Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter

Li, Weihan; Yue, Fan; Ringbeck, Florian; Jöst, Dominik; Han, Xiao; Ouyang, Minggao; Sauer, Dirk Uwe

doi:10.1016/j.jpowsour.2020.228534

Cited by 175 publications

(50 citation statements)

References 39 publications

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“…EKFs are mostly used with simple ECMs (see Figure 3) [174][175][176][177]. Sometimes, the model is enhanced by electro-chemical equations based on the Single Particle Model (SPM) [178][179][180]. However, the model accuracy significantly influences the estimation performance [101,103,[181][182][183] as does the measurement accuracy [110,181].…”

Section: Online Identification Of Core Temperaturementioning

confidence: 99%

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

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Section: Online Identification Of Core Temperaturementioning

confidence: 99%

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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“…Considering that the surface concentration of solid phase is related to the side reactions in the battery [35], it should be noted that here the c s,p,surf used is replaced by c s,n to calculate the battery SOC, which can better ensure the battery charging and discharging safety.…”

Section: Validation Of Simplified Emmentioning

confidence: 99%

Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

Pang

et al. 2021

Energies

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State of Charge (SOC) is essential for a smart Battery Management System (BMS). Traditional SOC estimation methods of lithium-ion batteries are usually conducted using battery equivalent circuit models (ECMs) and the impact of current sensor bias on SOC estimation is rarely considered. For this reason, this paper proposes an online SOC estimation based on a simplified electrochemical model (EM) for lithium-ion batteries considering sensor bias. In EM-based SOC estimation structure, the errors from the current sensor bias are addressed by proportional–integral observer. Then, the accuracy of the proposed EM-based SOC estimation is validated under different operating conditions. The results indicate that the proposed method has good performance and high accuracy in SOC estimation for lithium-ion batteries, which facilitates the on-board application in advanced BMS.

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“…A good choice is not necessarily based on the accuracy of the model, but rather on a compromise between accuracy, simplicity, computation time, or ease of integration into a global system. Different modeling approaches exist: physicochemical modeling [17,18], "black box" modeling [19][20][21], energy modeling, electrical modeling, and through the use of equivalent electrical circuits.…”

Section: Electrical Modeling By Equivalent Electrical Circuitsmentioning

confidence: 99%

An electric circuit model for a lithium-ion battery cell based on automotive drive cycles measurements

Khalfi

Boumaaz

Soulmani

et al. 2021

IJECE

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The on-board energy storage system plays a key role in electric vehicles since it directly affects their performance and autonomy. The lithium-ion battery offers satisfactory characteristics that make electric vehicles competitive with conventional ones. This article focuses on modeling and estimating the parameters of the lithium-ion battery cell when used in different electric vehicle drive cycles and styles. The model consists of an equivalent electrical circuit based on a second-order Thevenin model. To identify the parameters of the model, two algorithms were tested: Trust-Region-Reflective and Levenberg-Marquardt. To account for the dynamic behavior of the battery cell in an electric vehicle, this identification is based on measurement data that represents the actual use of the battery in different conditions and driving styles. Finally, the model is validated by comparing simulation results to measurements using the mean square error (MSE) as model performance criteria for the driving cycles (UDDS, LA-92, US06, neural network (NN), and HWFET). The results demonstrate interesting performance mostly for the driving cycles (UDDS and LA-92). This confirms that the model developed is the best solution to be integrated in a battery management system of an electric vehicle.

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Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter

Cited by 175 publications

References 39 publications

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

An electric circuit model for a lithium-ion battery cell based on automotive drive cycles measurements

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