Changfu Zou

Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs

Battery energy storage effectively staBIlizes the electric grid and aids renewable integration by balancing supply and demand in real time. The importance of such storage is especially crucial in densely populated urban areas, where traditional storage techniques such as pumped hydroelectric energy storage and compressed-air energy storage are often not feasible.

A Framework for Simplification of PDE-Based Lithium-Ion Battery Models

Simplified models are commonly used in battery management and control, despite their (often implicit) limitations in capturing the dynamic behavior of the battery across a wide range of operating conditions. This paper seeks to develop a framework for battery model simplification starting from an initial high-order physics-based model that will explicitly detail the assumptions underpinning the development of simplified battery models. Starting from the basis of a model capturing the electrochemical, thermal, electrical, and aging dynamics in a set of partial differential equations, a systematic approach based on singular perturbations and averaging is used to simplify the dynamics through identification of disparate timescales inherent in the problem. As a result, libraries of simplified models with interconnections based on the specified assumptions are obtained. A quantitative comparison of the simplified models relative to the original model is used to justify the model reductions. To demonstrate the utility of the framework, a set of battery charging strategies is evaluated at reduced computational effort on simplified models.

Electrochemical Estimation and Control for Lithium-Ion Battery Health-Aware Fast Charging

Fast charging strategies have gained an increasing interest toward the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifetime, and charging time, this paper proposes a health-aware fast charging strategy synthesized from electrochemical system modeling and advanced control theory. The battery charging problem is formulated in a linear time-varying model predictive control algorithm. In this algorithm, a control-oriented electrochemical–thermal model is developed to predict the system dynamics. Constraints are explicitly imposed on physically meaningful state variables to protect the battery from hazardous operations. A moving horizon estimation algorithm is employed to monitor battery internal state information. Illustrative results demonstrate that the proposed charging strategy is able to largely reduce the charging time from its benchmarks while ensuring the satisfaction of health-related constraints.

PDE battery model simplification for SOC and SOH estimator design

Accurate knowledge of the battery state-of-charge (SOC) and state-of-health (SOH) is critical for optimal and safe utilisation of the battery. Although the battery system dynamics contain electrochemical, thermal, electrical, and ageing phenomena, most state estimators resort to equivalent circuit models (ECM). These models are often not accurate and are problematic for SOC estimation during an extended range of operations and do not address SOH dynamics. In this paper, starting from an initial high-fidelity Lithium-ion (Li-ion) battery model consisting of a set of partial differential equations (PDE), a recently proposed framework for PDE battery model simplification is employed and one of these obtained models is used for battery state estimation. Model order reduction techniques are then constructively applied to the simplified PDE battery model and resulted in a computationally efficient ordinary differential equation (ODE) model. Based on this obtained ODE model, an extended Kalman filter (EKF) is designed for the estimation of both SOC and SOH. Simulations over 20 cycles show the designed estimator is capable of simultaneously estimating the batterys SOC in each electrode and SOH.

Model Predictive Control for Lithium-Ion Battery Optimal Charging

Charging time and lifetime are important performances for lithium-ion (Li-ion) batteries, but are often competing objectives for charging operations. Model-based charging controls are challenging due to the complicated battery system structure that is composed of nonlinear partial differential equations and exhibits multiple time-scales. This paper proposes a new methodology for battery charging control enabling an optimal tradeoff between the charging time and battery state-of-health (SOH). Using recently developed model reduction approaches, a physics-based low-order battery model is first proposed and used to formulate a model-based charging strategy. The optimal fast charging problem is formulated in the framework of tracking model predictive control (MPC). This directly considers the tracking performance for provided state-of-charge and SOH references, and explicitly addresses constraints imposed on input current and battery internal state. The capability of this proposed charging strategy is demonstrated via simulations to be effective in tracking the desirable SOH trajectories. By comparing with the constant-current constant-voltage charging protocol, the MPC-based charging appears promising in terms of both the charging time and SOH. In addition, this obtained charging strategy is practical for real-time implementation.

Nonlinear Fractional-Order Estimator With Guaranteed Robustness and Stability for Lithium-Ion Batteries

This paper proposes a new estimator design algorithm for state-of-charge (SoC) indication of lithium-ion batteries. A fractional-order model-based nonlinear estimator is first framed including a Luenberger term and a sliding mode term. The estimator gains are designed by Lyapunovs direct method, providing a guarantee for stability and robustness of the error system under certain assumptions. This generic estimation algorithm is then applied to lithium-ion batteries. A fractional-order circuit model is adopted to predict battery dynamic behaviours. Assumptions based on which the estimation algorithm is developed are justified and remarked. Experiments corresponding to electric vehicle applications are conducted to parameterize the battery model and demonstrate the estimation performance. It shows that the proposed approach is able to estimate SoC with errors less than 0.03 in the presence of initial deviation and persistent noise. Furthermore, the benefits of using the proposed estimator relative to other estimators are calculated over different cycles and conditions.

Control-Oriented Modeling of a Lithium-Ion Battery for Fast Charging

Abstract The fast charge battery control problem is characterized by the need to have a sufficiently detailed model that can capture both the charging process and the inevitable constraints that limit the rate of charging due to battery state of health requirements. Presently, it appears the minimal modeling requirements to address the charging problem in model based fashion are unknown. This work seeks to develop a modeling methodology covering a large range of applications through systematically simplifying the partial differential equations that describe battery dynamics. The effects of grid resolution and polynomial order on the complexity and accuracy of reduced order models have been investigated to provide insight into the minimum modeling requirements at different charge rates. The proposed models are intended for controller design and optimization applications including fast charge control.

Simplification techniques for PDE-based Li-Ion battery models

Battery systems are becoming increasingly prevalent as a source of power for applications across domains from consumer electronics to automotive, due to a range of factors such as portability and environmental considerations. The relatively high cost of batteries leads to a natural tradeoff in their use to ensure the lifetime of the battery is not unduly compromised while still delivering good performance. Similar tradeoffs have been successfully dealt with in other systems using model based control and estimation techniques, and this motivates their use for battery systems. Complicating this process is the complex nature of the physics-based models describing the operation of a battery cell, as these consist of a large number of partial differential equations spanning multiple, coupled domains. This second paper of the tutorial session will briefly review the existing physics-based battery models, and introduce recent approaches that have been used to develop simplified models based on the original high-fidelity model. The assumptions underpinning the model simplification will be presented and discussed.

Distributed Thermal-Electrochemical Modeling of a Lithium-Ion Battery to Study the Effect of High Charging Rates

Abstract In this paper, we investigate distributed thermal-electrochemical modeling of a Lithium-Ion battery cell to include the effect of temperature distribution across the thickness of the cell as a first step to study the module level temperature distribution at high charging rates. Most recent works have focused on lumped thermal models for a Li-Ion cell which ignore any temperature differential across cell thickness. However, even a small temperature differential across cell thickness at the cell level can contribute to significant temperature differential in the thickness direction of stacked-up Li-Ion cells at the module level. Such temperature differential can potentially impact the battery charging control system, especially at high charging rates. Here, the thermal-electrochemical partial differential and algebraic equations for a Li-ion cell are solved via a spatial finite difference method. Simulation results show that the temperature differentials over the cell thickness at the cell level are not insignificant, particularly at high charging rates.

PDE battery model simplification for charging strategy evaluation

A safe, fast charging strategy is desired in the utilisation of rechargeable Lithium-ion batteries. Traditionally, experimental methods are used in exploring and evaluating new strategies, but these require extensive time and cost. This paper aims to establish a model-based system for quick and accurate evaluation of charging strategies. Starting from a nonlinear coupled partial differential equation (PDE) battery model that accurately captures system dynamics, simplification techniques are conducted based on the identification of separable time scales within the states. By pertinent use of a singular perturbation approach, a PDE model simplification framework containing families of simplified battery models is established. All assumptions are explicitly stated and shown to enable families of simplified models to be rigorously justified. An evaluation procedure synthesised from the simplified models and averaging theory is proposed. This procedure is implemented on several typical battery charging strategies. The benefits relative to simulation on other higher order models are assessed in terms of computational efficiency and accuracy and demonstrate significant computational savings are possible with the proposed approach.