scholarly journals Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1410
Author(s):  
Zheng Chen ◽  
Jiapeng Xiao ◽  
Xing Shu ◽  
Shiquan Shen ◽  
Jiangwei Shen ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Joint Estimation ◽  
Model Based ◽  
Cubature Kalman Filter ◽  
Capacity Degradation ◽  
Estimation Scheme

In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium–ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available capacity in the scope of whole lifespan, is constructed to describe the capacity attenuation, and the battery available capacity is identified by a genetic algorithm (GA), together with the parameters related to SOC. The square root cubature Kalman filter (SRCKF) is employed to estimate the SOC with the consideration of capacity degradation. The experimental results demonstrate the effectiveness and feasibility of the co-estimation scheme.

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

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3383 ◽  
Author(s):  
Woo-Yong Kim ◽  
Pyeong-Yeon Lee ◽  
Jonghoon Kim ◽  
Kyung-Soo Kim
Keyword(s):  
Lithium Ion Battery ◽  
Nonlinear Model ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Battery Cell ◽  
Model Based ◽  
State Of Charge Estimation

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.

scholarly journals An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 478 ◽  
Author(s):  
Xingtao Liu ◽  
Chaoyi Zheng ◽  
Ji Wu ◽  
Jinhao Meng ◽  
Daniel-Ioan Stroe ◽  
...  
Keyword(s):  
Particle Filter ◽  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Maximum Error ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Model Parameters ◽  
State Of Power

In this paper, an improved method for estimating the state of charge (SOC) of lithium-ion batteries is proposed, which is developed from the particle filter (PF). An improved genetic particle filter (GPF), owing to the advantages of the PF and genetic algorithm, is proposed to overcome the disadvantage of the traditional particle filter: lacking the diversity of particles. Firstly, the relationship between SOC and open-circuit voltage (OCV) is identified on the low-current OCV test. Secondly, a first-order resistor and capacitance (RC) model is established, then, the least-squares algorithm is used to identify the model parameters via the incremental current test. Thirdly, GPF and the improved GPF (IGPF) are proposed to solve the problems of the PF. The method based on the IGPF is proposed to estimate the state of power (SOP). Finally, IGPF, GPF, and PF are employed to estimate the SOC on the federal urban driving schedule (FUDS). The results show that compared with traditional PF, the errors of the IGPF are 20% lower, and compared with GPF, the maximum error of the IGPF has declined 1.6% SOC. The SOC that is estimated by the IGPF is applied to estimate the SOP for battery, considering the restrictions from the peak SOC, the voltage, and the instruction manual. The result shows that the method based on the IGPF can successfully estimate SOP.

Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles

Energy ◽  
2012 ◽  
Vol 39 (1) ◽  
pp. 310-318 ◽  
Author(s):  
Hongwen He ◽  
Xiaowei Zhang ◽  
Rui Xiong ◽  
Yongli Xu ◽  
Hongqiang Guo
Keyword(s):  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Model Based

A framework for battery internal temperature and state-of-charge estimation based on fractional-order thermoelectric model

2022 ◽  
pp. 014233122110672
Author(s):  
Yujie Wang ◽  
Caijie Zhou ◽  
Guanghui Zhao ◽  
Zonghai Chen
Keyword(s):  
Lithium Ion Batteries ◽  
Fractional Order ◽  
Electrochemical Impedance ◽  
Prediction Method ◽  
Lithium Ion ◽  
State Of Charge ◽  
Joint Estimation ◽  
Internal Temperature ◽  
Model Based ◽  
Thermoelectric Model

In recent years, the rapid development of electric vehicles has raised a wave of innovation in lithium-ion batteries. The safety operation of lithium-ion batteries is one of the major bottlenecks restraining the development of the energy storage market. The temperature especially the internal temperature can significantly affect the performance and safety of the battery; therefore, this paper presented a novel framework for joint estimation of the internal temperature and state-of-charge of the battery based on a fractional-order thermoelectric model. Due to the nonlinearity, coupling, and time-varying parameters of lithium-ion batteries, a fractional-order thermoelectric model which is suitable for a wide temperature range is first established to simulate the battery’s thermodynamic and electrical properties. The parameters of the model are identified by the electrochemical impedance spectroscopy experiments and particle swarm optimization method at six different temperatures, and then the relationship between parameters and temperature is obtained. Finally, the framework for joint estimation of both the cell internal temperature and the state-of-charge is presented based on the model-based state observer. The experimental results under different operation conditions indicated that, compared with the traditional off-line prediction method, the model-based online estimation method not only shows stronger robustness under different initial conditions but also has better accuracy. Specifically, the absolute mean error of the estimation of state-of-charge and internal temperature based on the proposed method is about 0.5% and 0.3°C respectively, which is about half of that based on the off-line prediction method.

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