Lithium-ion battery cathode and anode potential observer based on reduced-order electrochemical single particle model

The fast charging of Lithium-ion batteries within electric vehicles can accelerate the side reaction of lithium plating due to an anode potential that occurs as state of charge increases. It is important to monitor the anode potential during battery charging, but it is not practical to measure the inside of the battery directly for a commercial cell. This paper proposes an observer for estimating the cathode and anode potentials based on the reduced-order electrochemical model, which only needs terminal voltage to track the cathode and anode potentials and their internal charge concentration. The observer design is based on the model order reduction and linearisation of a single particle model with electrolyte (SPMe) to achieve acceptable accuracy with a low calculation cost. The linearised model and the designed observer are validated by the experimental results of a three-electrode cell. The results show that the linearised model reduces the operation time by more than 99% compared with the full-order SPMe model using the same processor. The results also verify that the root mean square error of the cathode and anode potential estimated by the observer is less than 0.02 V for a charging current range from 0.3C to 1C. This shows that the developed cathode and anode potential observer based on the reduced-order electrochemical model can be used within real-time control applications to detect the anode potential in real time to avoid battery degradation caused by lithium plating

Efficient electrochemical model for lithium-ion cells

Lithium-ion batteries are used to store energy in electric vehicles. Physical models based on electro-chemistry accurately predict the cell dynamics, in particular the state of charge. However, these models are nonlinear partial differential equations coupled to algebraic equations, and they are computationally intensive. Furthermore, a variable solid-state diffusivity model is recommended for cells with a lithium ion phosphate positive electrode to provide more accuracy. This variable structure adds more complexities to the model. However, a low-order model is required to represent the lithium-ion cells' dynamics for real-time applications. In this paper, a simplification of the electrochemical equations with variable solid-state diffusivity that preserves the key cells' dynamics is derived. The simplified model is transformed into a numerically efficient fully dynamical form. It is proved that the simplified model is well-posed and can be approximated by a low-order finite-dimensional model. Simulations are very quick and show good agreement with experimental data

Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies

In this study, a framework is proposed for battery model identification to be applied in electric vehicle energy storage systems. The main advantage of the proposed approach is having capability to handle different battery chemistries. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and Lithium-Sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parametrisation is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. The use of identified parameters for battery state-of-charge (SOC) estimation is then discussed. It is demonstrated that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery’s open circuit voltage (OCV) is adequate for SOC estimation. However, Li-S battery SOC estimation can be challenging due to the chemistry’s unique features and the SOC cannot be estimated from the OCV-SOC curve alone because of its flat gradient. An observability analysis demonstrates that Li-S battery SOC is not observable using the common state-space representations in the literature. Finally, the problem’s solution is discussed using the proposed framework

Modelling and estimation in lithium-ion batteries: a literature review

Lithium-ion batteries are widely recognised as the leading technology for electrochemical energy storage. Their applications in the automotive industry and integration with renewable energy grids highlight their current significance and anticipate their substantial future impact. However, battery management systems, which are in charge of the monitoring and control of batteries, need to consider several states, like the state of charge and the state of health, which cannot be directly measured. To estimate these indicators, algorithms utilising mathematical models of the battery and basic measurements like voltage, current or temperature are employed. This review focuses on a comprehensive examination of various models, from complex but close to the physicochemical phenomena to computationally simpler but ignorant of the physics; the estimation problem and a formal basis for the development of algorithms; and algorithms used in Li-ion battery monitoring. The objective is to provide a practical guide that elucidates the different models and helps to navigate the different existing estimation techniques, simplifying the process for the development of new Li-ion battery applications.This research received support from the Spanish Ministry of Science and Innovation under projects MAFALDA (PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033/ ERDF,EU) and MASHED (TED2021-129927B-I00), and by FI Joan Oró grant (code 2023 FI-1 00827), cofinanced by the European Union.Peer ReviewedPostprint (published version

Detection and Isolation of Small Faults in Lithium-Ion Batteries via the Asymptotic Local Approach

This contribution presents a diagnosis scheme for batteries to detect and isolate internal faults in the form of small parameter changes. This scheme is based on an electrochemical reduced-order model of the battery, which allows the inclusion of physically meaningful faults that might affect the battery performance. The sensitivity properties of the model are analyzed. The model is then used to compute residuals based on an unscented Kalman filter. Primary residuals and a limiting covariance matrix are obtained thanks to the local approach, allowing for fault detection and isolation by chi-squared statistical tests. Results show that faults resulting in limited 0.15% capacity and 0.004% power fade can be effectively detected by the local approach. The algorithm is also able to correctly isolate faults related with sensitive parameters, whereas parameters with low sensitivity or linearly correlated are more difficult to precise.Comment: 8 pages, 2 figures, 3 tables, conferenc

A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors

Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15–30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced b attery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted