Novel Battery-Supercapacitor Hybrid Energy Storage System for Wide Ambient Temperature Electric Vehicles Operation

Reddy, B. Ramana Manohar; Das, Moumita; Chauhan, Nitin

doi:10.1109/tcsii.2023.3237860

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…Significantly, all the tests are carried out with the same brand new lithium-ion battery, and the sampling cycle is 1 s throughout. During experimentation, the battery is exposed to nine drive cycles, namely three dynamic testing driving cycles, including US06, LA92 and UDDS, 25 and six mixed drive cycles which are well designed driving cycles that simulate different driving conditions. The US06 cycle is an aggressive driving cycle designed to simulate vehicle behavior under high-speed and quick acceleration conditions.…”

Section: Battery Experimentsmentioning

confidence: 99%

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Qin,

Hao,

Ding

2023

J. Electrochem. Soc.

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State of charge (SOC) refers to the remaining capacity of the battery, which cannot be measured directly. A multi-measurement Kalman filter which is composed of two sub Kalman filters is constructed to improve the estimation accuracy of SOC. The two sub filters share the same state function but have different measurements, namely the terminal voltage and the SOC estimation from neural network, respectively. Based on minimizing the trace of error covariance, an optimal weighted matrix is computed to fuse the estimates of the two sub filters. The training dataset of neural network is collected from mixed discharging cycles experiment and corresponding charging process. By comparing the results with model-based methods, such as H-infinity filter, unscented Kalman filter, data-driven methods, like neural networks and hybrid method, the multi-measurement Kalman filter is verified by both the root mean square error and mean absolute error that are less than 2% in different drive cycles.

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Section: Battery Experimentsmentioning

confidence: 99%

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Qin,

Hao,

Ding

2023

J. Electrochem. Soc.

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“…Table 2 presents a direct comparison of the specific power/energy and charge/discharge times of batteries, supercapacitors, and capacitors. Based on these characteristics, current research trends are typically biased towards using SCs in hybrid energy-storage systems combined with batteries [16]. This could potentially boost the lifecycle of hybrid systems, with improved efficiencies compared to the ∼1000 cycles of batteries (see Table 2).…”

Section: Supercapacitors and Their Non-traditional Applicationsmentioning

confidence: 99%

“…In the recent past, a new trend has emerged of using SC banks in utility voltage stabilizer systems to provide grid support for short periods [19]. More details about SC applications are found in [13][14][15][16][17][18][19]. Compared to these traditional applications of SCs, the University of Waikato Power Electronics group has developed a unique set of new circuit topologies, now known as SC-assisted techniques, to potentially apply this technology in various novel circuit designs.…”

Section: Supercapacitors and Their Non-traditional Applicationsmentioning

confidence: 99%

Laplace Transform-Based Modelling, Surge Energy Distribution, and Experimental Validation of a Supercapacitor Transient Suppressor

Silva Thotabaddadurage

2023

Technologies

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The discovery of the transient-surge-withstanding capability of electrochemical dual-layer capacitors (EDLCs) led to the development of a unique, commercially beneficial circuit topology known as a supercapacitor transient suppressor (STS). Despite its low component count, the new design consists of a transient-absorbing magnetic core which takes the form of a coupled inductor placed between the AC-main- and load-side varistors. With an introduction to the structural features of metal oxide varistors (MOVs), gas tubes, thyristors, and EDLCs, this research presents a frequency (S)-domain analysis of an STS circuit to accurately model the surge propagation through its coupled inductor. Transient energy distribution trends among STS components are estimated in this paper, with an emphasis on peak energies absorbed and dissipated by the various inductive, capacitive, and resistive circuit elements. Moreover, this study reveals STS transient-mode test waveforms validated by a standard lightning surge simulator with supporting simulation plots based on LTSpice numerical techniques. Both experimental and simulation results are consistent, with the analytical findings showing 90% of the peak transient propagating through the primary coil, whereas only 10% is shared into the secondary coil of the coupled inductor. In addition, it is proven that the two STS MOVs dissipate over 50% of the transient energy for a standard 6 kV/3 kA combinational surge, while the magnetic core absorbs over 20% of the energy. All test procedures conducted during this research adhere to IEEE C62.41/IEC 61000-4-5 standards.

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“…Electrochemical capacitors exhibit energy and power density ranges between traditional capacitors and batteries [7]. Ultracapacitors experience diminished capacitance during discharge and cycle life [8].…”

mentioning

confidence: 99%

“…Charging and discharging processes are similar for both capacitors and batteries, making this approach versatile for different energy storage devices within their operating ranges. Super capacitors generally outperform regular batteries in various aspects [9][10][11][12][13], suggesting their potential as battery replacements. While battery-powered cars encounter energy management challenges, hybrid electric vehicles mitigate these issues [14].…”

mentioning

confidence: 99%

Futuristic Energy Management Solution: Fuzzy logic controller-Enhanced Hybrid Storage for Electric Vehicles with Batteries and Super Capacitors

Sudhakar,

Kumaraswamy

2023

IJEER

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The core focus of this study was directed towards devising an energy management strategy tailored for hybrid storage systems (HSS) within electric vehicles, with the prime objective of enhancing the longevity of the battery cycle. The batteries employed in electric vehicles (EVs) are prone to expedited deterioration resulting from harsh charging/discharging cycles and the substantial power surges experienced during acceleration and deceleration phases. While an excessively large energy storage system (ESS) could cater to the elevated power requisites, it inevitably grapples with augmented dimensions, bulk, and cost implications. In a bid to surmount these challenges, an innovative approach has been explored: integrating battery and supercapacitor (SC) elements within the HSS. This synergy aims not only to curtail the overall ESS footprint but also to elongate the operational lifespan of the battery. The pivotal concern revolves around constructing an adept energy management scheme that orchestrates the interplay between the primary energy storage component and the SC, a resource in demand by EVs. Central to this study is the proposition of an intelligent energy management strategy, grounded in fuzzy logic controller (FLC), seamlessly embedded within the within the HSS of the EV. To translate these concepts into tangible outcomes, a comprehensive assessment was conducted. By leveraging the capabilities of MATLAB/Simulink software, the state of charge for the super capacitor and the power dynamics of the battery were scrutinized across diverse driving scenarios over distinct time intervals. The standout feature of this investigation lies in the employment of an ingenious FLC strategy, meticulously regulating the energy and flow of power and energy between the battery and super capacitor elements within the HSS of the EV. In a comparative analysis against conventional control methodologies, this approach shines vividly, yielding superior outcomes and cementing its efficacy.

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Novel Battery-Supercapacitor Hybrid Energy Storage System for Wide Ambient Temperature Electric Vehicles Operation

Cited by 13 publications

References 19 publications

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Laplace Transform-Based Modelling, Surge Energy Distribution, and Experimental Validation of a Supercapacitor Transient Suppressor

Futuristic Energy Management Solution: Fuzzy logic controller-Enhanced Hybrid Storage for Electric Vehicles with Batteries and Super Capacitors

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