Impact of battery cell imbalance on electric vehicle range

Chen, Jun; Zhou, Zhaodong; Zhou, Ziwei; Wang, Xia; Liaw, Bor Yann

doi:10.1016/j.geits.2022.100025

Cited by 44 publications

(9 citation statements)

References 39 publications

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“…Nevertheless, they suffer from various limitations, including finite lifetimes, the need for periodic replacement, and environmental pollution. Consequently, they prove unsuitable for enabling wireless self-powering in rotating components, such as turbine blades [5][6][7]. If the vibrations in rotating machinery can be effectively harnessed and converted into electrical energy, it would pave the way for achieving self-powering capabilities for micro and small sensors [8,9].…”

Section: Introductionmentioning

confidence: 99%

Characterization of magnetostrictive bi-stable rotational vibration energy harvester with integrated centrifugal effect

Dong,

Liang,

Liu

et al. 2024

Smart Mater. Struct.

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Rotational machinery is a common presence in dust still production, and the occurrence of operational failures in components like engines and turbine blades necessitates effective measures. To solve this challenge, remote structural health monitoring using energy harvesting and wireless sensors has been widely employed to realize the self-powered sensing. This study proposes a magnet-induced bi-stable rotational energy harvester (REH), which utilizes the centrifugal effect to broaden the effective frequency bandwidth, enabling efficient energy harvesting in complex environments. A comprehensive mathematical model has been established to facilitate the dynamic characteristics of the bi-stable system, taking into account the centrifugal effect. The theoretical results demonstrate that the gap distance of magnetic configuration has great effects on the bi-stable system. Additionally, the centrifugal effect decided by the centrifugal radius and rotational speeds also affects the stable high-energy orbit oscillations. Furthermore, experimental results indicate that the proposed REH can effectively operate within the frequency range of 230-290 rpm, with a maximum RMS voltage of 780 mV and corresponding power of 4.35 mW. These findings validate the performance of the bi-stable magnetostrictive REH with the centrifugal effect and indicate its potential to effectively address the power supply challenges for wireless sensors. Overall, this study presents a promising solution for enhancing the energy harvesting performance of REH and also provides insights into the design of high-efficiency REH by magnet-induced nonlinearity and the centrifugal effect.

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Section: Introductionmentioning

confidence: 99%

Characterization of magnetostrictive bi-stable rotational vibration energy harvester with integrated centrifugal effect

Dong,

Liang,

Liu

et al. 2024

Smart Mater. Struct.

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“…In ref. [18], the researchers took the effect of imbalance between individual battery packs on electric vehicle mileage as a starting point, and performed environmental simulations for lithium-ion battery condition assessment with real driving data. They conducted modeling of basic battery cell dynamics using an equivalent circuit model with heterogeneous parameters, assessed the effect of individual parameter variations on the course after simulating 125 trips, and analyzed the significance of the variations in each parameter.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

Zheng,

Huang,

Liu

et al. 2024

Advcd Theory and Sims

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As lithium‐ion batteries are the main power source of new energy vehicles, making accurate predictions of unknown State of Charge (SOC) during vehicle operation for vehicle data monitoring is vital to the advancement of intelligent new energy vehicles. In this manuscript, an expression tree‐based genetic programming regression model (ETGPR) is proposed to estimate the real‐time SOC of lithium‐ion batteries. The proposed model mainly adopts the symbolic regression technique. In addition to the current–voltage curves being fed into the model, an additional approach is designed to ensure real‐time model predictions in dynamic situations, which includes the previous moment's power in the input parameters. Different seed hyperparameters in the model are set, and the model automatically performs evolutionary calculations. Subsequently, each parameter of the model is optimally adjusted to obtain a set of regression expressions that accurately reflect the relationship between the SOC and each parameter after a specified number of iterations. Finally, the generated expression is proven to perform better in terms of its ability to capture the nonlinear relationship between SOC and battery variables. Also, the model demonstrates excellent robustness in the presence of notable noise from input‐independent features compared to other models, a root mean square error (RMSE) of less than 0.3% and a mean absolute error (MAE) of less than 0.2% are achieved. Furthermore, the potential of the model's implement‐ability under variable temperature and real driving data conditions is verified.

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“…Studies have identified the differences between cells of the same chemistry from the same manufacturer as the main cause of this variability. [5]. This difference is investigated in this study by considering the variations in two key independent cell parameters viz,.…”

Section: Introductionmentioning

confidence: 99%

Decoding Range Variability in Electric Vehicles: Unravelling the Influence of Cell-to-Cell Parameter Variation and Pack Configuration

Singh

Mandal

Mulpuri

et al. 2023

Preprint

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Electric vehicles (EVs) are rapidly growing in popularity, but range variability has become an important research area with significant implications for EV performance, usability, and overall market adoption. This study aims to unravel the complexities of range variability by examining the contributing factors and offering innovative strategies to mitigate these differences during pack design. Through a detailed analysis of cell parameter deviation, cell connections, battery configuration, battery pack size, and driving behavior, the research illuminates their impact on extractable energy and driving range. The study employed a comprehensive approach and conducted systematic simulation-based experimentation to identify the optimal battery pack configuration based on maximum extractable energy, minimal variability and maximum range. The results reveal insights into the relationship between discharge rate and battery pack performance, and the impact of cell parameter variations on pack energy output. This research advances the understanding of EV performance optimisation, reduces pack-to-pack variability, and extends battery pack lifespan.

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Impact of battery cell imbalance on electric vehicle range

Cited by 44 publications

References 39 publications

Characterization of magnetostrictive bi-stable rotational vibration energy harvester with integrated centrifugal effect

Characterization of magnetostrictive bi-stable rotational vibration energy harvester with integrated centrifugal effect

A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

Decoding Range Variability in Electric Vehicles: Unravelling the Influence of Cell-to-Cell Parameter Variation and Pack Configuration

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