasing the resource efficiency of the propulsion system. Since large ships record tracking data of their cruise and no a priori training is required to adjust the energy management, the proposed method can be implemented with small additional computational effort. The functionality of the energy management method was verified using data from a real ship and records of the water currents in the North Sea. The accuracy of the load prediction is 2.7% and the attenuation of the fuel cell’s power output could be increased by approximately 32%.

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Section: Modeling Of the Batterymentioning

confidence: 99%

A Data-Driven Approach to Ship Energy Management: Incorporating Automated Tracking System Data and Weather Information

Ünlübayir,

Mierendorff,

Börner

et al. 2023

JMSE

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“…There are different methods for modeling the LIB 31,32 . Electrochemical modeling and equivalent circuit modeling are two typical battery modeling methods 33 . The electrochemical approach cannot precisely estimate the SOC of a battery because it involves nonlinear equations and partial differential equations 34 .…”

Section: Battery Modelingmentioning

confidence: 99%

“…31,32 Electrochemical modeling and equivalent circuit modeling are two typical battery modeling methods. 33 The electrochemical approach cannot precisely estimate the SOC of a battery because it involves nonlinear equations and partial differential equations. 34 However, due to the rapid advancement of the LIB electrochemical model, it is now possible to use an advanced BMS-based electrochemical model to implement effective, secure, and reliable control techniques for LIB on board.…”

Section: Battery Modelingmentioning

confidence: 99%

State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

2022

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Lithium‐ion batteries (LIBs) are currently the most widely used energy storage technologies in electric vehicles (EVs) due to their higher power density, greater energy density, longer life, fast charging capabilities, and low discharge rate. The brain of rechargeable batteries is referred to be the battery management system (BMS). The operation of the battery is monitored & regulated by an electronic system called BMS. Most significantly, it prevents the battery from exceeding its safety limits. One of the important parameters of BMS is the state of charge (SOC); it determines the battery's remaining capacity of accessible stored energy, battery lifespan, and cell balancing. As a result, an accurate SOC calculation is essential for BMS, and it is used for safer operations that avoid overcharging and discharging for the optimum battery life. The primary ambition of this article is to find an accurate and robust SOC estimation based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques. RMS error, average error, minimum error, and maximum error are used to compare the EKF method and UKF method. Two resistor‐capacitor (2RC) equivalent circuit model has been considered for the dynamic performance of the battery. The waveforms are displayed, and errors are simulated for SOC estimate using the EKF and UKF algorithms, taking the same initial SOC into consideration at a time. The UKF algorithm outperforms the EKF approach in terms of dynamic results when comparing the two methods.

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“…In reference [19], an algorithm for estimating battery SOC based on H infinity was proposed. Reference [20] proposes some design methods for nonlinear observers to estimate SOC. In addition, there are also some data-driven estimation algorithms such as neural network algorithm [21], support vector machine method [22], genetic algorithm [23], and artificial intelligence algorithm [24].…”

Section: Introductionmentioning

confidence: 99%

An Improved Multi innovation Adaptive Robust Dual Kalman Filter Algorithm for Estimating Battery State

guan,

Yang,

et al. 2023

Preprint

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In response to the inaccurate estimation of State of Charge (SOC) in current power battery management systems, and considering that SOC may be subject to offset constraints from State of Health (SOH) when estimated separately. A method combining adaptive robust Kalman filtering with multiple innovation theories and online identification of dual Kalman filtering parameters is proposed. This control method is based on adaptive robust Kalman control. It corrects the estimated values using multiple innovation values and Kalman gains at different times. Dual Kalman filtering is used for online parameter identification and joint estimation of battery health status, which increases the amount of error information and provides an optimized method for accurate estimation of SOC and SOH. To verify the rationality of the algorithm, a second-order RC equivalent circuit model is used to characterize the dynamic characteristics of the battery, and experimental verification is carried out under different operating conditions. The experimental results show that the average error of SOC under three operating conditions: UDDS, FUDS, and US06 is 0.56\%, 0.31\%, and 1.23\%, respectively. The estimated error of SOH after stabilization is less than 1.73\%. The estimation error is the lowest among the five estimation algorithms. The proposed algorithm has been verified to have good accuracy and convergence. The multi-innovation adaptive robust dual Kalman filtering algorithm provides a theoretical basis for accurate state estimation and widespread application of lithium batteries.

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Estimation of lithium‐ion battery state of charge for electric vehicles using a nonlinear state observer

Cited by 5 publications

References 41 publications

A Data-Driven Approach to Ship Energy Management: Incorporating Automated Tracking System Data and Weather Information

A Data-Driven Approach to Ship Energy Management: Incorporating Automated Tracking System Data and Weather Information

State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

An Improved Multi innovation Adaptive Robust Dual Kalman Filter Algorithm for Estimating Battery State

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