Search

Battery systems are inherently networked systems, in which battery cells or packs are grouped by serial and parallel connections to meet power and capacity specifications. In real-time operation, battery packs must be controlled collaboratively so that their capacities can be fully utilized. By employing both analog charge/discharge currents and discrete-event bypass switches in networked battery systems, this paper develops a new hybrid control methodology to control the states of charge (SOCs) and optimize performance. The paper first establishes necessary and sufficient conditions for controllability under hybrid control mechanism. These conditions are then used to derive control strategies to achieve coordinated SOC control, state consensus, and performance optimization. Control algorithms are developed, their convergence properties proved, and optimality established. Simulation examples are used to demonstrate the methods and their performances using a production battery system. [ABSTRACT FROM AUTHOR]

This paper develops an integrated state estimation and control methodology of networked battery systems that require only terminal voltage and current measurements for reduced cost and complexity. Without direct internal voltage/current measurements and individual control on each battery pack in the system, a networked battery system is inherently uncontrollable and unobservable. By employing bypass power electronics, we show that the networked system can be made controllable and observable by switching control. To coordinate switching control for integrated state of charge (SOC) estimation and control, this paper introduces switching control algorithms that use time division, duty cycle control, and periodic switching. It is shown that under noisy terminal voltage and current measurements, state estimation algorithms are convergent, and the control algorithms can achieve balanced SOC control during operation. The proposed hardware and methodology framework is useful for networked battery systems that can accommodate battery packs of different types, ages, and power/capacity ratings. Case studies using production batteries demonstrate convergence behavior for both state estimation and SOC consensus control under time-varying charging current profiles. The approach will be especially useful for managing battery storage systems to support power grids with renewable energy sources where the battery systems are required to operate continuously. [ABSTRACT FROM AUTHOR]

Jumping disturbances and large noises in input voltage sources to a power converter can cause substantial excursion of its output voltage even under a well-designed feedback controller. Predictive compensation can achieve improved disturbance rejection and tracking performance in such scenarios, resulting in a two-degree-of-freedom design. While the feedback controller has embedded robustness, designing feedforward controllers, which are open-loop compensators, is challenging due to the fact that converter internal parameters change from aging and variations in operating conditions, and loads themselves are part of the converter dynamics. When converter dynamics change, system performance deteriorates significantly, making adaptation mandatory. By integrating system identification with the feedforward compensator, an adaptive feedforward compensation design is proposed in this brief. Working on a boost dc–dc converter as a typical platform, combined feedback and adaptive feedforward design is explored. The results show that the two-degree-of-freedom adaptive design results in much improved performance in rejecting disturbances from input power sources. [ABSTRACT FROM AUTHOR]

This paper analyzes the reliability of large scale battery storage systems consisting of multiple battery modules. The whole system reliability assessment is based on the reliability evaluation of system components including individual battery modules and power electronic converters. In order to evaluate the reliability of a battery module, a reliability model based on the state of health of individual battery cells is introduced. The state of health of a battery cell is calculated based on the capacity fade of the cell using a weighted Ampere-hour throughput method. A universal generating function-based method is then introduced to evaluate the reliability of the battery module. The reliability model of dc/ac power electronic converters is also presented in this paper. The reliability analysis is conducted for battery storage systems with different system configurations and management strategies, and the influence of system configuration on the reliability of battery system is studied. Comparative studies are conducted for a classic battery energy storage system (BESS) and a reconfigurable BESS (RBESS) to demonstrate the advantages of having a reconfigurable system topology. The comparison results show that the proposed RBESS has higher system reliability and more power outputs than the classic BESS. [ABSTRACT FROM PUBLISHER]

This paper analyzes system-level capacity, stored energy, and power loss, and introduces methods for their diagnosis with reliability characterization, for networked battery systems. Since system-level features of a networked battery system are functions of its member battery packs, it is of essential importance to establish rigorously this component-to-system relationship. The paper provides a method to calculate accurately diagnosis error probabilities on the system-level quantities when individual batteries’ measurements are subject to measurement errors and their characterizing parameters are estimated from such measurements. These results are then used to choose diagnosis decision variables such as the thresholds for detecting faulty batteries and data sizes for estimation. Focusing on stored energy, capacity, and power loss, this paper characterizes error statistics of estimation algorithms for these quantities. Convergence of the algorithms, asymptotic probability distributions of the estimates, and diagnosis reliability analysis are performed rigorously by using stochastic differential equations, central limit theorems, and large deviations principles. Simulated case studies and experimental data are used to illustrate the methods and their usefulness. [ABSTRACT FROM PUBLISHER]

Dual-source trolleybuses powered by onboard battery and grid electricity offer unique advantages in fuel economy, cost reduction, and passenger capacity, which are particularly appealing for public transportation in populated cities. Their mobility and power supply network configurations introduce challenging power management issues on their dedicated supply power grids. To ensure safe, reliable, and efficient operation of trolleybuses, their high-current and dynamic loads must be distributed to the feeders properly and promptly. Based on certain emerging networks of feeders and supply stations for city trolleybus systems, this paper introduces a new framework for current flow balancing based on recently developed weighted-and-constrained consensus control methods to manage the power supply network. Using only neighborhood information exchange among feeder lines in the network, the consensus control can achieve global current balancing with fast convergence to a balanced state, robustness to load perturbations, reconfiguration with feeder addition and deletion, and rebalancing under feeder capacity variation. The methodology is scalable in the sense that system expansion will not substantially increase the control system complexity. The power system configurations of the Beijing dual-source trolleybus system are used for simulation case studies on the new power management methods. Robustness and scalability are demonstrated, together with discussions on the feasibility, flexibility, and implementation issues of the methodology. [ABSTRACT FROM PUBLISHER]

The lithium titanate battery, which uses Li 4Ti 5O 12 (LTO) as its anode instead of graphite, is a promising candidate for fast charging and power assist vehicular applications due to its attractive battery performance in rate characteristics and chemical stability. Unfortunately, commonly used battery models, including a large number of enhanced electrical models, become problematic when describing current–voltage characteristics of lithium titanate batteries. In this paper, a novel Butler–Volmer equation-based electric model is employed to outline unique phenomena induced by changing rates for high-power lithium titanate batteries. The robustness of the proposed model for three types of lithium titanate batteries under varying loading conditions, including galvanostatic test and Federal Urban Dynamic Schedule test, is evaluated and compared against experimental data. The experimental results of three types of lithium titanate batteries with common anode materials but differentiated cathode materials show good agreement with the model estimation results with maximum voltage errors below 2%. [ABSTRACT FROM PUBLISHER]

The reliable operation of battery management systems depends critically on the accurate estimation of the state of charge (SOC) and characterizing parameters of a battery system. SOC estimation employs models that must be robust against variations in battery cell electrochemical features, aging, and operating conditions. This paper reveals that commonly used SOC estimation schemes are fundamentally flawed in providing the robustness of SOC estimation against model uncertainties. Parameter estimation methodologies and adaptive SOC estimation design are introduced in this paper to enhance SOC estimation accuracy and robustness. By a scrutiny of the impact of parameter variations on SOC estimation accuracy, the SOC–open-circuit-voltage mapping is identified to be the most critical function that must be accurately established. Identification algorithms are introduced, and their convergence properties are established. The integration of the identification algorithms and SOC estimation schemes lead to an adaptive SOC estimation framework that is superior over the existing methods in providing much improved accuracy and robustness. Experimental studies are conducted to validate the algorithms. [ABSTRACT FROM PUBLISHER]

Reliability of battery diagnosis depends on accurate estimation of the state of charge (SOC) and battery characterizing parameters including maximum capacity, internal impedance, polarization coefficients, and their probabilistic characterizations. This paper develops a framework that employs real-time operating data to estimate jointly the SOC and parameters, performs statistical analysis to derive quantitative diagnostic procedures with error analysis. Convergence of the algorithms, asymptotic distributions, and diagnosis reliability analysis are performed rigorously by using stochastic differential equations, central limit theorems, and large deviations principles. Simulated case studies and experimental data are used to illustrate the diagnosis algorithms and their capabilities. Experimental studies are conducted to verify the results. [ABSTRACT FROM PUBLISHER]

Accurate estimation of the state of charge in battery systems is of essential importance for battery system management. Due to nonlinearity, high sensitivity of the inverse mapping from external measurements, and measurement errors, SOC estimation has remained a challenging task. This is further compounded by the fact that battery characteristic model parameters change with time and operating conditions. This paper introduces an adaptive nonlinear observer design that compensates nonlinearity and achieves better estimation accuracy. A two-time-scale signal processing method is employed to attenuate the effects of measurement noises on SOC estimates. The results are further expanded to derive an integrated algorithm to identify model parameters and initial SOC jointly. Simulations were performed to illustrate the capability and utility of the algorithms. Experimental verifications are conducted on Li-ion battery packs of different capacities under different load profiles. [ABSTRACT FROM AUTHOR]

Renewable energy generation, vehicle electrification, and smart grids rely critically on energy storage devices for enhancement of operations, reliability, and efficiency. Battery systems consist of many battery cells, which have different characteristics even when they are new, and change with time and operating conditions due to a variety of factors such as aging, operational conditions, and chemical property variations. Their effective management requires high fidelity models. This paper aims to develop identification algorithms that capture individualized characteristics of each battery cell and produce updated models in real time. It is shown that typical battery models may not be identifiable, unique battery model features require modified input/output expressions, and standard least-squares methods will encounter identification bias. This paper devises modified model structures and identification algorithms to resolve these issues. System identifiability, algorithm convergence, identification bias, and bias correction mechanisms are rigorously established. A typical battery model structure is used to illustrate utilities of the methods. [ABSTRACT FROM PUBLISHER]