Search

Simultaneous vibration control and energy harvesting of vehicle suspensions have attracted significant research attention over the past decades. However, existing energy harvesting shock absorbers (EHSAs) compromise suspension performance for high-efficiency energy harvesting and being only responsive to narrow-bandwidth vibrations. In this paper, we propose a new ball-screw-based EHSA design – inerter pendulum vibration absorber (IPVA) – that integrates an electromagnetic rotary EHSA with a nonlinear pendulum vibration absorber.We show that this design simultaneously improves ride comfort and energy harvesting efficiency by exploiting the nonlinear effects of pendulum inertia. To further improve the performance, we develop a novel stochastic linearization model predictive control (SL-MPC) approach in which we employ stochastic linearization to approximate the nonlinear dynamics of EHSA that has superior accuracy compared to standard linearization. In particular, we develop a new stochastic linearization method with guaranteed stabilizability, which is a prerequisite for control designs. This leads to an MPC problem that is much more computationally efficient than the nonlinear MPC counterpart with no major performance degradation. Also, the effect of different road preview configurations on control performance is investigated, which is shown to have a significant impact on the control performance. Extensive simulations are performed to show the superiority of the proposed new nonlinear EHSA and to demonstrate the efficacy of the proposed SL-MPC. [ABSTRACT FROM AUTHOR]

Integrated power system combines electrical power for both ship service and electric propulsion loads by forming a microgrid. In this article, a battery/flywheel hybrid energy storage system (HESS) is studied to mitigate load fluctuations in a shipboard microgrid. This article focuses on how to determine the reference operation state of the flywheel, which depends on both future power load and the power split between the battery and flywheel. Two control strategies are proposed—an optimization-based approach and a lookup-table-based approach. Case studies are performed in different sea conditions, and simulation results demonstrate that the proposed control strategies outperform baseline control strategies in terms of power fluctuation mitigation and HESS power-loss reduction. A comparison between the two proposed approaches is performed, where their performances are quantified, the advantages and disadvantages of each strategy are analyzed, and the cases where they are most applicable are highlighted. [ABSTRACT FROM AUTHOR]

A battery/supercapacitor hybrid energy storage system (HESS) is overactuated in the sense that there are two power sources providing a single power output. This feature of HESS is exploited in this article to simultaneously achieve accurate identification of the battery states/parameters and high system efficiency. By actively injecting current signals, the state of charge and state of health, together with other battery parameters, can be identified sequentially. Sufficient richness in the input (i.e., battery current) is necessary to ensure identification accuracy. Since signal richness for identification can be in conflict with efficient operation, a novel model predictive control (MPC) strategy is used to simultaneously consider both objectives to determine the optimal power distribution between supercapacitor and battery. The tradeoff between identification accuracy and system efficiency is investigated. Simulation results show that the proposed MPC can significantly improve identification accuracy at the expense of a slight decrease in system efficiency when compared to the baseline MPC, which does not consider the signal richness. Therefore, it is validated that the proposed MPC can effectively achieve simultaneous identification and efficient operation. [ABSTRACT FROM AUTHOR]

Microscopic traffic models describe how cars interact with their neighbors in an uninterrupted traffic flow and are frequently used for reference in advanced vehicle control design. In this paper, we propose a novel mechanical system-inspired microscopic traffic model using a mass-spring-damper-clutch system. This model naturally captures the ego vehicle’s resistance to large relative speed and deviation from a (driver- and speed-dependent) desired relative distance when following the lead vehicle. Compared with the existing car-following (CF) models, this model offers physically interpretable insights into the underlying CF dynamics and is able to characterize the impact of the ego vehicle on the lead vehicle, which is neglected in the existing CF models. Thanks to the nonlinear wave propagation analysis techniques for mechanical systems, the proposed model, therefore, has great scalability so that multiple mass-spring-damper-clutch systems can be chained to study the macroscopic traffic flow. We investigate the stability of the proposed model on the system parameters and the time delay using the spectral element method. We also develop a parallel recursive least square with inverse QR decomposition (PRLS-IQR) algorithm to identify the model parameters online. These real-time estimated parameters can be used to predict the driving trajectory that can be incorporated into advanced vehicle longitudinal control systems for improved safety and fuel efficiency. The PRLS-IQR is computationally efficient and numerically stable, and therefore, it is suitable for online implementation. The traffic model and the parameter identification algorithm are validated on both the simulations and naturalistic driving data from multiple drivers. Promising performance is demonstrated. [ABSTRACT FROM AUTHOR]

Estimating the State-of-Charge (SoC) and State-of-Health (SoH), together with the parameters used in representing the dynamics of a lithium-ion battery, is essential to ensure optimal and reliable operation. However, this simultaneous estimation can take a significant amount of time to converge, and the estimation accuracy is limited by measurement noise and model inaccuracy. Note that for overactuated systems (e.g., hybrid energy storage systems and hybrid electric vehicles), the overactuation feature can be exploited to optimize the battery current profile for the estimation purpose. This article shows the potential to improve estimation accuracy when the desired current is actively injected and the estimation algorithm is properly structured. Specifically, by incorporating a high-pass filter, battery parameters can be independently characterized by injecting high-frequency and medium-frequency currents, and battery SoC/SoH can then be estimated sequentially from the estimated parameters. A Cramer–Rao bound analysis shows that the accuracy of the proposed sequential estimation is much better than the case where all parameters and states are simultaneously estimated. The analysis is verified by simulation and experimental results. We point out that for battery-only applications (e.g., electric vehicles); the proposed method has limitations, as generally, the battery current profile cannot be changed. [ABSTRACT FROM AUTHOR]

Online State of Charge (SoC) and State of Health (SoH) estimations are essential for efficient, safe, and reliable operation of Lithium ion batteries. Based on the first-order equivalent-circuit model (ECM), a multi-scale extended Kalman filter is adopted in this paper to estimate ECM parameters and battery SoC using dual time scales. The nature of the battery excitations significantly influences the estimation performance. When the input–output data, i.e., the input current and output voltage, is insufficiently rich in frequency content, the estimation performance is poor. Thus, the excitation current should be optimized for the accurate estimation of parameters and states. A Cramer–Rao bound analysis is conducted considering voltage noise, current amplitude, and current frequency, which shows the loss of accuracy in multi-parameter estimation (estimating all states and parameters) when compared to single-parameter estimation (estimating only one parameter/state). However, it also shows that the loss of accuracy can be significantly reduced when the excitation current is carefully chosen to satisfy certain criteria. Both simulation and experimental results verify the analysis results and show that a current profile with optimal frequency components achieves the best estimation performance, thereby, providing guidelines for designing battery current profiles for improved SoC and SoH estimation performance. [ABSTRACT FROM AUTHOR]

This paper presents the analysis, design, and experimental validation of parameter identification of battery/supercapacitor (SC) hybrid energy storage system (HESS) for the purpose of condition monitoring and maximum power estimation. The analytic bounds on the error of battery and SC parameter identification, considering voltage measurement noise, are obtained based on the Fisher information matrix and Cramer–Rao bound analysis. The identification of different parameters requires different signal patterns to ensure high accuracy, rendering tradeoffs in the multiparameter identification process. With an appropriately designed current profile, HESS parameters are identified using recursive least squares with a forgetting factor. The identified parameters are then used to estimate the maximum power capability of the HESS. The maximum power capabilities of the battery and SC are estimated for both 1 and 30 s time horizons. The parameter identification algorithm can be applied to systems including either batteries or SCs when the optimal excitation current can be injected. Experimental validation is conducted on an HESS test-bed, which shows that the proposed algorithm is effective in estimating the HESS maximum power based on appropriate current excitation. [ABSTRACT FROM AUTHOR]