Physics-based battery SOC estimation methods: Recent advances and future perspectives

Wu, Longxing; Lyu, Zhiqiang; Huang, Zebo; Zhang, Chao; Wei, Changyin

doi:10.1016/j.jechem.2023.09.045

Cited by 52 publications

(10 citation statements)

References 150 publications

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“…Part of that is the description of the cell's non-linearities in the equivalent circuit models (ECMs) and electrochemical-based equivalent circuit models (EECMs) through resistors and capacitors using an The main gauging factors to differentiate among these approaches are complexity, accuracy, and processing time, as shown in Figure 2. OCV is technically considered one of the simplest methods of determining a battery's SoC, and the needed relaxation time for the battery to be chemically stable after any charging or discharging cycles is somewhat tricky and might take several hours, which makes it suitable only for providing a general indication if the battery is full or empty [32,33]. Similarly, an ampere-hour approach can give acceptable results if accurate sensors are utilized.…”

Section: State-of-charge Estimation Approachesmentioning

confidence: 99%

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Hussein,

Esoofally,

Donekal

et al. 2024

Batteries

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Batteries have been considered a key element in several applications, ranging from grid-scale storage systems through electric vehicles to daily-use small-scale electronic devices. However, excessive charging and discharging will impair their capabilities and could cause their applications to fail catastrophically. Among several diagnostic indices, state-of-charge estimation is essential for evaluating a battery’s capabilities. Various approaches have been introduced to reach this target, including white, gray, and black box or data-driven battery models. The main objective of this work is to provide an extensive comparison of currently highly utilized machine learning-based estimation techniques. The paper thoroughly investigates these models’ architectures, computational burdens, advantages, drawbacks, and robustness validation. The evaluation’s main criteria were based on measurements recorded under various operating conditions at the Energy Systems Research Laboratory (ESRL) at FIU for the eFlex 52.8 V/5.4 kWh lithium iron phosphate battery pack. The primary outcome of this research is that, while the random forest regression (RFR) model emerges as the most effective tool for SoC estimation in lithium-ion batteries, there is potential to enhance the performance of simpler models through strategic adjustments and optimizations. Additionally, the choice of model ultimately depends on the specific requirements of the task at hand, balancing the need for accuracy with the complexity and computational resources available and how it can be merged with other SoC estimation approaches to achieve high precision.

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Section: State-of-charge Estimation Approachesmentioning

confidence: 99%

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Hussein,

Esoofally,

Donekal

et al. 2024

Batteries

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show abstract

“…This approach is initiated by establishing the electrochemical model or equivalent circuit model (ECM) of the battery. The electrochemical model exhibits high estimation accuracy [15,16]. ECM simulates the battery's internal reactions by constructing a nonlinear dynamic relationship between the open circuit voltage (OCV), load current, and terminal voltage.…”

Section: Introductionmentioning

confidence: 99%

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

Chang,

Li,

Sun

et al. 2024

Batteries

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This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles.

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“…The ongoing evolution of vehicle powertrain is primarily associated with the transition from conventional fossil energy to new energy sources such as hydrogen and electricity, impacting environmental considerations [1,2]. This transition necessitates innovative solutions to deal with the challenge arising from long-distance operations without frequent recharging.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Power Management Strategy for Extended-Range Electric Logistics Vehicles Based on Driving Pattern Recognition

Wei,

Wang,

Chen

et al. 2023

Actuators

Self Cite

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The primary objective of an energy management strategy is to achieve optimal fuel economy through proper energy distribution. The adoption of a fuzzy energy management strategy is hindered due to different reasons, such as uncertainties surrounding its adaptability and sustainability compared to conventional energy control methods. To address this issue, a fuzzy energy management strategy based on long short-term memory neural network driving pattern recognition is proposed. The time-frequency characteristics of vehicle speed are obtained using the Hilbert–Huang transform method. The multi-dimensional features are composed of the time-frequency features of vehicle speed and the time-domain signals of the accelerator pedal and brake pedal. A novel driving pattern recognition approach is designed using a long short-term memory neural network. A dual-input and single-output fuzzy controller is proposed, which takes the required power of the vehicle and the state of charge of the battery as the input, and the comprehensive power of the range extender as the output. The parameters of the fuzzy controller are selected according to the category of driving pattern. The results show that the fuel consumption of the method proposed in this paper is 5.8% lower than that of the traditional fuzzy strategy, and 4.2% lower than the fuzzy strategy of the two-dimensional feature recognition model. In general, the proposed EMS can effectively improve the fuel consumption of extended-range electric vehicles.

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Physics-based battery SOC estimation methods: Recent advances and future perspectives

Cited by 52 publications

References 150 publications

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

Adaptive Fuzzy Power Management Strategy for Extended-Range Electric Logistics Vehicles Based on Driving Pattern Recognition

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