Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Ali, Muhammad Umair; Zafar, Amad; Nengroo, Sarvar Hussain; Hussain, Sadam; Alvi, Muhammad Junaid; Kim, Hee-Je

doi:10.3390/en12030446

Cited by 265 publications

(106 citation statements)

References 213 publications

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“…where α and β are the coefficients of the linear term, f (z) is a bounded nonlinear function consisting of a sum of sine functions and a n , b n , and c n are the coefficients of the sum of sine functions. Figure 2 conceptually represents (6). Equations (1)-(3) can be rewritten as a nonlinear state space representation by using (6).…”

Section: Nonlinear System Model For a Single Battery Cellmentioning

confidence: 99%

“…In contrast to HEVs, the mileage range of EVs is directly affected by the power and energy density of the battery itself and the performance of the battery management system (BMS). Hence, many studies related to lithium-ion (Li-ion) batteries, including the development of new materials and algorithms for inner state estimation, have been conducted by various research groups [1][2][3][4][5][6][7][8]. Increasing the energy and power density via advancements in battery manufacturing technology requires a higher level of monitoring of the battery states to fully and safely use the potential of the battery.…”

Section: Introductionmentioning

confidence: 99%

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A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

et al. 2019

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This paper presents a nonlinear-model-based observer for the state of charge estimation of a lithium-ion battery cell that always exhibits a nonlinear relationship between the state of charge and the open-circuit voltage. The proposed nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies. The proposed method has the following advantages: (1) The observability condition of the proposed nonlinear-model-based observer is derived regardless of the shape of the open circuit voltage curve, and (2) because the terminal voltage is contained in the state vector, the proposed model and its observer are insensitive to sensor noise. A series of experiments using an INR 18650 25R battery cell are performed, and it is shown that the proposed method produces convincing results for the state of charge estimation compared to conventional SOC estimation methods.

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Section: Nonlinear System Model For a Single Battery Cellmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

et al. 2019

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“…The influence of ambient temperature is not discussed in this paper. For the sample batteries in this paper, the relationship we measured can be expressed as Equation (1).…”

Section: Battery Model and Parameter Identificationmentioning

confidence: 99%

“…The standard particle filter algorithm was simulated and tested under the charge/discharge cycle shown in Figure 7. Initial SOC value of the battery can be obtained by measuring the open circuit voltage after fully charging the test battery and resting for one day, and then substituting the open circuit voltage into Equation (1). In the process of charge/discharge test, the test equipment records the actual charge/discharge current in real time, and obtains the real SOC of the battery by Coulomb counting.…”

Section: The Estimation and Verification Of The Standard Particle Filmentioning

confidence: 99%

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Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter

Liu

et al. 2019

Energies

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State of charge (SOC) estimation of lithium batteries is one of the most important unresolved problems in the field of electric vehicles. Due to the changeable working environment and numerous interference sources on vehicles, it is more difficult to estimate the SOC of batteries. Particle filter is not restricted by the Gaussian distribution of process noise and observation noise, so it is more suitable for the application of SOC estimation. Three main works are completed in this paper by taken LFP (lithium iron phosphate) battery as the research object. Firstly, the first-order equivalent circuit model is adapted in order to reduce the computational complexity of the algorithm. The accuracy of the model is improved by identifying the parameters of the models under different SOC and minimum quadratic fitting of the identification results. The simulation on MATLAB/Simulink shows that the average voltage error between the model simulation and test data was less than 24.3 mV. Secondly, the standard particle filter algorithm based on SIR (sequential importance resampling) is combined with the battery model on the MATLAB platform, and the estimating formula in recursive form is deduced. The test data show that the error of the standard particle filter algorithm is less than 4% and RMSE (root mean square error) is 0.0254. Thirdly, in order to improve estimation accuracy, the auxiliary particle filter algorithm is developed by redesigning the importance density function. The comparative experimental results of the same condition show that the maximum error can be reduced to less than 3.5% and RMSE is decreased to 0.0163, which shows that the auxiliary particle filter algorithm has higher estimation accuracy.The intrinsic parameter measurement method is to select suitable battery parameters that can directly characterize the SOC for direct measurement, and then estimate the battery SOC through these parameters. The commonly used intrinsic parameters include open circuit voltage (OCV), impedance spectrum [7], electromotive force (EMF), and internal resistance (IR). The advantage of this method is that the battery SOC can be directly obtained through the mapping relationship between the SOC and the battery intrinsic parameter, and the disadvantage is that the estimation accuracy relies excessively on the accuracy of the mapping relationship between SOC and intrinsic parameters of battery. Holger [8] summarized the SOC estimation method based on the impedance spectrum. Waag [9] confirmed that the impedance parameter of a Li-ion battery changes negatively as the battery ages.Computer intelligence methods found in literature mainly include the neural network model, genetic algorithm and particles swarm optimization. The advantage of these methods is that they do not require a thorough understanding of battery internal mechanism, but they do require large amounts of data for the machine learning process. Sheikhan et al. [10] used an artificial neural network to realize feedback correction of the traditional time-integration m...

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