Real-time state of charge estimation of electrochemical model for lithium-ion battery

This paper proposes the real-time Kalman filter based observer for Lithium-ion concentration estimation for the electrochemical battery model. Since the computation limitation of real-time battery management system (BMS) micro-processor, the battery model which is utilized in observer has been further simplified. In this paper, the Kalman filter based observer is applied on a reduced order model of single particle model to reduce computational burden for real-time applications. Both solid phase surface lithium concentration and battery state of charge (SoC) can be estimated with real-time capability. Software simulation results and the availability comparison of observers in different Hardware-in- the-loop simulation setups demonstrate the performance of the proposed method in state estimation and real-time application

Control-oriented implementation and model order reduction of a lithium-ion battery electrochemical model

The use of electrochemical models makes it computationally intractable for online implementation as the model is subject to a complicated mathematical structure including partial-differential equations (PDE). This paper is based on the single particle model with electrolyte dynamics. Methods to solve the PDEs in the governing equations are given. Model order reduction techniques are applied to the electrochemical model to reduce the order from 350 to 14. The models solved by numerical solution, residue grouping method and balanced truncation method are compared with experimental data of a coin cell for validation. The results show that the reduced order model can decrease simulation time 75 times compared with the high order model. And the accuracy of the model is kept with 2.3% root mean square error comparing with the experiment results

Battery power requirements in high-performance electric vehicles

International standards and guidelines regarding characterisation and cycle life testing of batteries in electric vehicles (EVs) currently do not take into account high-performance driving. Using simulation software, track driving in a high-performance vehicle is simulated, and speed-time profiles are recorded. These as well as established driving cycles are used in conjunction with an EV model to determine power profiles at battery terminals. The difference in the resulting power profiles suggest that the evaluation of batteries for the HP segment requires separate characterisation and cycle life tests

Derivation of an effective thermal electrochemical model for porous electrode batteries using asymptotic homogenisation

Thermal electrochemical models for porous electrode batteries (such as lithium ion batteries) are widely used. Due to the multiple scales involved, solving the model accounting for the porous microstructure is computationally expensive, therefore effective models at the macroscale are preferable. However, these effective models are usually postulated ad hoc rather than systematically upscaled from the microscale equations. We present an effective thermal electrochemical model obtained using asymptotic homogenisation, which includes the electrochemical model at the cell level coupled with a thermal model that can be defined either at the cell or the battery level. The main aspects of the model are the consideration of thermal effects, the diffusion effects in the electrode particles, and the anisotropy of the material based on the microstructure, all of them incorporated in a systematic manner. We also compare the homogenised model with the standard electrochemical Doyle, Fuller & Newman model

Electrical and thermal behaviour of pouch-format lithium ion battery cells under high-performance and standard automotive duty-cycles

Six pouch-format cells comprising a carbon anode and nickel-cobalt-manganese (NCM) cathode are characterized. Their 1C discharge capacity and open circuit voltage are determined. Internal Resistance is investigated via Hybrid Pulse Power Characterization tests and Electrochemical Impedance Spectroscopy. They are subsequently subject to two different electrical loading profiles, one representing high-performance (HP) driving applications, the other representing urban and extra-urban driving scenarios. The cells are instrumented with thermocouples to determine their surface temperature during cycling. The experimental results show that HP scenarios result in higher temperatures and temperature gradients, requiring bespoke thermal management strategies and suggesting increased degradation over prolonged use

A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery

Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is a key but challenging issue in the applications of battery health diagnosis and management. This paper applies advanced machine-learning techniques to achieve effective future capacities and RUL prediction for lithium-ion batteries with reliable uncertainty management. To be specific, after using the empirical mode decomposition (EMD) method, the original battery capacity data is decomposed into some intrinsic mode functions (IMFs) and a residual. Then the long short term memory (LSTM) sub-model is applied to estimate the residual while the gaussian process regression (GPR) sub-model is utilized to fit the IMFs with the uncertainty level. Consequently, both the long-term dependence of capacity and uncertainty quantification caused by the capacity regenerations can be captured directly and simultaneously. Experimental aging data from different batteries are deployed to evaluate the performance of proposed LSTM+GPR model in comparison with the solo GPR, solo LSTM, GPR+EMD and LSTM+EMD models. Illustrative results demonstrate the combined LSTM+GPR model outperforms other counterparts and is capable of achieving accurate results for both 1-step and multi-step ahead capacity predictions. Even predicting the RUL at the early battery cycle stage, the proposed data-driven approach still presents good adaptability and reliable uncertainty quantification for battery health diagnosis

Model migration neural network for predicting battery aging trajectories

Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction

Global sensitivity analysis of the single particle lithium-ion battery model with electrolyte

The importance of global sensitivity analysis (GSA) has been well established in many scientific areas. However, despite its critical role in evaluating a model’s plausibility and relevance, most lithium ion battery models are published without any sensitivity analysis. In order to improve the lifetime performance of battery packs, researchers are investigating the application of physics based electrochemical models, such as the single particle model with electrolyte (SPMe). This is a challenging research area from both the parameter estimation and modelling perspective. One key challenge is the number of unknown parameters: the SPMe contains 31 parameters, many of which are themselves non-linear functions of other parameters. As such, relatively few authors have tackled this parameter estimation problem. This is exacerbated because there are no GSAs of the SPMe which have been published previously. This article addresses this gap in the literature and identifies the most sensitive parameter, preventing time being wasted on refining parameters which the output is insensitive to

Nonlinear system-identification of the filling phase of a wet-clutch system

The work presented illustrates how the choice of input perturbation signal and experimental design improves the derived model of a nonlinear system, in particular the dynamics of a wet-clutch system. The relationship between the applied input current signal and resulting output pressure in the filling phase of the clutch is established based on bandlimited periodic signals applied at different current operating points and signals approximating the desired filling current signal. A polynomial nonlinear state space model is estimated and validated over a range of measurements and yields better fits over a linear model, while the performance of either model depends on the perturbation signal used for model estimation

Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries

Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This paper derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regression (GPR) technique is employed to capture the underlying mapping among capacity, storage temperature, and SOC. By modifying the isotropic kernel function with an automatic relevance determination (ARD) structure, high relevant input features can be effectively extracted to improve prediction accuracy and robustness. Experimental battery calendar aging data from nine storage cases are utilized for model training, validation, and comparison, which is more meaningful and practical than using the data from a single condition. Illustrative results demonstrate that the proposed GPR model with ARD Matern32 (M32) kernel outperforms other counterparts and can achieve reliable prediction results for all storage cases. Even for the partial-data training test, multi-step prediction test and accelerated aging training test, the proposed ARD-based GPR model is still capable of excavating the useful features, therefore offering good generalization ability and accurate prediction results for calendar aging under various storage conditions. This is the first known data-driven application that utilizes the GPR with ARD kernel to perform battery calendar aging prognosis