Optimal charging strategy design for lithium‐ion batteries considering minimization of temperature rise and energy loss

Chen, Zheng; Shu, Xing; Xiao, Renxin; Yan, Wensheng; Liu, Yonggang; Shen, Jiangwei

doi:10.1002/er.4560

Cited by 34 publications

(17 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where T denotes the battery temperature. To further evaluate performance of the online identification method, the commonly employed offline parameter identification method, i.e., GA, is applied to identify other parameters, including the ohmic resistance, polarization resistance and polarization capacitor [21,[36][37][38]. The root-mean-square error (RMSE) between the model output and measured voltage is selected as the fitness function:…”

Section: ) Offline Identificationmentioning

confidence: 99%

“…When the sample i x lies outside the two critical hyperplanes, i  is less than zero and it indicates the negative distance from highlighting the positive distance from i x to the nearest critical hyper plane. The Lagrange function, as shown in (38), is defined and the maximum condition of the function is solved to achieve the minimization.…”

Section: B the Soh Estimation Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation

Shu

Shen

et al. 2020

Journal of Power Sources

Self Cite

View full text Add to dashboard Cite

In this paper, an adaptive fusion algorithm is proposed to robustly estimate the state of charge of lithium-ion batteries. An improved recursive least square algorithm with a forgetting factor is employed to identify parameters of the built equivalent circuit model, and the least square support vector machine algorithm is synchronously leveraged to estimate the battery state of health. On this basis, an adaptive H-infinity filter algorithm is applied to predict the battery state of charge and to cope with uncertainty of model errors and prior noise evaluation. The proposed algorithm is comprehensively validated within a full operational temperature range of battery and with different aging status. Experimental results reveal that the maximum absolute error of the fusion estimation algorithm is less than 1.2%, manifesting its effectiveness and stability when subject to internal capacity degradation of battery and operating temperature variation.

show abstract

Section: ) Offline Identificationmentioning

confidence: 99%

Section: B the Soh Estimation Algorithmmentioning

confidence: 99%

An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation

Shu

Shen

et al. 2020

Journal of Power Sources

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Reference 28, a temperature compensation model mainly accounting for the temperature variation of 0°C to 40°C is built based on a two‐order ECM, and then the SOC is estimated by the UKF, which declares that the maximum estimation error is less than 3%. In practical applications, the battery temperature dynamically changes within a large range (usually between 10°C and 50°C), thus significantly affecting estimation accuracy of SOC 29 . Thus, it is imperative to establish a temperature‐dependent electrical model to cover the whole operation temperature range, 30 and then the SOC can be estimated robustly with the help of advanced filters 31 …”

Section: Introductionmentioning

confidence: 99%

Stateof charge estimation framework for lithium‐ion batteries based on square root cubature Kalman filter under wide operation temperature range

Shen

Xiong

Shu

et al. 2020

Intl J of Energy Research

Self Cite

View full text Add to dashboard Cite

Summary Due to the significant influence of temperature on battery charging and discharging performance, exact evaluation of state of charge (SOC) under complex temperature environment becomes increasingly important. This paper develops an advanced framework to estimate the SOC for lithium‐ion batteries with consideration of temperature variation. First, an accurate electrical model with wide temperature compensation is established, and a series of experiments are carried out under wide range time‐varying temperature from −20°C to 60°C. Then, the genetic algorithm is leveraged to identify the temperature‐dependent model parameters. On this basis, the battery SOC is accurately estimated based on the square root cubature Kalman filter algorithm. Finally, the availability of the proposed method at different temperatures is validated through a complicated mixed working cycle test, and the experimental results manifest that the devised framework can accurately evaluate SOC under wide time‐varying temperature range with the maximum error of less than 2%.

show abstract

“…Electric vehicles (EVs) and hybrid EVs (HEVs) are promising solutions, which however, require electrical energy storage systems to completely or partially replace propelling power supplied by traditional internal combustion engines [2]. In this context, applications of lithium-ion batteries have been intensively spurred due to their numerous advantages, such as their wide environmental temperature operation capability, high energy density, long lifespan and their large charge/discharge current [3]. For lithium-ion batteries, the state of charge (SOC) and available capacity, usually provided by battery management systems (BMSs), are crucial parameters for evaluation of the electrical performance of the battery, as well as for the control of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.Energies 2020, 13, 1410 2 of 15 initial values, and the latter is not suitable for online estimation, as it usually costs long shelving time to acquire the OCV value. With the development of computation technologies and machine learning, a variety of artificial intelligence-based, data-driven methods, such as neural networks [6] and support vector machines [7], are proposed for SOC estimation by establishing black-box models. Data-driven methods feature a strong nonlinear mapping capability with high accuracy; however, these approaches show high complexity, and require a considerable amount of training data. Alternatively, model-based methods have been widely investigated and applied for SOC estimation, thanks to the capabilities of online application, high precision and the independence of initial values. Conventional modeling manners mainly include electrochemical models and the equivalent circuit models (ECMs). Compared with complicated electrochemical models, ECM is commonly used to describe the electrical behavior of batteries, and subsequently to estimate the SOC due to its simplification and preferable precision. Yanwen Li et al. proposed a multi-model probability fusion algorithm to describe the battery's electrical characteristics, and subsequently estimate the SOC [8]. In model-based methods, the combination of the battery model and the intelligent filtering algorithm is a hotspot in SOC estimation research. The frequently used filtering algorithms include Kalman filtering (KF) [9], the H-infinity filter (HIF) [10], particle filter (PF) [11], and their various extensions. In particular, the extended KF (EKF) is widely employed to execute SOC estimation using a first-order Taylor expansion on the basis of the battery's nonlinear model [12]. Nonetheless, the second and higher order expansion is usually neglected, thus leading to slow a convergence rate, and even divergence. The unscented KF (UKF) is exploited to estimate battery SOC, based on the recursive unscented transformation to approximate the nonlinear observation without Taylor polynomial expansions [13]. The UKF shows better estimation precision and robustness than the EKF in strong, nonlinear systems [14]. On the basis of the radial-spherical cubature criterion, the cubature Kalman filter (CKF) leverages a set of volume points to appro...

show abstract

Optimal charging strategy design for lithium‐ion batteries considering minimization of temperature rise and energy loss

Cited by 34 publications

References 45 publications

An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation

An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation

Stateof charge estimation framework for lithium‐ion batteries based on square root cubature Kalman filter under wide operation temperature range

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

Contact Info

Product

Resources

About