State of Charge Estimation of Li-Ion Battery Based on Adaptive Sliding Mode Observer

Wang, Qi; Jiang, Jiaxin; Gao, Tian; Ren, Shurui

doi:10.3390/s22197678

Cited by 11 publications

(6 citation statements)

References 28 publications

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“…When the air enters the channels with a temperature lower than the temperature of the battery, the heat of the battery is removed through surface convection by the channels. The speed of heat removal from each channel is calculated by equation (15). According to this equation, the heat exchange speed in the channels' output is a function of the temperature at the channel's output and the temperature of the battery cell.…”

Section: Cooling Platesmentioning

confidence: 99%

“…In [7], the battery life is considered in the EMS problem, but the battery temperature is assumed to be constant. The design of different systems for the thermal management of batteries has been studied in [15] and [16]. References [17] and [18] have presented methods to estimate battery SOC, and 3 [19] the optimal range of battery temperature has been investigated.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Dynamic Programming Energy Management System for Parallel Hybrid Electric Vehicles According to Battery Model

Mousaei

allahyari²

2023

Preprint

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This article proposes an offline Energy Management System (EMS) for Parallel Hybrid Electric Vehicles (PHEVs). Dividing the torque between the Electric Motor (EM) and the Internal Combustion Engine (ICE) requires a suitable EMS. Batteries are vital to HEVs and significantly impact overall vehicle cost and performance. High temperature and high battery State of Charge (SOC) are the main factors that accelerate battery aging. SOC is the most critical state variable in EMS and was usually considered the only dynamic variable in previous studies. For simplicity, the battery temperature was often assumed to be constant, and the effect of EMS on temperature change was neglected. In this paper, we first apply Dynamic Programming (DP) to a PHEV without considering battery temperature variations. Then, the battery model is improved by modeling the cooling system to take into account temperature variations and show how neglecting the thermal dynamics of the battery in EMS is impractical. Finally, by integrating battery temperature as a state variable in the optimization problem, a new EMS is proposed to control battery temperature and SOC variation. Simulation results of the tested vehicle show that the proposed method controls battery charge and temperature. The proposed EMS method prevents uncontrolled fluctuations in battery temperature and reduces its deterioration rate.

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Section: Cooling Platesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Dynamic Programming Energy Management System for Parallel Hybrid Electric Vehicles According to Battery Model

Mousaei

allahyari²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This observer estimates SOC and core temperature using measurements of battery terminal voltage and surface temperature. Reference [16] proposes a battery model considering temperature effects and corresponding adaptive sliding mode observer. In practical application scenarios, considering the complex characteristics of the battery model and the special requirements for observer performance, further analysis is still required in terms of jitter suppression [17].…”

Section: Introductionmentioning

confidence: 99%

SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer

Xueyi,

Tinglong

2024

The Journal of Engineering

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Accurate state of charge (SOC) estimation and fault identification and localization are crucial in the field of battery system management. This article proposes an innovative method based on sliding mode observation theory for SOC estimation and short‐circuit fault location. The core of this new method is the design of an adaptive sliding mode observer, which reduces jitter by introducing adaptive switching gain, establishes an internal loop of gain and error, and improves the performance of SOC estimation. In addition, recursive least squares method was used to identify the key parameters of the model. Secondly, based on obtaining the SOC of each battery cell in series with the energy storage PACK, the specificity of the faulty battery cell in SOC change trend is utilized to identify and locate the short‐circuit fault of the energy storage PACK. The simulation and test results show that the designed adaptive sliding mode observer can significantly improve the estimation accuracy of SOC and has better stability. Compared to the commonly used Kalman estimation and BP neural network estimation methods, the designed method has improved accuracy by 5.53% and 3.42%, respectively. In addition, based on the accurate identification of SOC, the short‐circuit fault diagnosis results of the battery PACK have a high accuracy, confirming the feasibility and effectiveness of the designed strategy that includes SOC estimation and short‐circuit fault identification and positioning, and has broad application prospects.

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“…In general, determining the SOC of rechargeable lithium-ion batteries is a complex task, due to the non-linear reaction dynamics caused by the electrochemical characteristics of lithium-ion batteries [27,28]. This means that the SOC can only indirectly be calculated by measuring some physical parameters, such as charge/discharge current, terminal voltage, and internal resistance [29], or by using a mathematical model and algorithm [30,31]. However, the physical parameters of lithium-ion batteries are usually affected by some uncertain factors, such as the open-circuit voltage (U oc ) of the battery, temperature variation, battery aging, and overall efficiency of the battery systems [32].…”

Section: Introductionmentioning

confidence: 99%

Novel Ordinary Differential Equation for State-of-Charge Simulation of Rechargeable Lithium-Ion Battery

Nteutse,

Mugenga,

Geletu

et al. 2024

Applied Sciences

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Lithium-ion battery energy storage systems are rapidly gaining widespread adoption in power systems across the globe. This trend is primarily driven by their recognition as a key enabler for reducing carbon emissions, advancing digitalization, and making electricity grids more accessible to a broader population. In the present study, we investigated the dynamic behavior of lithium-ion batteries during the charging and discharging processes, with a focus on the impact of terminal voltages and rate parameters on the state of charge (SOC). Through modeling and simulations, the results show that higher terminal charging voltages lead to a faster SOC increase, making them advantageous for applications requiring rapid charging. However, large values of voltage-sensitive coefficients and energy transfer coefficients were found to have drawbacks, including increased battery degradation, overheating, and wasted energy. Moreover, practical considerations highlighted the trade-off between fast charging and time efficiency, with charging times ranging from 8 to 16 min for different rates and SOC levels. On the discharging side, we found that varying the terminal discharging voltage allowed for controlled discharging rates and adjustments to SOC levels. Lower sensitivity coefficients resulted in more stable voltage during discharging, which is beneficial for applications requiring a steady power supply. However, high discharging rates and sensitivity coefficients led to over-discharging, reducing battery life and causing damage. These new findings could provide valuable insights for optimizing the performance of lithium-ion batteries in various applications.

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State of Charge Estimation of Li-Ion Battery Based on Adaptive Sliding Mode Observer

Cited by 11 publications

References 28 publications

An Improved Dynamic Programming Energy Management System for Parallel Hybrid Electric Vehicles According to Battery Model

An Improved Dynamic Programming Energy Management System for Parallel Hybrid Electric Vehicles According to Battery Model

SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer

Novel Ordinary Differential Equation for State-of-Charge Simulation of Rechargeable Lithium-Ion Battery

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