Showing papers on "State of charge published in 2015"

Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles

Abstract: Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SoC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SoC trajectories that are utilized as final-state constraints in the MPC level. This PHEV energy management framework is evaluated under three different scenarios: 1) without traffic flow information; 2) with static traffic flow information; and 3) with dynamic traffic flow information. Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.

Energy Sharing Control Scheme for State-of-Charge Balancing of Distributed Battery Energy Storage System

Abstract: This paper presents an energy sharing state-of-charge (SOC) balancing control scheme based on a distributed battery energy storage system architecture where the cell balancing system and the dc bus voltage regulation system are combined into a single system. The battery cells are decoupled from one another by connecting each cell with a small lower power dc–dc power converter. The small power converters are utilized to achieve both SOC balancing between the battery cells and dc bus voltage regulation at the same time. The battery cells' SOC imbalance issue is addressed from the root by using the energy sharing concept to automatically adjust the discharge/charge rate of each cell while maintaining a regulated dc bus voltage. Consequently, there is no need to transfer the excess energy between the cells for SOC balancing. The theoretical basis and experimental prototype results are provided to illustrate and validate the proposed energy sharing controller.

Double-Quadrant State-of-Charge-Based Droop Control Method for Distributed Energy Storage Systems in Autonomous DC Microgrids

Abstract: In this paper, a double-quadrant state-of-charge (SoC)-based droop control method for distributed energy storage system is proposed to reach the proper power distribution in autonomous dc microgrids. In order to prolong the lifetime of the energy storage units (ESUs) and avoid the overuse of a certain unit, the SoC of each unit should be balanced and the injected/output power should be gradually equalized. Droop control as a decentralized approach is used as the basis of the power sharing method for distributed energy storage units. In the charging process, the droop coefficient is set to be proportional to the nth order of SoC, while in the discharging process, the droop coefficient is set to be inversely proportional to the nth order of SoC. Since the injected/output power is inversely proportional to the droop coefficient, it is obtained that in the charging process the ESU with higher SoC absorbs less power, while the one with lower SoC absorbs more power. Meanwhile, in the discharging process, the ESU with higher SoC delivers more power and the one with lower SoC delivers less power. Hence, SoC balancing and injected/output power equalization can be gradually realized. The exponent n of SoC is employed in the control diagram to regulate the speed of SoC balancing. It is found that with larger exponent n, the balancing speed is higher. MATLAB/simulink model comprised of three ESUs is implemented and the simulation results are shown to verify the proposed approach.

An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots

Abstract: In this brief, to get a more accurate and robust state of charge (SoC) estimation, the lithium-ion battery model parameters are identified using an adaptive unscented Kalman filtering method, and based on the updated model, the battery SoC is estimated consequently. An adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the unscented Kalman filter (UKF) context. The effectiveness of the proposed method is evaluated through experiments under different power duties in the laboratory environment. The obtained results are compared with that of the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms. The comparison shows that the proposed method provides better accuracy both in battery model parameters estimation and the battery SoC estimation.

Power management of an isolated hybrid AC/DC micro-grid with fuzzy control of battery banks

Abstract: This study focuses on the development of a supervisory control scheme for power management and operation of an isolated hybrid AC/DC micro-grid, which consists of an AC micro-grid and a DC micro-grid. In the proposed hybrid micro-grid, wind and diesel generators and AC loads are connected to the AC micro-grid, whereas photovoltaic array and DC loads are tied to the DC micro-grid. Moreover, the authors consider two independent battery banks in the AC and DC micro-grids. Furthermore, the AC and the DC micro-grids are coupled through a bidirectional converter, which can act as an inverter or rectifier. The objectives of the proposed supervisory controller are listed as follows: (i) maximum utilisation of renewable energy sources along with satisfying the load power demand in both AC and DC micro-grids, (ii) maintaining state of charge (SOC) of battery banks in both AC and DC micro-grids and (iii) managing the power exchange between the AC and the DC micro-grids while the reliability of the whole system is taken into account. The supervisory controller is formalised using a state machine approach. For these purposes, 15 distinct operation modes are considered. Furthermore, in order to extend the battery life cycle, a fuzzy controller manages the desired SOC controlling the charge and discharge currents. The effectiveness of the proposed supervisory controller is evaluated through extensive numerical simulations.

Energy Management Strategy for HEVs Including Battery Life Optimization

Abstract: This paper presents an optimal control-based energy management strategy for a parallel hybrid electric vehicle (HEV). Not only does this strategy try to minimize fuel consumption while maintaining the state of charge of the battery within reasonable bounds, it also seeks to minimize wear of the battery and extend its life. This paper focuses on understanding the optimal control solution offered by Pontryagin’s minimum principle (PMP) in this context. Simulation-based results are presented and analyzed, which show that the control algorithm is able to reduce battery wear by decreasing battery operating severity factor with minimal fuel economy penalty. The benefit of this strategy is especially evident when ambient and driving conditions are especially severe.

Robust and Adaptive Estimation of State of Charge for Lithium-Ion Batteries

Abstract: 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.

A Supervisory Control Strategy for Plug-In Hybrid Electric Vehicles Based on Energy Demand Prediction and Route Preview

Abstract: This paper presents a supervisory control strategy for plug-in hybrid electric vehicles based on energy demand prediction and route preview. The aim is to minimize the fuel consumption in real-time operation. This strategy is realized through three successive steps. First, a neural network model is established to predict the energy demand of the vehicle. It reduces the complete traffic data to several statistical parameters, which contributes to ease the prediction process. Second, a mathematical model is proposed to translate the predicted energy demand into a state of charge (SOC) reference of the battery, which significantly simplifies the SOC-programming method. Finally, the adaptive equivalent consumption minimization strategy (ECMS) is used to track the SOC reference and determine the powertrain state. The proposed strategy can optimally distribute the energy between the engine and the motor on a global range and achieve an optimal torque split on a local range. Simulations are carried out on a power-split plug-in hybrid electric bus, and the proposed strategy shows substantial improvements in fuel economy and other indexes compared with the rule-based strategy and the ECMS.

Lyapunov-Based Adaptive State of Charge and State of Health Estimation for Lithium-Ion Batteries

Abstract: This paper presents an adaptive state of charge (SOC) and state of health (SOH) estimation technique for lithium-ion batteries. The adaptive strategy estimates online parameters of the battery model using a Lyapunov-based adaptation law. Therefore, the adaptive observer stability is guaranteed by Lyapunov's direct method. Since no a priori knowledge of battery parameters is required, accurate estimation is still achieved, although parameters change due to aging or other factors. Unlike other estimation strategies, only battery terminal voltage and current measurements are required. Simulation and experimental results highlight the high SOC and SOH accuracy estimation of the proposed technique.

Electrochemical-acoustic time of flight: in operando correlation of physical dynamics with battery charge and health

Abstract: We demonstrate that a simple acoustic time-of-flight experiment can measure the state of charge and state of health of almost any closed battery. An acoustic conservation law model describing the state of charge of a standard battery is proposed, and experimental acoustic results verify the simulated trends; furthermore, a framework relating changes in sound speed, via density and modulus changes, to state of charge and state of health within a battery is discussed. Regardless of the chemistry, the distribution of density within a battery must change as a function of state of charge and, along with density, the bulk moduli of the anode and cathode changes as well. The shifts in density and modulus also change the acoustic attenuation in a battery. Experimental results indicating both state-of-charge determination and irreversible physical changes are presented for two of the most ubiquitous batteries in the world, the lithium-ion 18650 and the alkaline LR6 (AA). Overall, a one- or two-point acoustic measurement can be related to the interaction of a pressure wave at multiple discrete interfaces within a battery, which in turn provides insights into state of charge, state of health, and mechanical evolution/degradation.

Nonlinear Robust Observers for State-of-Charge Estimation of Lithium-Ion Cells Based on a Reduced Electrochemical Model

Abstract: Advanced battery management systems rely on accurate cell- or module-level state-of-charge (SOC) information for effective control, monitoring, and diagnostics. Electrochemical models provide arguably the most accurate and detailed information about the SOC of lithium-ion cells. In this brief, two nonlinear observer designs are presented based on a reduced order electrochemical model. Both observers consist of a Luenberger term acting on nominal errors and a variable structure term for handling model uncertainty. Using Lyapunov’s direct method, the design of the Luenberger term in each observer is formulated as a linear matrix inequality problem, whereas the variable structure term is designed assuming uncertainty bounds. Simulation and experimental studies are included to demonstrate the performance of the proposed observers.