A Computationally E cient Online Optimal Charging Algorithm to Minimise Solid Electrolyte Interface Layer Growth in Lithium-ion Battery.

PhD Theses.Lithium-ion batteries have emerged as major energy storage devices over the last few decades. For enhanced battery life, understanding the relevant degradation mechanisms and their control has been a signi cant area of research interest. The dissertation explores the state of health in lithium-ion batteries in terms of solid electrolyte interface layer growth. The proposed optimal strategy gives a quantitative approach to measure the interface layer. A novel non-linear model predictive control algorithm is devised for online optimal charging by explicitly incorporating degradation mechanisms into control to reduce the degradation process. Chemical and mechanical degradation mechanisms have been considered separately for the growth of the interface layer. The work addresses the challenge of minimising layer growth during charging using the rst-order model in chemical degradation. However, the interface layer is modelled based on the break and repair e ect in mechanical degradation. A single particle model is used for optimal charging using orthogonal projection-based model reformulation. Gauss pseudo-spectral method is used for the optimisation of charging trajectories. Results of the optimal algorithm are compared with the traditional constant current constant voltage approach without considering the interface layer growth. The aim of using di erent degradation concepts is to nd similarities in charging patterns in lithium-ion batteries. Moreover, it is ensured that overpotential caused by lithium plating remains in a healthy regime considering chemical degradation, i.e. it must be positive during charging. Simulation results have been presented to demonstrate the advantages of the proposed charging method dealing with two side reactions simultaneously. The dissertation extends the results of the proposed non-linear model predictive control strategy considering chemical degradation in two ways. First, the single particle model with temperature dynamics was adopted to examine the thermal behaviour of lithium-ion batteries and temperature control. Second, the di erential atness method is applied to examine its computational bene ts over pseudo-spectral methods. A brief discussion on implementing the proposed algorithm in a battery management system of electric vehicles is presente

Electrochemical Model-Based Fast Charging: Physical Constraint-Triggered PI Control

This paper proposes a new fast charging strategy for lithium-ion (Li-ion) batteries. The approach relies on an experimentally validated high-fidelity model describing battery electrochemical and thermal dynamics that determine the fast charging capability. Such a high-dimensional nonlinear dynamic model can be intractable to compute in real-time if it is fused with the extended Kalman filter or the unscented Kalman filter that is commonly used in the community of battery management. To significantly save computational efforts and achieve rapid convergence, the ensemble transform Kalman filter (ETKF) is selected and tailored to estimate the nonuniform Li-ion battery states. Then, a health- and safety-aware charging protocol is proposed based on successively applied proportional-integral (PI) control actions. The controller regulates charging rates using online battery state information and the imposed constraints, in which each PI control action automatically comes into play when its corresponding constraint is triggered. The proposed physical constraint-triggered PI charging control strategy with the ETKF is evaluated and compared with several prevalent alternatives. It shows that the derived controller can achieve close to the optimal solution in terms of charging time and trajectory, as determined by a nonlinear model predictive controller, but at a drastically reduced computational cost

Optimal charging and state-of-charge estimation of a Lithium-ion cell using a simplified full homogenised macro-scale model

Advanced battery management systems (BMS) need accurate and computationally efficient Li-ion cell model for optimum operation as the performance of charging and estimation algorithms of BMS are dependent upon the accuracy of the mathematical model of a cell. This research work presents a novel, accurate and computationally efficient electrochemical model and develops charging and estimation algorithm based on the model. The simplified model is based on the novel full homogenised macroscale model (FHM). The simplified FHM model is compared with a simplified model based on the pseudo-two-dimensional (P2D) model. The FHM model is based on the homogenisation theory, while the volume averaging technique is the basis of the P2D model. Diffusion partial differential equations (PDEs) are approximated by ordinary differential equations with time-varying coefficients. The intercalation current and conduction equation are also approximated to develop variants of the simplified model. The diffusion and reaction rate parameters of the FHM model are more accurate at high temperatures than the parameters based on the empirical Bruggeman method, as the FHM model parameters are based on the numerical model of the electrode structure. The simulations results verify that, compared with a similar simplified model based on the P2D model, the proposed simplified FHM model is more accurate at 318K and higher temperature. The output voltage predicted by the proposed simplified model and the simplified P2D model has a root mean square (RMS) tracking error of 0.6% and 2%, respectively, at 1C input current and 318K temperature. The computational time of the proposed simplified model is reduced by 35% compared with that of the FHM model. Subsequently we present optimal charging of Li-ion cell based on the simplified full homogenised macro-scale (FHM) model. A solid electrolyte interface (SEI) layer model is included in the simplified FHM model to quantify health degradation. With these models, a multi-objective optimal control problem subject to constraints from safety concerns is formulated to achieve the health-conscious optimal charging. This constrained optimal control problem is converted to a nonlinear programming problem (NLP). A nonlinear model predictive control (NMPC) strategy is adopted by solving the NLP at each sampling time using the pseudo-spectral optimisation method. The effect of the input current upper bound on the cell film resistance Rfilm and state of health (SoH) reveals that Rfilm and SoH are more sensitive to input current upper bound at lower values of input current upper bound. Simulation results show that the simplified model and pseudo-spectral method are crucial for reducing the computational load to achieve feasible real-time implementation. The proposed algorithm is more efficient in reducing the health degradation than the conventional constant current constant voltage (CCCV ) charging algorithm since it can explicitly handle the film resistance and capacity as health parameters. Multiple cycle charging simulation reveals that the health-conscious algorithm decrease health degradation and increase battery life. Three observers are used and compared for output feedback charging of a Li-ion cell, i.e. extended Kalman filter (EKF), sliding mode observer (SMO) and moving horizon estimator (MHE). The observers are used in a closed-loop with an NMPC for optimal, health-conscious charging of a Li-ion cell. Simulation results show that EKF and SMO have a low computational burden, whereas MHE exhibits superior performance

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously

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

One-Shot Parameter Identification of the Thevenin's Model for Batteries: Methods and Validation

Parameter estimation is of foundational importance for various model-based battery management tasks, including charging control, state-of-charge estimation and aging assessment. However, it remains a challenging issue as the existing methods generally depend on cumbersome and time-consuming procedures to extract battery parameters from data. Departing from the literature, this paper sets the unique aim of identifying all the parameters offline in a one-shot procedure, including the resistance and capacitance parameters and the parameters in the parameterized function mapping from the state-of-charge to the open-circuit voltage. Considering the well-known Thevenin's battery model, the study begins with the parameter identifiability analysis, showing that all the parameters are locally identifiable. Then, it formulates the parameter identification problem in a prediction-error-minimization framework. As the non-convexity intrinsic to the problem may lead to physically meaningless estimates, two methods are developed to overcome this issue. The first one is to constrain the parameter search within a reasonable space by setting parameter bounds, and the other adopts regularization of the cost function using prior parameter guess. The proposed identifiability analysis and identification methods are extensively validated through simulations and experiments

A Study of Computationally Efficient Advanced Battery Management: Modeling, Identification, Estimation and Control

Lithium-ion batteries (LiBs) are a revolutionary technology for energy storage. They have become a dominant power source for consumer electronics and are rapidly penetrating into the sectors of electrified transportation and renewable energies, due to the high energy/power density, long cycle life and low memory effect. With continuously falling prices, they will become more popular in foreseeable future. LiBs demonstrate complex dynamic behaviors and are vulnerable to a number of operating problems including overcharging, overdischarging and thermal runaway. Hence, battery management systems (BMSs) are needed in practice to extract full potential from them and ensure their operational safety. Recent years have witnessed a growing amount of research on BMSs, which usually involves topics such as dynamic modeling, parameter identification, state estimation, cell balancing, optimal charging, thermal management, and fault detection. A common challenge for them is computational efficiency since BMSs typically run on embedded systems with limited computing and memory capabilities. Inspired by the challenge, this dissertation aims to address a series of problems towards advancing BMSs with low computational complexity but still high performance. Specifically, the efforts will focus on novel battery modeling and parameter identification (Chapters 2 and 3), highly efficient optimal charging control (Chapter 4) and spatio-temporal temperature estimation of LiB packs (Chapter 5). The developed new LiB models and algorithms can hopefully find use in future LiB systems to improve their performance, while offering insights into some key challenges in the field of BMSs. The research will also entail the development of some fundamental technical approaches concerning parameter identification, model predictive control and state estimation, which have a prospect of being applied to dynamic systems in various other problem domains