A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures

Wang, Chun; He, Haibo; Zhang, Yongzhi; Mu, Hao

doi:10.1016/j.apenergy.2017.03.060

Cited by 62 publications

(25 citation statements)

References 34 publications

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“…ECMs can easily identify model parameters and provide high accuracy that can satisfy the requirements of BMS [32,33]. By employing the resistor, capacitor, and voltage source, ECMs can describe the dynamic, external characteristics of LIBs.…”

Section: Equivalent Circuit Model Of Lithium-ion Batteriesmentioning

confidence: 99%

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

et al. 2018

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Abstract:The accuracy of state-of-charge (SOC) estimation, one of the most important functions of a battery management system (BMS), is the basis for the proper operation of an electric vehicle. This study proposes a method for accurate SOC estimation. To achieve a balance between accuracy and simplicity, a second-order resistor-capacitor equivalent circuit model is applied before the algorithm is deduced, and the parameters of the established model are determined using a fitting technique. Battery state space equations are then described. A strong tracking H-infinity filter (STHF) is proposed based on an H-infinity filter (HF) and a strong tracking filter. By introducing a suboptimal fading factor, the STHF approach can use the relevant information in the estimation residual sequence to update the estimation results. To verify the robustness of this approach, battery test experiments are performed at different temperatures on lithium-ion batteries. Finally, the SOC estimation results obtained using the STHF suggest that the STHF method exhibits high robustness against the measured noises and initial error. For comparison, the estimation results of the commonly used extended Kalman filter (EKF) and HF methods are also displayed. It is suggested that the proposed STHF approach obtains a more accurate SOC estimation.

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Section: Equivalent Circuit Model Of Lithium-ion Batteriesmentioning

confidence: 99%

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

et al. 2018

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“…To protect EV batteries and extend their life, an UC is combined with them [116][117][118][119]. The sudden load variations are absorbed by the UC, and hence efficient use of the batteries can be achieved [120,121].…”

Section: Impedance Source Invertermentioning

confidence: 99%

A comprehensive review on hybrid electric vehicles: architectures and components

2019

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The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuelefficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors' role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability. Keywords Hybrid electric vehicle Á Hybrid energy storage system Á Architecture Á Traction motors Á Bidirectional converter Abbreviations and symbols ABS Antilock braking system AC Alternating current ADTR Antidirectional-twin-rotary ADVISOR Advanced vehicle simulator ANN Artificial neural network ASCI Auto-sequential commutated mode singlephase inverter BEV Battery electric vehicle BLDC Brushless DC motor CD Charge depletion CDFIM Cascaded DFIM CF-qZSI Current-fed quasi-ZSI CMPPT Centralized MPPT CS Charge sustaining CSI Current source inverter CS-PMSM Compound-structure PMSM CVT Continuous variable transmission DC Direct current DFIM Doubly fed induction motor DMPPT Distributed MPPT DRM Double-rotor machines DTC Direct torque control e-CVT Electronic continuous variable transmission EM Electric motor EMS Energy management system EREV Extended range electric vehicle ESS Energy storage system EV Electric vehicle FC Fuel cell

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“…To better estimate battery states, various mathematical models have been established, including electrochemical models [26] and ECMs [27]. However, they differ greatly in accuracy, computation complexity and reliability.…”

Section: Battery Modeling and Analysismentioning

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

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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...

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A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures

Cited by 62 publications

References 34 publications

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

A comprehensive review on hybrid electric vehicles: architectures and components

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

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