Novel method for modelling and adaptive estimation for SOC and SOH of lithium-ion batteries

Li, Zuxin; Shen, Shengyu; Zhou, Zhe; Cai, Zhiduan; Gu, Wei‐Min; Zhang, Fengying

doi:10.1016/j.est.2023.106927

Cited by 39 publications

(9 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SoC estimation becomes more challenging as the battery ages. To account for aging effects, a battery aging model, such as the following, can be incorporated into SoC estimation [54]:…”

Section: ) Aging Effectsmentioning

confidence: 99%

“…For EVs with straightforward battery designs, the added complexity may not necessarily translate into proportional benefits, leading to inefficiencies in terms of computational resources and implementation costs [70][71][72]. In simple battery systems, the added complexity resulted in an 11.17% increase in computational resources without significant improvement in accuracy, highlighting the inefficiency of applying complex models [51,54].…”

Section: ) Overcomplication For Simple Battery Systemsmentioning

confidence: 99%

“…The integration of non-linearity and dynamics may lead to overcomplication for simple battery systems, reducing their efficiency [34,60,74]. In simpler battery systems, the integration of non-linearity and dynamics resulted in a 12.61% reduction in overall efficiency, raising questions about the suitability of such methods [54,56,57].…”

Section: ) Impact On Simple Battery Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Mousaei,

Naderi,

Bayram

2024

IEEE Access

View full text Add to dashboard Cite

As the share of electric vehicles increases, electric vehicles are exposed to broader of driving conditions (e.g., extreme weather), which reduce the performance and driving ranges of electric vehicles below their nameplate rating. To ensure customer confidence and support steady growth in electric vehicle adoption rates, accurate estimation of battery state of charge and maintaining battery state of health through optimal charge/discharge decisions are critical. Recently, vehicle manufacturers have begun to employ machine learning techniques to improve state-of-charge management to better inform drivers about both short-term (state of charge) and long-term (state of health) performance of their vehicles. This comprehensive review article explores the intersection of machine learning and state of charge management in electric vehicles. Recognizing the critical importance of state of charge in optimizing electric vehicle performance, the article starts by evaluating traditional state of charge estimation methods. Subsequently, it delves into the transformative impact of machine learning techniques and associated algorithms on state of charge management. Through the lens of various case studies, this article demonstrates how machine learning-based state of charge estimation empowers electric vehicles to make informed and dynamic energy usage decisions, enhancing efficiency and extending battery life. The challenges of data availability, model interpretability, and real-time processing constraints are acknowledged as impediments to the widespread adoption of machine learning techniques. Despite these challenges, the future outlook for machine learning in state of charge management appears promising, with emerging trends such as deep learning and reinforcement learning poised to refine state of charge estimation accuracy. Moreover, this study sheds light on the transformative potential of machine learning in enhancing state of charge management efficiency and effectiveness for electric vehicles, offering critical insights.. Machine learning emerges as a game-changing force in state of charge management for electric vehicles, paving the way for intelligent and adaptive vehicles that are both environmentally friendly and efficient. This evolving field invites further research and development, making it a vital and exciting area within the automotive industry.

show abstract

“…SoC estimation becomes more challenging as the battery ages. To account for aging effects, a battery aging model, such as the following, can be incorporated into SoC estimation [54]:…”

Section: ) Aging Effectsmentioning

confidence: 99%

Section: ) Overcomplication For Simple Battery Systemsmentioning

confidence: 99%

Section: ) Impact On Simple Battery Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Mousaei,

Naderi,

Bayram

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…SOC and SOH are two critical parameters in battery management, and they hold significant importance for both the performance and lifespan of the battery [3]. Firstly, SOC represents the percentage of the battery's current state of charge, i.e., the ratio of stored charge in the battery relative to its maximum capacity.…”

Section: Introductionmentioning

confidence: 99%

Systematic Design and Implementation Method of Battery-Energy Comprehensive Management Platform in Charging and Swapping Scenarios

Ren,

Liao,

Chen

2024

Energies

View full text Add to dashboard Cite

Batteries are one of the most crucial energy storage devices today, and battery-energy management technology has an extremely significant impact on the performance and lifespan of batteries. The traditional design approach for battery-energy management platforms often neglects considerations for charging and discharging scenarios. Additionally, functional modules are designed independently, leading to incompatibility issues between hardware and control units, thereby limiting the system’s performance. To address these challenges and enhance system coordination, this paper proposes a systematic design and implementation method for a battery-energy comprehensive management platform applied in charging and swapping scenarios. The method consists of four parts: hardware design, a dynamic load charging-balance control strategy, a composite micro-source hierarchical coordination control strategy, and a system emergency-response and protection strategy. The proposed method has been successfully applied to a design and has been used to build a battery-energy comprehensive management platform. Finally, through experiments, it has been demonstrated that this system can achieve energy scheduling, battery-energy balance, mode switching, and fault protection in a stable and reliable manner.

show abstract

“…The physical model method has been widely applied in battery research. [20][21][22] This method establishes accurate battery models to simulate the dynamic behavior of batteries, enabling practical estimation of the battery's SOH. [23][24][25] The parameters of the models are closely related to the aging level of the battery.…”

mentioning

confidence: 99%

A Novel State-of-Health Prediction and Assessment Strategies for High-Capacity Mining Lithium-Ion Batteries Based on Multi-Indicator

Feng,

Cai,

Zhan

et al. 2024

J. Electrochem. Soc.

View full text Add to dashboard Cite

Accurately assessing battery state of health (SOH) is crucial for ensuring the safety of lithium-ion batteries. However, current SOH evaluation methods suffer from inconsistent criteria and limited accuracy in prediction models. This paper introduces a novel SOH prediction and assessment strategy that relies on multiple indicators to address these challenges. First, multifaceted health factors are extracted based on charge cycle data, including battery charging time, incremental capacity, and dV/dt curve. Subsequently, a support vector regression model optimized by the sparrow search algorithm is proposed to predict SOH. The results show that MAE, RMSE, and MAPE are less than 0.037%, 0.047%, and 0.04%, respectively. Meanwhile, the Kalman filtering method is used to identify the second-order RC model online, and the relative SOH curves are obtained by defining the SOH through the internal resistance. Finally, by analyzing the effects of capacity and internal resistance changes on SOH, a new strategy for SOH assessment is proposed, which considers various factors and selects an appropriate judgment mechanism according to the characteristics exhibited by the battery at different life stages. The strategy is more conservative and reliable, providing a solid guarantee for the safe operation of mining equipment.

show abstract

Novel method for modelling and adaptive estimation for SOC and SOH of lithium-ion batteries

Cited by 39 publications

References 48 publications

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Systematic Design and Implementation Method of Battery-Energy Comprehensive Management Platform in Charging and Swapping Scenarios

A Novel State-of-Health Prediction and Assessment Strategies for High-Capacity Mining Lithium-Ion Batteries Based on Multi-Indicator

Contact Info

Product

Resources

About