Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter (1506.08689v1)

Abstract: This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.

• The paper introduces a high-fidelity pseudo-2D thermal-electrochemical model that accurately captures lithium-ion battery dynamics.
• It employs Chebyshev orthogonal collocation and a modified extended Kalman filter to reduce computational complexity and achieve state estimation errors below 1% within 200 seconds.
• Simulations validate the method’s robust performance during high discharge rates and dynamic driving cycles, paving the way for advanced battery management systems.

Overview of Lithium-ion Battery State Estimation with a Thermal-Electrochemical Model

This paper presents rigorous research on the development of a lithium-ion battery state estimation approach grounded on a thermal-electrochemical model using orthogonal collocation and a modified extended Kalman filter (EKF). The research addresses the intrinsic challenges of maintaining accuracy and computational efficiency in battery management systems (BMS), crucial for applications in electric vehicles and grid storage.

Research Contributions

1. Model Development and Discretization: The authors employ the pseudo-two-dimensional (P2D) electrochemical model, coupled with a thermal representation, to accurately capture lithium-ion battery dynamics. The spatial discretization of the partial differential equations uses Chebyshev orthogonal collocation, leading to substantial reductions in computational complexity compared to finite-difference methods. This approach approximates a high-fidelity, spatially-resolved model that remains computationally light enough for real-time applications.
2. State Estimation Algorithm: Utilizing a modified EKF, the algorithm efficiently estimates the model's states from given measurements, overcoming non-linearities and algebraic equations in the DAEs. The EKF is crafted to rapidly adapt to initial errors in the state-of-charge (SOC) and measurement noise, with error reductions below 1% observed within 200 seconds.
3. Simulation and Validation: The implementation is validated against a high-order COMSOL Multiphysics model, demonstrating accurate voltage predictions up to a 10C discharge rate, with computation times reduced by a factor of 30. Under dynamic conditions like those in the Combined ARTEMIS Driving Cycle, the model shows robust performance, accurately estimating states while maintaining low computational demands.

Implications and Significance

The presented methodology offers significant contributions for future BMS designs that necessitate high reliability and efficiency. By integrating a high-fidelity P2D model with a tractable EKF, the paper paves the way for developing advanced, real-time BMS capable of precise state estimation and diagnostics under varied operational conditions. This is essential for innovating battery technologies, improving safety, and optimizing vehicle range.

Future Directions

• Experimental Application: Future research should incorporate experimental validation to bridge the gap between simulation and practical applications, ensuring the model's adaptability to real-world conditions.
• Parameter Identification: Investigation into parameter estimation techniques will further enhance the observability and accuracy of the model, allowing for improved prediction of degradation phenomena and extending battery life.
• Scalability and Implementation: Translating this approach to hardware-embedded systems within BMS could enable enhanced health-conscious control strategies, essential for the next-generation electric vehicles and energy storage solutions.

In conclusion, this research profoundly contributes to the advancement of lithium-ion battery management, setting a strong foundation for scalable, high-precision estimation methods in the domain of electric mobility and energy storage.