Overview of batteries and battery management for electric vehicles

Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries

To unlock the promise of electrified transportation and smart grid, emerging advanced battery management systems (BMSs) shall play an important role in health-aware monitoring, diagnosis, and control of widely used lithium-ion (Li-ion) batteries. Sophisticated physics-based battery models incorporated in the advanced BMS can offer valuable battery internal information to achieve improved operational safety, reliability and efficiency, and to extend the lifetime of the batteries. However, developed from the fundamental electrochemical and thermodynamic principles, the rigorous physics-based models are saddled with exceedingly high cognitive and computational complexity for practical applications. This article reviews prevailing order reduction techniques of physics-based Li-ion battery models to facilitate the development of next-generation BMSs. We analyze and comparatively characterize these techniques, mainly from perspectives of model fidelity, computational efficiency, and the scope of applications. By representing many effective and flexible reduced-order models as equivalent circuits, designers and practitioners, who do not have electrochemical expertise but with knowledge of circuit theory, can readily gain insights into multi-physical dynamics as well as their coupling effects inside the batteries. In addition, recommendations are made on how to select appropriate physics-based models for various model-based applications in battery management. Finally, the prospect of physical model-enabled BMSs is discussed, including the potential challenges and future research directions.

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Model Order Reduction Techniques for Physics-Based Lithium-ion Battery Management: A Survey

http://manuscript.elsevier.com/S0378775322001513/pdf/S0378775322001513.pdf

Developing extreme fast charge battery protocols – A review spanning materials to systems

Commandable areas of a modular converter for DC voltage imbalance mitigation in fuel cell systems

Most state-of-the art battery-control strategies rely on voltage-based design limits to address performance and lifetime concerns. Such approaches are inherently conservative. However, by exploiting internal electrochemical quantities, it is possible to control battery performance right up to true physical bounds. This letter develops an extensible framework that combines model predictive control (MPC) with computationally efficient realization algorithm (xRA)-generated reduced-order electrochemical models for the advanced control of lithium-ion batteries. The approach is demonstrated on the fast-charge problem where hard constraints are imposed on problem variables to avoid lithium plating induced performance degradation. This letter establishes a general mathematical foundation for the incorporation of electrochemically rich reduced-order models directly into an MPC framework.

A Computational Framework for Lithium Ion Cell-Level Model Predictive Control Using a Physics-Based Reduced-Order Model

Battery management implement cell balancing algorithms to equalize state of charge of series-connected cells in a battery pack. Balancing strategies range from passive, where a simple resistive circuit is used to drain current from the battery cell, to active, where sophisticated control schemes and advanced circuitry may be employed. Recent development of active balancing control strategies for a modular cell-balancing architecture utilize low-voltage bypass DC-DC converters and a shared low-voltage dc bus. Although these systems show promise in preliminary experiments, questions remain over the inherent stability properties of the architecture. This letter presents a stability analysis for an $N$ -cell battery module and provides insight for design of embedded controllers.

Stability and Control Analysis for Series-Input/Parallel-Output Cell Balancing System for Electric Vehicle Battery Packs

A coupled electro-thermal (CET) model is used in conjunction with linear parameter-varying model predictive control (LPV-MPC) to compute dynamic state of power (SOP) limits for lithium-ion batteries. The LPV approach uses the previous-step ‘tail’ to improve MPC prediction accuracy, while a linearized CET model enables constraints on current, voltage and temperature for accurate and safe power estimation. Improved SOP limits can be used in battery management systems (BMS) for improved long-term battery performance.

An LPV-MPC Inspired Battery SOP Estimation Algorithm Using a Coupled Electro-Thermal Model

This paper presents a novel application of model predictive control (MPC) to the problem of managing lithiumion cell performance using a highly accurate low-order electrothermal equivalent circuit model and is experimentally validated via laboratory experiments. The proposed method uses MPC to ensure compliance with cell-level operational limits and has the potential to extend lifetime by mitigating certain mechanisms of cell degradation leading to capacity fade. A five-state electrothermal model is developed, characterized and validated. Implementation employs an extended nonlinear Kalman filter for state estimation and MPC for controlling charge/discharge current. The complete method is experimentally validated using a 26650 cylindrical format lithium-iron phosphate (LFP) cell. The new method: (i) develops a fully coupled electro-thermal model of cell dynamics; (ii) incorporates a dynamic hysteresis model to improve accuracy; (iii) employs a nonlinear Kalman filter for accurate state estimation to inform the MPC algorithm; and (iv) utilizes a modified form of MPC which correctly models direct feed-through behavior to characterize ohmic resistance.

Lithium-Ion Battery Charging Control Using a Coupled Electro-Thermal Model and Model Predictive Control

Many battery applications require timely estimates of the maximum power that may be either extracted during operation or added during charge. Traditional methods of computing these estimates rely mostly on simple computations using empirically derived limits imposed on cell voltage, current and possibly temperature. While generally effective, such methods do not fully account for the dynamic aspects of the underlying model. This paper proposes a novel method of real-time power limit estimation that uses a model predictive control-inspired approach which enlarges the functional window right up to true established bounds of cell performance.

Aloisio Kawakita de Souza

Papers

A Computational Framework for Lithium Ion Cell-Level Model Predictive Control Using a Physics-Based Reduced-Order Model

Stability and Control Analysis for Series-Input/Parallel-Output Cell Balancing System for Electric Vehicle Battery Packs

An LPV-MPC Inspired Battery SOP Estimation Algorithm Using a Coupled Electro-Thermal Model

Lithium-Ion Battery Charging Control Using a Coupled Electro-Thermal Model and Model Predictive Control

A Low-Cost MPC-Based Algorithm for Battery Power Limit Estimation.