Power Electronics-Enabled Self-X Multicell Batteries: A Design Toward Smart Batteries

The traditional multicell battery design usually employs a fixed configuration to connect multiple cells in series and in parallel during operation in order to achieve the required voltage and current. However, this fixed configuration results in low reliability, low fault tolerance, and non-optimal energy conversion efficiency. This paper proposes a novel power electronics-enabled self-X, multicell battery design. The proposed multicell battery can automatically configure itself according to the dynamic load/storage demand and the condition of each cell. The proposed battery can self-heal from failure or abnormal operation of single or multiple cells, self-balance from cell state variations, and self-optimize to achieve optimal energy conversion efficiency. These features are achieved by a new cell switching circuit and a high performance battery management system proposed in this paper. The proposed design is validated by simulation and experiment for a 6 × 3 cell polymer lithium-ion battery. The proposed design is universal and can be applied to any type and size of battery cells

A Series-Connected Self-Reconfigurable Multicell Battery Capable of Safe and Effective Charging/Discharging and Balancing Operations

Bidirectional DC/DC converters are commonly used for charging and discharging multicell batteries under various modes, such as Pulsed Current (PC), Constant Current (CC), and Constant Current Constant Voltage (CCCV). The charge and discharge are usually terminated by the converters when battery voltages reach some threshold values. However, cell state imbalance is commonly present in traditional multicell batteries, which reduces the available capacities of the batteries in certain charge/discharge cycles and shortens the life cycles of the batteries. To solve this problem, this paper proposes a series-connected, self-reconfigurable, multicell battery with a bidirectional DC/DC converter capable of safe and effective charging, discharging, and balancing operations. The DC/DC converter uses a unified Constant Current Adaptive Voltage (CCAV) control scheme, which can fully charge each cell of the battery without damage as well as discharge the battery safely. Moreover, with the proposed design, balancing and self-healing can be achieved during operation. This enhances the reliability and energy conversion efficiency of the battery. The proposed design is validated by simulation studies for a six-cell, series connected, lithium-ion battery pack. The proposed design is universal and can be applied to any types of batteries

Online State of Charge and Electrical Impedance Estimation for Multicell Lithium-ion Batteries

This paper proposes a hybrid battery model-based high-fidelity state of charge (SOC) and electrical impedance estimation method for multicell lithium-ion batteries. The hybrid battery model consists of an enhanced Coulomb counting algorithm for SOC estimation and an electrical circuit battery model. A particle swarm optimization (PSO)-based online parameter identification algorithm is designed to estimate the electrical parameters of the cells sequentially. An SOC compensator is designed to correct the errors of the enhanced Coulomb counting SOC estimations for the cells sequentially. This leads to an accurate, robust online SOC estimation for individual cells of a battery pack. The proposed method is validated by simulation and experimental data collected from a battery tester for a four-cell polymer lithiumion battery pack. The proposed method is applicable to other types of electrochemical batteries

RECHARGEABLE MULTI - CELL BATTERY

A method for power management of a multi-cell battery includes identifying a desired power value and voltage value, determining a battery voltage value and a battery current value for a battery, determining a number of battery banks from a plurality of battery banks to use for the battery, where each battery bank includes one or more battery cells (or battery modules), checking availability of each of the one or more battery cells (or battery modules), selecting one or more battery banks from the plurality of battery banks, where the selection of a battery bank is based on the availability of the battery cells (or battery modules) included in the battery pack, and a quantity of the selected battery banks is equal to the determined number of battery banks, and connecting the available battery cells (or battery mod ules) in the selected one or more battery banks to form the battery

RECHARGEABLE MULTI-CELL BATTERY

A method for power management of a multi - cell battery includes identifying a desired power value and voltage value , determining a battery voltage value and a battery current value for a battery , determining a number of battery banks from a plurality of battery banks to use for the battery , where each battery bank includes one or more battery cells ( or battery modules ) , checking availability of each of the one or more battery cells ( or battery modules ) , selecting one or more battery banks from the plurality of battery banks , where the selection of a battery bank is based on the availability of the battery cells ( or battery modules ) included in the battery pack , and a quantity of the selected battery banks is equal to the determined number of battery banks , and connecting the available battery cells ( or battery mod ules ) in the selected one or more battery banks to form the battery

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Algorithmic

Model-Based Condition Monitoring and Power Management for Rechargeable Electrochemical Batteries

Rechargeable multicell batteries have been used in various electrical and electronic systems, e.g., renewable energy systems, electric-drive vehicles, commercial electronics, etc. However, there are still concerns about the reliability and performance degradation of rechargeable batteries caused by low thermal stability and the aging process. A properly designed battery management system (BMS) is required for condition monitoring and control of multicell batteries to ensure their safety, reliability, and optimal performance. The goal of this dissertation research was to develop a novel BMS for rechargeable multicell batteries. First, this research developed high-fidelity battery models for online condition monitoring and power management of battery cells. The battery models were capable of capturing the dynamic circuit characteristics, nonlinear capacity and nonlinear open-circuit voltage effects, hysteresis effect, and temperature effect of the battery cells. Second, this research developed a novel self-X, multicell battery design. The proposed multicell battery can automatically configure itself according to the dynamic load/storage demand and the condition of each cell. The proposed battery can self-heal from failure or abnormal operation of single or multiple cells, self-balance from cell state imbalances, and self-optimize to improve energy conversion efficiency. These features were achieved by a highly efficient cell switching circuit and a high-performance condition monitoring and control system. Moreover, this research developed several model-based condition monitoring algorithms based on the proposed battery models. First, a particle swarm optimization-based parameter identification algorithm was developed to estimate the impedance and state of charge (SOC) of batteries using the proposed hybrid battery model. Second, an algorithm combining a regression method for parameter identification, a sliding-mode observer for SOC estimation, and a two-point capacity estimation method were proposed. In addition, an electrical circuit with hysteresis model-based condition monitoring algorithm was proposed. It systematically integrates: a fast upper-triangular and diagonal recursive least square for online parameter identification, a smooth variable structure filter for SOC estimation, and a recursive total least square for maximum capacity and state of health estimation. These algorithms provided accurate, robust condition monitoring for lithium-ion batteries. Due to the low complexity, the proposed second and third algorithms are suitable for the embedded BMS applications. Advisers: Wei Qiao and Liyan Q

A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects

A high-fidelity battery model capable of accurately predicting battery performance is required for proper design and operation of battery-powered systems. However, the existing battery models have at least one of the following drawbacks: 1) requiring intensive computation due to high complexity, 2) not applicable for electrical circuit design and simulation, and 3) not capable of accurately capturing the State of Charge (SOC) and predicting runtime of the battery due to neglecting the nonlinear capacity effects. This thesis proposes a novel hybrid battery model, which takes the advantages of an electrical circuit battery model to accurately predicting the dynamic circuit characteristics of the battery, and an analytical battery model to capturing the nonlinear capacity effects for accurate SOC tracking and runtime prediction of the battery. The proposed battery model is validated by simulation and experimental studies for single-cell and multicell polymer lithium-ion batteries as well as for a lead-acid battery. The proposed model is applicable to other types and sizes of electrochemical battery cells, such as Nikel Cadmium (NiCd) and Nikel Metal Hydride (NiMH). The proposed battery model is computationally effective for simulation, design, and real-time management of battery-powered systems. Adviser: Wei Qia

A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects

A high-fidelity battery model capable of accurately predicting battery performance is required for proper design and operation of battery-powered systems. However, the existing battery models have at least one of the following drawbacks: 1) requiring intensive computation due to high complexity; 2) not applicable for electrical circuit design and simulation; and 3) not capable of accurately capturing the state of charge (SOC) and predicting run time of the battery due to neglecting the nonlinear capacity effects. This paper proposes a novel hybrid battery model, which takes the advantages of an electrical circuit battery model to accurately predicting the dynamic circuit characteristics of the battery and an analytical battery model to capturing the nonlinear capacity effects for the accurate SOC tracking and runtime prediction of the battery. The proposed battery model is validated by the simulation and experimental studies for the single-cell and multicell polymer lithium-ion batteries, as well as for a lead-acid battery. The proposed model is applicable to other types and sizes of electrochemical battery cells. The proposed battery model is computational effective for simulation, design, and real-time management of battery-powered systems

A Multicell Battery System Design for Electric and Plug-in Hybrid Electric Vehicles

The performance of electric vehicles (EVs) and plug-in hybrid electric vehicle (PHEVs) strongly relies on their battery storage system, which consists of multiple battery cells connected in series and parallel. However, cell state variations are commonly present, which reduces the energy conversion efficiency of the battery system. Furthermore, in a large battery system the risk of catastrophic faults of cells increases because a large numbers of cells are used. To solve these problems, this paper proposes a novel power electronics-enabled, self-X, multicell battery system design. The proposed battery system can self-heal from failures or abnormal operations of single or multiple cells and self-balance from cell state variations. These features are achieved by a cell switching circuit and a high performance battery management system (BMS). The proposed design is validated by simulation studies in MATLAB Simulink for a battery system containing five modules connected in series, where each module consists of 6×3 cylindrical lithium-ion cells. The proposed design is scalable to large battery systems for EV/PHEV applications