Battery technologies and functionality of battery management system for EVs: Current status, key challenges, and future prospectives

Waseem, Mohammad; Ahmad, Mumtaz; Parveen, Aasiya; Suhaib, Mohd

doi:10.1016/j.jpowsour.2023.233349

Cited by 55 publications

(17 citation statements)

References 208 publications

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“…Continuous research and development in battery technology like advanced battery chemistry, Energy-Dense Anode and Cathode Materials, Improved Electrolytes, Advanced Battery Management Systems (BMS), Battery Thermal Management, along with ongoing advancements in materials science and manufacturing processes, are key to improving the specific energy of batteries in electric vehicles. As technology evolves, it is possible to see higher specific energy batteries that offer longer driving ranges and improved overall performance for EVs [15].…”

Section: Electric Vehicle Batteriesmentioning

confidence: 99%

Investigations of phase change materials in battery thermal management systems for electric vehicles: a review

Dolla,

Fetene

2024

Mater. Res. Express

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Taking advantage of electric vehicles' low pollution, the world is changing its face to electric vehicle (EV) production. As EVs rely heavily on specialized batteries, it's important to manage them safely and properly to prevent thermal runaway. High ambient temperatures and varied charging/discharging rates increases battery temperature. To address these challenges, Battery Thermal Management System (BTMS) come into play. This work focuses on passive cooling in BTMS, which is one of two categories of BTMS, with the other being active cooling using liquid-air systems. Passive BTMS has gained prominence in research due to its cost-effectiveness, reliability, and energy efficiency, as it avoids the need for additional components like pumps/fans. This article specifically discusses recent experimental studies regarding phase change material (PCM)-based thermal management techniques for battery packs. It explores methods for enhancing thermal conductivity in PCMs and identifies methodologies for BTMS experiments using PCMs. Also recommends the importance of optimization techniques like machine learning, temperature sensors, and state-of-charge management, to ensure accuracy and uniform temperature distribution across the pack. While paraffin wax has been a popular choice in experimental studies for its capacity to absorb and release heat during phase transitions, as a matter of its low thermal conductivity (0.2 to 0.3 Wk-1m-1) limits reaction in rapid charging/discharging of batteries. So integration with highly thermally conductive additives is recommended. Additives such as heat pipes offer superior thermal conductivity compared to expanded graphite (5 to 200 Wk-1m-1). As a result, the integration of heat pipes further reduces the temperature of battery by 28.9% in addition to the reduction of 33.6% by pure PCMs in time of high charge/discharge rates (5C to 8C). So high-conductivity additives correlate directly with improved thermal performance and are essential for maintaining optimal battery temperatures and overall reliability in EV battery packs.

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Section: Electric Vehicle Batteriesmentioning

confidence: 99%

Investigations of phase change materials in battery thermal management systems for electric vehicles: a review

Dolla,

Fetene

2024

Mater. Res. Express

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“…The ICE generator is exploited to recharge the batteries or/and supply the motor with electrical power via a PE converter but does not directly propel the vehicle. 16 Merely the wheels of the vehicle are driven through the electric motor by amplifying the transmission.…”

Section: Series Hev Configurationmentioning

confidence: 99%

Energy management control strategies for energy storage systems of hybrid electric vehicle: A review

Veerendra,

Mohamed,

García Márquez

2024

Energy Storage

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Continuous efforts to preserve the environment and to reduce gaseous emissions due to the massive growth of urban economic development and heightened concerns over crude oil depletion have accelerated researchers to find long‐term solutions, particularly in the transportation sector with the focus on powertrain electrification. This article delivers a comprehensive overview of electric vehicle architectures, energy storage systems, and motor traction power. Subsequently, it emphasizes different charge equalization methodologies of the energy storage system. This work's contribution can be identified in two points: first, providing an overview of different energy management methods to researchers and scholars. Second, to highlight the state‐of‐the‐art leanings in major components and to highlight promising approaches to hybrid electric vehicle future development.

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“…3,4 As a result, enhancing the reliability, safety, and lifespan of lithium-ion batteries has become a major research focus. 5,6 In this context, battery management systems are indispensable in the use of lithium-ion batteries. 7 These systems can monitor various battery parameters such as current, voltage, and temperature.…”

Section: Motivations and Technical Challengesmentioning

confidence: 99%

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

Zou,

Wang,

Hai

et al. 2024

J. Electrochem. Soc.

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With the rapid development of electric vehicles and green energy sources, the use of backpropagation neural network (BPNN) to precisely estimate the state of charge (SOC) in lithium-ion batteries has become a popular research topic. However, traditionally BPNN has low prediction accuracy and large output fluctuations. To address the shortcomings of BPNN, self-adaptive flower pollination algorithm (SFPA) was proposed to optimize the initial weights and thresholds of BPNN, and an output sliding average window (OSAW) strategy is proposed to smooth SOC outputs in this research, which SOC estimation method is named SFPA-BP-OSAW. In addition, the performance of the newly-proposed method is compared with other common related algorithms under different working conditions to verify the effectiveness of SFPA-BP-OSAW. The experimental results show that the mean absolute error of SFPA-BP-OSAW is 0.771% and 0.897%, and the root mean square error is 0.236% and 0.37%, respectively, under HPPC and BBDST working conditions. Experimental data and error analysis show that the method proposed in this paper has fast convergence, high prediction accuracy, and curve smoothness.

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Battery technologies and functionality of battery management system for EVs: Current status, key challenges, and future prospectives

Cited by 55 publications

References 208 publications

Investigations of phase change materials in battery thermal management systems for electric vehicles: a review

Investigations of phase change materials in battery thermal management systems for electric vehicles: a review

Energy management control strategies for energy storage systems of hybrid electric vehicle: A review

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

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