BattX: An equivalent circuit model for lithium-ion batteries over broad current ranges

Biju, Nikhil; Fang, Huazhen

doi:10.1016/j.apenergy.2023.120905

Cited by 23 publications

(2 citation statements)

References 36 publications

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“…A single-cell battery model is in Fig. 9 [30]. To understand the battery model concept, a voltage source is connected to a series resistor, namely open circuit voltage (VOCV) and internal resistance (RO).…”

Section: B Batterymentioning

confidence: 99%

A Novel Intelligent Controller-Based Power Management System With Instantaneous Reference Current in Hybrid Energy-Fed Electric Vehicle

Mathesh,

Saravanakumar

2023

IEEE Access

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Nowadays, more than one power source is needed to fulfil the power demand for electric vehicles. The multiple sources enhance reliability regarding the cruising range and decrease the charging cost. However, the inclusion of multi-sources generally gives rise to issues in the controller unit, such as slow response due to the immediate changes in load and power conversion complexity while switching the sources. This paper presents a novel intelligent control scheme based on fuzzy logic to mitigate this issue. The proposed controller includes a solar panel, a fuel cell, and a battery as input source. In this work, to examine the instantaneous reference currents from the sources and to manage power for the electric vehicle motor, a permanent magnet synchronous machine (PMSM) is considered. The proposed controller performs real-time power management, Maximum power point tracking (MPPT) for the PV system, and load calculations based on vehicle dynamics. The proposed work explores power management techniques for efficient control by utilizing real-time irradiance data from the Solcast website and drive cycle data collected at a university campus. Finally, the proposed controller was developed using MATLAB/Simulink and implemented as a hardware investigation with the LabVIEW tool and FPGA controller for a 1 kW PMSM to validate that the controller enables consistent power split operation in different load conditions.

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Section: B Batterymentioning

confidence: 99%

A Novel Intelligent Controller-Based Power Management System With Instantaneous Reference Current in Hybrid Energy-Fed Electric Vehicle

Mathesh,

Saravanakumar

2023

IEEE Access

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“…The model was successfully implemented, and the results showed that the simulations ran many orders of magnitude faster than the state-of-the-art models, making it suitable for online monitoring. Additionally, in reference [55], the authors introduced a framework aimed at simplifying the DFN by mapping its diffusion phases, thermal dynamics, and voltage output to equivalent electric circuit models. The results showed high fidelity at low computation cost.…”

Section: Literature Reviewmentioning

confidence: 99%

Determination of Fast Battery-Charging Profiles Using an Electrochemical Model and a Direct Optimal Control Approach

Gonzalez-Saenz,

Becerra

2023

Batteries

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This paper describes an approach to determine a fast-charging profile for a lithium-ion battery by utilising a simplified single-particle electrochemical model and direct collocation methods for optimal control. An optimal control problem formulation and a direct solution approach were adopted to address the problem effectively. The results shows that, in some cases, the optimal current profile resembles the current profile in the Constant Current–Constant Voltage charging protocol. Several challenges and knowledge gaps were addressed in this work, including a reformulation of the optimal control problem that utilises direct methods as an alternative to overcome the limitations of indirect methods employed in similar studies. The proposed formulation considers the minimum-time optimal control case, trade-offs between the total charging time, the maximisation of the lithium bulk concentration, and energy efficiency, along with inequality constraints and other factors not previously considered in the literature, which can be helpful in practical applications.

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Strong tracking adaptive window multi‐innovation cubature Kalman filter algorithm for lithium‐ion battery state of energy estimation

Lin,

Wang,

Yang

2024

Circuit Theory & Apps

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SummaryAccurate estimation of lithium‐ion battery state of energy (SOE) is an important prerequisite for prolonging battery life and ensuring battery safety. To achieve a high‐precision estimation of the SOE, while a ternary lithium‐ion battery being the specifically targeted in this study, a novel method for SOE estimation is proposed, which combines limited‐memory recursive least squares (LM‐RLS) with strong tracking adaptive window Multi‐innovation cubature Kalman filtering (STW‐MCKF). In the LM‐RLS algorithm, the model parameters at the current time are updated with a limited dataset to solve the data saturation problem and improve the recognition accuracy of the RLS algorithm. The CKF algorithm is optimized by the STW algorithm in the STW‐CKF algorithm to enhance its robustness under strong disturbances. Additionally, a self‐adaptive window multiple innovation strategy is proposed to improve the accuracy of SOE estimation and the stability of the CKF algorithm, while maintaining a balance between computational complexity and SOE estimation accuracy. To validate the effectiveness of the algorithm, experiments are conducted under DST and BBDST conditions. The results show that the STW‐MCKF algorithm has a maximum convergence time of 4 s and an SOE estimation error within 1.04% under DST conditions. Under BBDST conditions, the maximum convergence time is 3 s, and the SOE estimation error is within 2.34%. Furthermore, the STW‐MCKF algorithm demonstrates good stability under the two conditions, indicating the effectiveness of the proposed method for lithium‐ion battery SOE estimation.

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BattX: An equivalent circuit model for lithium-ion batteries over broad current ranges

Cited by 23 publications

References 36 publications

A Novel Intelligent Controller-Based Power Management System With Instantaneous Reference Current in Hybrid Energy-Fed Electric Vehicle

A Novel Intelligent Controller-Based Power Management System With Instantaneous Reference Current in Hybrid Energy-Fed Electric Vehicle

Determination of Fast Battery-Charging Profiles Using an Electrochemical Model and a Direct Optimal Control Approach

Strong tracking adaptive window multi‐innovation cubature Kalman filter algorithm for lithium‐ion battery state of energy estimation

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