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Sökning: WFRF:(Hu Xiaosong)

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1.
  • Hu, Xiaosong, et al. (författare)
  • Cost-optimal energy management of hybrid electric vehicles using fuel cell/battery health-aware predictive control
  • 2020
  • Ingår i: IEEE Transactions on Power Electronics. - 0885-8993 .- 1941-0107. ; 35:1, s. 382-392
  • Tidskriftsartikel (refereegranskat)abstract
    • Energy management is an enabling technology for increasing the economy of fuel cell/battery hybrid electric vehicles. Existing efforts mostly focus on optimization of a certain control objective (e.g., hydrogen consumption), without sufficiently considering the implications for on-board power sources degradation. To address this deficiency, this article proposes a cost-optimal, predictive energy management strategy, with an explicit consciousness of degradation of both fuel cell and battery systems. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, a model predictive control framework, for the first time, is established to minimize the total running cost of a fuel cell/battery hybrid electric bus, inclusive of hydrogen cost and costs caused by fuel cell and battery degradation. The efficacy of this framework is evaluated, accounting for various sizes of prediction horizon and prediction uncertainties. Second, the effects of driving and pricing scenarios on the optimized vehicular economy are explored.
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2.
  • Deng, Zhongwei, et al. (författare)
  • Battery health evaluation using a short random segment of constant current charging
  • 2022
  • Ingår i: iScience. - : Elsevier BV. - 2589-0042. ; 25:5
  • Tidskriftsartikel (refereegranskat)abstract
    • Accurately evaluating the health status of lithium-ion batteries (LIBs) is significant to enhance the safety, efficiency, and economy of LIBs deployment. However, the complex degradation processes inside the battery make it a thorny challenge. Data-driven methods are widely used to resolve the problem without exploring the complex aging mechanisms; however, random and incomplete charging-discharging processes in actual applications make the existing methods fail to work. Here, we develop three data-driven methods to estimate battery state of health (SOH) using a short random charging segment (RCS). Four types of commercial LIBs (75 cells), cycled under different temperatures and discharging rates, are employed to validate the methods. Trained on a nominal cycling condition, our models can achieve high-precision SOH estimation under other different conditions. We prove that an RCS with a 10mV voltage window can obtain an average error of less than 5%, and the error plunges as the voltage window increases.
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3.
  • Deng, Zhongwei, et al. (författare)
  • Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data
  • 2022
  • Ingår i: IEEE transactions on power electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8993 .- 1941-0107. ; 37:5, s. 5021-5031
  • Tidskriftsartikel (refereegranskat)abstract
    • The rapid development of battery technology has promoted the deployment of electric vehicles (EVs). To ensure the healthy and sustainable development of EVs, it is urgent to solve the problems of battery safety monitoring, residual value assessment, and predictive maintenance, which heavily depends on the accurate state-of-health (SOH) estimation of batteries. However, many published methods are unsuitable for actual vehicle conditions. To this end, a data-driven method based on the random partial charging process and sparse Gaussian process regression (GPR) is proposed in this article. First, the random capacity increment sequences (oQ) at different voltage segments are extracted from the partial charging process. The average value and standard deviation of oQ are used as features to indicate battery health. Second, correlation analysis is conducted for three types of batteries, and high correlations between the features and battery SOH are verified at different temperatures and discharging current rates. Third, by using the proposed features as inputs, sparse GPR models are constructed to estimate the SOH. Compared with other data-driven methods, the sparse GPR has the highest estimation accuracy, and its average maximum absolute errors are only 2.88%, 2.52%, and 1.51% for three different types of batteries, respectively.
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4.
  • Hu, Jutao, et al. (författare)
  • The origin of anomalous hydrogen occupation in high entropy alloys
  • 2022
  • Ingår i: Journal of Materials Chemistry A. - : Royal Society of Chemistry (RSC). - 2050-7488 .- 2050-7496. ; 10:13, s. 7228-7237
  • Tidskriftsartikel (refereegranskat)abstract
    • Metal hydrogen storage materials have been the focus of intensive research in the field of hydrogen-based economy. An outstanding question is that the number of hydrogen atoms accommodated in metal hydrides is generally much below the number of interstices, which limits their hydrogen storage capacities. Unlike traditional FCC metal hydrides where hydrogen can only occupy tetrahedral interstices, this study demonstrates that hydrogen can also occupy octahedral interstices in FCC high entropy alloy (HEA) hydrides, which leads to the violation of the Switendick criterion. For Ti25V25Nb25Ta25 and Ti25V25Nb25Zr25 HEAs, nearly 20% and 17.5% of octahedral interstices can be occupied by hydrogen, respectively. The anomalous hydrogen occupation mainly originates from the intrinsic electron delocalization between hydrogen atoms in HEA hydrides, which presents a sharp contrast to traditional metal hydrides. Such electron delocalization decreases repulsive interactions between hydrogens and promotes the electron localization at octahedral interstices. Additionally, this study reveals that hydrogen occupation at octahedral interstices enhances the structural disordering and decreases the thermal stability of HEA hydrides, which will be beneficial to reduce the dehydrogenation temperature. The presented results may provide a new strategy for the design of high-density storage materials.
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5.
  • Hu, Xiaosong, et al. (författare)
  • A Novel Multi-scale Co-estimation Framework of State of Charge, State of Health, and State of Power for Lithium-Ion Batteries
  • 2018
  • Ingår i: JOINT INTERNATIONAL CONFERENCE ON ENERGY, ECOLOGY AND ENVIRONMENT ICEEE 2018 AND ELECTRIC AND INTELLIGENT VEHICLES ICEIV 2018. - : DEStech Publications. - 2475-8833. - 9781605955902
  • Konferensbidrag (refereegranskat)abstract
    • Considering the underlying coupling among State of Charge (SOC), State of Health (SOH), and State of Power (SOP), this work proposes a novel multi-timescale co-estimation framework for these lithium-ion battery states. A modified moving horizon estimator (mMHE) is applied to the SOC estimation in real time. The model parameters affecting the SOP estimation are periodically updated through an mMHE optimization with a relatively long time horizon. The ampere-hour integral and the estimated SOC are employed to realize the SOH estimation offline. The effectiveness of the joint SOC/SOH/SOP estimation is validated experimentally on real-world batteries.
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6.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Advanced Power-Source Integration in Hybrid Electric Vehicles: Multicriteria Optimization Approach
  • 2015
  • Ingår i: IEEE Transactions on Industrial Electronics. - 0278-0046 .- 1557-9948. ; 62:12, s. 7847-7858
  • Tidskriftsartikel (refereegranskat)abstract
    • System integration and power-flow control of on-board power sources are critical to the performance and cost competitiveness of hybrid electric vehicles (HEVs). The existing methods mostly focus on fuel minimization in hybrid powertrains, while disregarding many other concerns. This article presents an innovative multicriteria optimization approach and showcases its validity and usefulness in a case study of a fuel-cell hybrid bus. Three key technical contributions are made. First, a convex multicriteria optimization framework is devised for quickly and efficiently evaluating the optimal tradeoffs between the fuel-cell durability and hydrogen economy in the bus, as well as the corresponding fuel-cell dimension. Second, the impact of driving pattern on both the optimal fuel-cell size and Pareto optimality is investigated by considering discrepant driving schedules. Finally, a preliminary but useful economic assessment in both current and future scenarios is performed to explore the most cost-effective tradeoff.
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7.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling
  • 2016
  • Ingår i: IEEE Transactions on Industrial Electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0046 .- 1557-9948. ; 63:4, s. 2645-2656
  • Tidskriftsartikel (refereegranskat)abstract
    • Battery health monitoring and management is of extreme importance for the performance and cost of electric vehicles. This paper is concerned with machine-learning-enabled battery state-of-health (SOH) indication and prognosis. The sample entropy of short voltage sequence is used as an effective signature of capacity loss. Advanced sparse Bayesian predictive modeling (SBPM) methodology is employed to capture the underlying correspondence between the capacity loss and sample entropy. The SBPM-based SOH monitor is compared with a polynomial model developed in our prior work. The proposed approach allows for an analytical integration of temperature effects such that an explicitly temperature-perspective SOH estimator is established, whose performance and complexity is contrasted to the support vector machine (SVM) scheme. The forecast of remaining useful life is also performed via a combination of SBPM and bootstrap sampling concepts. Large amounts of experimental data from multiple lithium-ion battery cells at three different temperatures are deployed for model construction, verification, and comparison. Such a multi-cell setting is more useful and valuable than only considering a single cell (a common scenario). This is the first known application of combined sample entropy and SBPM to battery health prognosis.
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8.
  • Hu, Xiaosong, et al. (författare)
  • Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order calculus
  • 2018
  • Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 67:11, s. 10319-10329
  • Tidskriftsartikel (refereegranskat)abstract
    • Lithium-ion batteries have emerged as the state-of-The-Art energy storage for portable electronics, electrified vehicles, and smart grids. An enabling Battery Management System holds the key for efficient and reliable system operation, in which State-of-Charge (SOC) estimation and State-of-Health (SOH) monitoring are of particular importance. In this paper, an SOC and SOH co-estimation scheme is proposed based on the fractional-order calculus. First, a fractional-order equivalent circuit model is established and parameterized using a Hybrid Genetic Algorithm/Particle Swarm Optimization method. This model is capable of predicting the voltage response with a root-mean-squared error less than 10 mV under various driving-cycle-based tests. Comparative studies show that it improves the modeling accuracy appreciably from its second-and third-order counterparts. Then, a dual fractional-order extended Kalman filter is put forward to realize simultaneous SOC and SOH estimation. Extensive experimental results show that the maximum steady-state errors of SOC and SOH estimation can be achieved within 1%, in the presence of initial deviation, noise, and disturbance. The resilience of the co-estimation scheme against battery aging is also verified through experimentation.
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9.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Comparison of Three Electrochemical Energy Buffers Applied to a Hybrid Bus Powertrain with Simultaneous Optimal Sizing and Energy Management
  • 2014
  • Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 15:3, s. 1193-1205
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper comparatively examines three different electrochemical energy storage systems (ESSs), i.e., Li-ion battery, supercapacitor, and dual buffer, for a hybrid bus powertrain operated in Gothenburg, Sweden. Existing studies focus on comparing these ESSs in terms of either general attributes (e.g., energy density and power density) or their implications to the fuel economy of hybrid vehicle with a heuristic/non-optimal ESS size and power management strategy. This paper adds four original contributions to the related literature. First, the three ESSs are compared in a framework of simultaneous optimal ESS sizing and energy management, where the ESSs can serve the powertrain in a most cost-effective manner. Second, convex optimization is used to implement the framework, which allows the hybrid powertrain designers/integrators to rapidly and optimally perform integrated ESS selection, sizing, and power management. Third, both hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV) scenarios for the powertrain are considered, in order to systematically examine how different the ESS requirements are for HEV and PHEV applications. Finally, a sensitivity analysis is carried out to evaluate how price variations of the on-board energy carriers affect the results and conclusions.
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10.
  • Hu, Xiaosong, et al. (författare)
  • Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model
  • 2018
  • Ingår i: IEEE/ASME Transactions on Mechatronics. - 1083-4435 .- 1941-014X. ; 23:1, s. 167-178
  • Tidskriftsartikel (refereegranskat)abstract
    • Efficient battery condition monitoring is of particular importance in large-scale, high-performance, and safety-critical mechatronic systems, e.g., electrified vehicles and smart grid. This paper pursues a detailed assessment of optimization-driven moving horizon estimation (MHE) framework by means of a reduced electrochemical model. For state-of-charge estimation, the standard MHE and two variants in the framework are examined by a comprehensive consideration of accuracy, computational intensity, effect of horizon size, and fault tolerance. A comparison with common extended Kalman filtering and unscented Kalman filtering is also carried out. Then, the feasibility and performance are demonstrated for accessing internal battery states unavailable in equivalent circuit models, such as solid-phase surface concentration and electrolyte concentration. Ultimately, a multiscale MHE-type scheme is created for State-of-Health estimation. This study is the first known systematic investigation of MHE-type estimators applied to battery management.
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11.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes
  • 2013
  • Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 111, s. 1001-1009
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper is concerned with the tank-to-wheel (TTW) analysis of a series plug-in hybrid electric bus operated in Gothenburg, Sweden. The bus line and the powertrain model are described. The definition and the calculation method of the recuperation and fuel-to-traction efficiencies are delineated for evaluating the TTW energy conversion. The two efficiencies are quantified and compared for two optimization-based energy management strategies, in which convex modeling and optimization are used. The impact of downsizing the battery on the two efficiencies is also investigated.
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12.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Enhanced Sample Entropy-based Health Management of Li-ion Battery for Electrified Vehicles
  • 2014
  • Ingår i: Energy. - : Elsevier BV. - 0360-5442 .- 1873-6785. ; 64, s. 953-960
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper discusses an ameliorated sample entropy-based capacity estimator for PHM (prognostics and health management) of Li-ion batteries in electrified vehicles. The aging datasets of eight cells with identical chemistry were used. The sample entropy of cell voltage sequence under the well-known HPPC (hybrid pulse power characterization) profile is adopted as the input of the health estimator. The calculated sample entropy and capacity of a reference Li-ion cell (randomly selected from the eight cells) at three different ambient temperatures are employed as the training data to establish the model by using the least-squares optimization. The performance and robustness of the estimator are validated by means of the degradation datasets from the other seven cells. The associated results indicate that the proposed health management strategy has an average relative error of about 2%.
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13.
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14.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Integrated optimization of battery sizing, charging, and power management in plug-in hybrid electric vehicles
  • 2016
  • Ingår i: IEEE Transactions on Control Systems Technology. - 1063-6536 .- 1558-0865. ; 24:3, s. 1036-1043
  • Tidskriftsartikel (refereegranskat)abstract
    • This brief presents an integrated optimization framework for battery sizing, charging, and on-road power management in plug-in hybrid electric vehicles. This framework utilizes convex programming to assess interactions between the three optimal design/control tasks. The objective is to minimize carbon dioxide (CO2) emissions, from the on-board internal combustion engine and grid generation plants providing electrical recharge power. The impacts of varying daily grid CO2 trajectories on both the optimal battery size and charging/power management algorithms are analyzed. We find that the level of grid CO2 emissions can significantly impact the nature of emission-optimal on-road power management. We also observe that the on-road power management strategy is the most important design task for minimizing emissions, through a variety of comparative studies.
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15.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus
  • 2015
  • Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 137, s. 913-
  • Tidskriftsartikel (refereegranskat)abstract
    • Energy storage systems (ESSs) play an important role in the performance and economy of electrified vehicles. Hybrid energy storage system (HESS) combining both lithium-ion cells and supercapacitors is one of the most promising solutions. This paper discusses the optimal HESS dimensioning and energy management of a fuel cell hybrid electric bus. Three novel contributions are added to the relevant literature. First, efficient convex programming is used to simultaneously optimize the HESS dimension (including sizes of both the lithium-ion battery pack and the supercapacitor stack) and the power allocation between the HESS and the fuel cell system (FCS) of the hybrid bus. In the combined plant/controller optimization problem, a dynamic battery State-of-Health (SOH) model is integrated to quantitatively examine the impact of the battery replacement strategy on both the HESS size and the bus economy. Second, the HESS and the battery-only ESS options are systematically compared in the proposed optimization framework. Finally, the battery-health-perceptive HESS optimization outcome is contrasted to the ideal one neglecting the battery degradation (assuming that the battery is durable over the bus service period without deliberate power regulation).
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16.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Model-based Dynamic Power Assessment of Lithium-Ion Batteries Considering Different Operating Conditions
  • 2014
  • Ingår i: IEEE Transactions on Industrial Informatics. - 1941-0050 .- 1551-3203. ; 10:3, s. 1948-1959
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper is concerned with model-based dynamic peak-power evaluation for LiNMC and LiFePO4 batteries under different operating conditions. The battery test and our prior study on linear-parameter-varying (LPV) battery modeling are briefly introduced. The peak-power estimation method that incorporates an explicit prediction horizon and design constraints on the battery current, voltage, and SOC are elaborated and its computational load is analyzed. The discharge and charge peak powers are quantitatively assessed under different dynamic characterization tests, in which a comparison with the conventional PNGV-HPPC method and approaches using the less accurate models is conducted. The robustness of the peak-power estimation approach against varying battery temperatures and aging levels is investigated. The methods to improve the credibility of the peak-power assessment in the context of battery degradation are explored.
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17.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Multi-objective Optimal Sizing and Control of Fuel Cell Systems for Hybrid Vehicle Applications
  • 2015
  • Ingår i: European Control Conference. ; , s. 2559 - 2564
  • Konferensbidrag (refereegranskat)abstract
    • This paper discusses the multi-objective optimal sizing and energy management of proton-exchange-membrane fuel cell systems (PEMFCS) in hybrid electric vehicles. First, a convex multi-objective optimization framework is established for quickly and efficiently examining the optimal PEMFCS size and tradeoffs between the PEMFCS durability and hydrogen economy for a fuel cell hybrid bus. Second, the implication of driving patterns to both the optimal PEMFCS size and Pareto optimality is investigated. The proposed framework, along with the optimization results, is useful to health-aware PEMFCS integration, simulation, and control at a vehicle system level.
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18.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Optimal Dimensioning and Power Management of a Fuel Cell/Battery Hybrid Bus via Convex Programming
  • 2015
  • Ingår i: IEEE/ASME Transactions on Mechatronics. - 1083-4435 .- 1941-014X. ; 20:1, s. 457-468
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper is concerned with the simultaneous optimal component sizing and power management of a fuel cell/battery hybrid bus. Existing studies solve the combined plant/controller optimization problem for fuel cell hybrid vehicles (FCHVs) by using methods with disadvantages of heavy computational burden and/or suboptimality, for which only a single driving profile was often considered. This paper adds three important contributions to the FCHVs-related literature. First, convex programming is extended to rapidly and efficiently optimize both the power management strategy and sizes of the fuel cell system (FCS) and the battery pack in the hybrid bus. The main purpose is to encourage more researchers and engineers in FCHVs field to utilize the new effective tool. Second, the influence of the driving pattern on the optimization result (both the component sizes and hydrogen economy) of the bus is systematically investigated by considering three different bus driving routes, including two standard testing cycles and a realistic bus line cycle with slope information in Gothenburg, Sweden. Finally, the sensitivity of the optimization outcome to the potential price decreases of the FCS and the battery is quantitatively examined.
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19.
  • Hu, Xiaosong, et al. (författare)
  • Technological developments in batteries: A survey of principal roles, types, and management needs
  • 2017
  • Ingår i: IEEE Power and Energy Magazine. - 1540-7977. ; 15:5, s. 20-31
  • Forskningsöversikt (refereegranskat)abstract
    • Battery energy storage effectively staBIlizes the electric grid and AIDS renewable integration by balancing supply and demand in real time. The importance of such storage is especially crucial in densely populated urban areas, where traditional storage techniques such as pumped hydroelectric energy storage and compressed-air energy storage are often not feasible.
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20.
  • Ju, Fei, et al. (författare)
  • Predictive energy management with engine switching control for hybrid electric vehicle via ADMM
  • 2023
  • Ingår i: Energy. - : Elsevier BV. - 0360-5442 .- 1873-6785. ; 263
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper studies energy management (EM) of a power-split hybrid electric vehicle (HEV) equipped with planetary gear sets. We first formulate a mixed-integer global optimal control problem that includes a binary switching variable. Convex modeling, including the fuel model for a compound energy conversion unit, is then presented to reformulate the mixed-integer EM as a two-step program. For optimizing the engine switching and battery power decisions in the first step, we employ the alternating direction method of multipliers (ADMM) algorithm where the solution of the convex relaxation is used to initialize the non-convex problem. On the standard driving cycle, simulation results indicate that the ADMM based EM method saves 7.63% fuel compared to a heuristic method, and shows 99% optimality compared to a dynamic programming method, while saving three orders of magnitude in computing time. An ADMM combined model predictive control (ADMM-MPC) method is also developed that is suitable for receding horizon control scenarios. The ADMM-MPC method shows 5.28% fuel saving when implemented using a prediction horizon of 15 samples. Meanwhile, the mean computing time for MPC updates is 3.53 ms. Our results demonstrate that the proposed ADMM is capable of real-time control.
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21.
  • Lei, Z., et al. (författare)
  • Residual capacity estimation for ultracapacitors in electric vehicles using artificial neural network
  • 2014
  • Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). - : Elsevier BV. - 1474-6670. - 9783902823625 ; 19, s. 3899-3904
  • Konferensbidrag (refereegranskat)abstract
    • The energy storage system (ESS) plays a significant role in fulfilling the driving performance requirements and ensuring operational safety in an electric vehicle. Ultracapacitors (UCs) are being actively studied and used in parallel with batteries or fuel cells forming hybrid energy storage systems in electric vehicles. They show excellent potential in terms of the sourcing and sinking of power, particularly for the peak-power demand encountered in aggressive regenerative braking. Since there are an increasing number of ultracapacitor applications, which now includes commercial automotive applications, establishing a good model to represent their dynamics, especially the residual capacity estimation (RCE), is vital; but this is challenging. This paper presents a residual capacity estimation model which is based on an artificial neural network (ANN). This takes both charging and discharging current and temperature into consideration. The proposed ANN model comprises of three inputs and one output: the inputs are temperature, current and voltage, and the output is the residual charge. The model is trained and validated by feeding a test database which is extracted from experimental testing of ultracapacitors at different currents and temperatures on a well-established test rig. The training data should span the whole prediction scope, therefore the test currents and temperatures both vary over a wide range and cover all the possible operational conditions of the on-board ultracapacitors. The Back-Propagation (BP) algorithm, together with an early stopping strategy, is adopted to train the proposed ANN model to assure adequately accurate prediction while avoiding overfitting risks. The model performance is validated with experimental results over a set of test data randomly selected.
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22.
  • Li, Shengbo, et al. (författare)
  • Mechanism of vehicular periodic operation for optimal fuel economy in free-driving scenarios
  • 2015
  • Ingår i: IET Intelligent Transport Systems. - : Institution of Engineering and Technology (IET). - 1751-9578 .- 1751-956X. ; 9:3, s. 306-313
  • Tidskriftsartikel (refereegranskat)abstract
    • In addition to the fuel-efficient design of vehicles, eco-driving technologies can further reduce the fuel consumption and carbon emissions of road transportation. One of the major issues in eco-driving technologies is how to determine the fuel savings or fuel-optimised operating strategies of the power train. We examine the periodic operation of autonomous vehicles in free-driving scenarios with the purpose of maximising fuel economy. The design of such strategies can be considered to be an optimal control problem, which is proved by -test to have singular arcs because of the S-shaped engine fueling rate. The optimal operations are solved numerically by using the Legendre pseudospectral method, and many are found to demonstrate periodic behaviours, that is, pulse and gliding. In periodic operation, the engine switches between the minimum break specific fuel consumption point and the idling point, while the vehicle speed oscillates between its upper and lower bounds. The formation of periodic operation, as well as some key properties, is analysed and presented.
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23.
  • Murgovski, Nikolce, 1980, et al. (författare)
  • Combined design and control optimization of hybrid vehicles
  • 2015
  • Ingår i: Handbook of Clean Energy Systems. - 9781118991978 ; , s. 1-14
  • Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
    • Hybrid vehicles play an important role in reducing energy consumption and pollutant emissions of ground transportation. The increased mechatronic system complexity, however, results in a heavy challenge for efficient component sizing and power coordination among multiple power sources. This chapter presents a convex programming framework for the combined design and control optimization of hybrid vehicles. An instructive and straightforward case study of design and energy control optimization for a fuel cell/supercapacitor hybrid bus is delineated to demonstrate the effectiveness and the computational advantage of the convex programming methodology. Convex modeling of key components in the fuel cell/supercapactior hybrid powertrain is introduced, while a pseudo code in CVX is also provided to elucidate how to practically implement the convex optimization. The generalization, applicability, and validity of the convex optimization framework are also discussed for various powertrain configurations (i.e., series, parallel, and series-parallel), different energy storage systems (e.g., battery, supercapacitor, and dual buffer), and advanced vehicular design and controller synthesis accounting for the battery thermal and aging conditions. The proposed methodology is an efficient tool that is valuable for researchers and engineers in the area of hybrid vehicles to address realistic optimal control problems.
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24.
  • Murgovski, Nikolce, 1980, et al. (författare)
  • Computationally efficient energy management of a planetary gear hybrid electric vehicle
  • 2014
  • Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). - : Elsevier BV. - 1474-6670. - 9783902823625 ; 19
  • Konferensbidrag (refereegranskat)abstract
    • We present a method for obtaining a computationally efficient, sub-optimal energy management of an electrified vehicle containing a planetary gear set. We first reformulate the optimization problem to become separable in space (optimization variables). The problem is then decomposed into two optimization problems. The first is a static problem that looks for the optimal engine speed that maximizes efficiency of a compound unit, resembling an engine-generator unit combining the planetary gear and kinetic energy converters connected to it. The second is a dynamic optimization problem deciding the optimal power split between an electric buffer and the compound unit. By approximating the losses of the compound unit as convex, second order polynomial in generated power, we are able to solve the power split problem in less than 2 seconds, when the engine on/off sequence is known in advance. By comparing results with dynamic programming, we observed an approximation error of less than 0.2%.
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25.
  • Murgovski, Nikolce, 1980, et al. (författare)
  • Convex relaxations in the optimal control of electrified vehicles
  • 2015
  • Ingår i: American Control Conference. - 0743-1619. - 9781479986842 ; 2015-July, s. 2292-2298
  • Konferensbidrag (refereegranskat)abstract
    • When controlling the energy flow in electrified powertrains by means of convex optimization, the typically non-convex set of the original formulation needs to be relaxed to a convex super-set. In this paper we show that when using the backward simulation approach, where vehicle velocity is equal to the reference velocity, the global optimum of the original non-convex problem can be obtained by solving the relaxed convex problem. When vehicle velocity is kept as a state in the problem, in the so called forward simulation approach, we provide a condition for which, when satisfied, an agreement will be achieved between the solutions of the relaxed and the original problem.
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26.
  • Murgovski, Nikolce, 1980, et al. (författare)
  • Filtering driving cycles for assessment of electrified vehicles
  • 2013
  • Ingår i: Workshop for new energy vehicle dynamic system and control technology.
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • We present a method for pre-filtering driving cycles that are to be used for assessment of electrified vehicles. The method ensures that the vehicle may exactly follow the filtered velocity demanded by the driving cycle. Employing convex optimization, the method also allows optimal velocity shaping that minimizes the amount of wasted energy. We illustrate the method by an example of performance assessment of a hybrid electric bus in a series powertrain topology.
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27.
  • Shangguan, Yidan, et al. (författare)
  • On the Fundamental Diagram for Freeway Traffic: Exploring the Lower Bound of the Fitting Error and Correcting the Generalized Linear Regression Models
  • 2023
  • Ingår i: MATHEMATICS. - 2227-7390. ; 11:16
  • Tidskriftsartikel (refereegranskat)abstract
    • In traffic flow, the relationship between speed and density exhibits decreasing monotonicity and continuity, which is characterized by various models such as the Greenshields and Greenberg models. However, some existing models, i.e., the Underwood and Northwestern models, introduce bias by incorrectly utilizing linear regression for parameter calibration. Furthermore, the lower bound of the fitting errors for all these models remains unknown. To address above issues, this study first proves the bias associated with using linear regression in handling the Underwood and Northwestern models and corrects it, resulting in a significantly lower mean squared error (MSE). Second, a quadratic programming model is developed to obtain the lower bound of the MSE for these existing models. The relative gaps between the MSEs of existing models and the lower bound indicate that the existing models still have a lot of potential for improvement.
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28.
  • Sun, C., et al. (författare)
  • Comparison of velocity forecasting strategies for predictive control in HEVS
  • 2014
  • Ingår i: ASME 2014 Dynamic Systems and Control Conference, DSCC 2014; San Antonio; United States; 22 October 2014 through 24 October 2014. - 9780791846193 ; 2, s. Art. no. 6031-
  • Konferensbidrag (refereegranskat)abstract
    • The performance of model predictive control (MPC) for energy management in hybrid electric vehicles (HEVS) is strongly dependent on the projected future driving profile. This paper proposes a novel velocity forecasting method based on artificial neural networks (ANN). The objective is to improve the fuel economy of a power-split HEV in a nonlinear MPC framework. In this study, no telemetry or on-board sensor information is required. A comparative study is conducted between the ANNbased method and two other velocity predictors: generalized exponentially varying and Markov-chain models. The sensitivity of the prediction precision and computational cost on tuning parameters in examined for each forecasting strategy. Validation results show that the ANN-based velocity predictor exhibits the best overall performance with respect to minimizing fuel consumption.
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29.
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30.
  • Wei, Shaoyuan, et al. (författare)
  • Optimisation of a Catenary-Free Tramline Equipped with Stationary Energy Storage Systems
  • 2020
  • Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 69:3, s. 2449-2462
  • Tidskriftsartikel (refereegranskat)abstract
    • Catenary-free trams powered by on-board supercapacitor systems require high charging power from tram stations along the line. Since a shared electric grid is suffering from power superimposition when several trams charge at the same time, we propose to install stationary energy storage systems (SESSs) for power supply network to downsize charging equipment and reduce operational cost of the electric grid. To evaluate the trade-off between component cost and operational cost, an optimisation problem, which integrates type selection, sizing, energy management and different installation configurations of the SESSs, is introduced and formulated in terms of an annual cost. Disciplined convex modelling is applied to obtain a computationally tractable solution for a case study on an existing line in China. Results show that the optimal solution may reduce tramline cost by 11.48%.
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31.
  • Wei, Shaoyuan, et al. (författare)
  • Stochastic optimization of a stationary energy storage system for a catenary-free tramline
  • 2020
  • Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 280
  • Tidskriftsartikel (refereegranskat)abstract
    • To realize economical operation of a catenary-free tramline, we propose installing a stationary energy storage system (SESS) to assist the electric grid for trams charging. As the tram operation may not be fully aligned with a predetermined timetable, an economical coordination of the electric grid and the SESS under uncertain charging demands is investigated. To this end, a chance-constrained program is formulated where demanded charging of the tramline is satisfied in a probabilistic sense. The introduced chance-constrained program is translated into a robust and deterministic mixed-integer second-order cone program (MISOCP) by first saturating charging power to a stochastic upper limit and then prolonging charging periods until entire energy is delivered for all charging scenarios that are being investigated. A case study for the Haizhu line in Guangzhou, China, shows that a cost–benefit of 13.70% can be obtained by installing an SESS when charging power is fully delivered for all scenarios, while a 28.47% cost-saving can be achieved when charging power is delivered 99% of the time.
  •  
32.
  • Xie, Yi, et al. (författare)
  • Novel Mesoscale Electrothermal Modeling for Lithium-Ion Batteries
  • 2020
  • Ingår i: IEEE Transactions on Power Electronics. - 0885-8993 .- 1941-0107. ; 35:3, s. 2595-2614
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper devises an innovative mesoscale electrothermal model for Li-ion batteries. This model manipulates the mesoscale calculation grid in finite element analysis as independent small cell sandwiches and establishes a lumped equivalent circuit model for each cell sandwich. Then, such electrical models are arranged in parallel to form a multilayer equivalent circuit to simulate electrical characteristics of a whole battery, through capturing the current and terminal voltage of each constituent cell sandwich. This modeling idea overcomes the entrenched disadvantage of heat generation models with lumped parameters, i.e., the unavailability of heat generation distribution inside a battery. Besides the current and terminal voltage, the temperature and state of charge dependent open-circuit voltage and entropy coefficient are incorporated into a Newman's heat generation model to estimate the heat generated in the calculation grid. The battery temperature distribution is eventually derived by solving the heat conduction equation with thermal conductivity as a function of the battery temperature. We leverage the developed electrothermal model to track the temperature evolution of an 18650 Li-ion battery at different ambient temperatures and discharge rates, for the first time. Experimental results demonstrate that the electrothermal model can precisely emulate the battery thermal dynamics with an average error of 0.72 °C. Moreover, a comparative study shows that the proposed model outperforms common resistance-based thermal models that do not consider the heat generation distribution and the interdependence between the battery temperature and thermal conductivity.
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33.
  • Xu, Fuguo, et al. (författare)
  • Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution
  • 2022
  • Ingår i: CONTROL THEORY AND TECHNOLOGY. - : SPRINGERNATURE. - 2095-6983 .- 2198-0942. ; 20, s. 145-160
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.
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34.
  • Yang, Y. L., et al. (författare)
  • Arrhenius Equation-Based Cell-Health Assessment: Application to Thermal Energy Management Design of a HEV NiMH Battery Pack
  • 2013
  • Ingår i: Energies. - : MDPI AG. - 1996-1073 .- 1996-1073. ; 6:5, s. 2709-2725
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a model-based cell-health-conscious thermal energy management method. An Arrhenius equation-based mathematical model is firstly identified to quantify the effect of temperature on the cell lifetime of a Nickel Metal Hydride (NiMH) battery pack. The cell aging datasets collected under multiple ambient temperatures are applied to extract the Arrhenius equation parameters. The model is then used as an assessment criterion and guidance for the thermal management design of battery packs. The feasibility and applicability of a pack structure with its cooling system, is then evaluated, and its design problems are studied by a computational fluid dynamics (CFD) analysis. The performance and eligibility of the design method is validated by both CFD simulations and experiments.
  •  
35.
  • Ye, X., et al. (författare)
  • Design and implementation of a real-time power management strategy for a parallel hybrid electric bus
  • 2014
  • Ingår i: Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. - 2041-2991 .- 0954-4070.
  • Tidskriftsartikel (refereegranskat)abstract
    • A real-time energy management strategy derived from an equivalent consumption minimization strategy for a parallel hybrid electric bus is introduced. Although an equivalent consumption minimization strategy is a near-optimal control strategy for the power management problems of hybrid electric vehicles, the computation cost and the driveability requirements are still barriers for it to be directly implemented on real-time controllers. This paper analyses the controller characteristics of the equivalent consumption minimization strategy based on the Willans line model for an internal-combustion engine and an electric motor. A two-step method is proposed to simplify and approximate the standard equivalent consumption minimization strategy controller. The strategy for the proposed controller, which is calledthe Willans line–equivalent consumption minimization strategy, can reduce the computation cost of optimization and can guarantee near-optimum fuel economy, while improving the vehicle driveability. The proposed controller is then validated by offline simulation and an onboard bench test. A backward simulation is conducted to test the fuel economy performance of the controller simplified from the two steps, together with dynamic programming and an equivalent consumption minimization strategy based on look-up tables. Then the controller is tuned by a high-fidelity forward simulationto explore the trade-off between the fuel economy and the driveability. Finally, a bench test with real powertrain components is presented to illustrate the effectiveness of the proposed methodology.
  •  
36.
  • Ye, Xiao, et al. (författare)
  • Modeling and control strategy development of a parallel hybrid electric bus
  • 2013
  • Ingår i: International Journal of Automotive Technology. - : Springer Science and Business Media LLC. - 1976-3832 .- 1229-9138. ; 14:6, s. 971-985
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents the system modeling, control strategy design, and experiment validation of a parallel hybridelectric bus with an automatic manual transmission (AMT) and a dry clutch. The mathematical model representation and thesystem architecture of the powertrain are first described. Next, a complete control scheme including energy managementstrategy and coordinated control of the AMT and the clutch is presented. The controller and powertrain models are thenintegrated in a way that the power management and the hybrid driveline perform in real world. The analysis and validationthrough model simulation and comparison with experiment data are conducted. A good agreement between the model andexperiment demonstrates the efficacy and credibility of the integrated model. The integrated model is employed in bothsimulation and bench-test assessments for the development of a hybrid control unit. The results indicate that the model-baseddesign methodology is beneficial to systematically analyzing and understanding the dynamics of hybrid electric powertrain.
  •  
37.
  • Zou, Changfu, 1987, et al. (författare)
  • A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors
  • 2018
  • Ingår i: Journal of Power Sources. - : Elsevier BV. - 0378-7753. ; 390, s. 286-296
  • Forskningsöversikt (refereegranskat)abstract
    • Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15–30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced b attery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted.
  •  
38.
  • Zou, Changfu, 1987, et al. (författare)
  • Electrochemical estimation and control for lithium-ion battery health-aware fast charging
  • 2018
  • Ingår i: IEEE Transactions on Industrial Electronics. - 0278-0046 .- 1557-9948. ; 65:8, s. 6635-6645
  • Tidskriftsartikel (refereegranskat)abstract
    • Fast charging strategies have gained an increasing interest toward the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifetime, and charging time, this paper proposes a health-aware fast charging strategy synthesized from electrochemical system modeling and advanced control theory. The battery charging problem is formulated in a linear time-varying model predictive control algorithm. In this algorithm, a control-oriented electrochemical-thermal model is developed to predict the system dynamics. Constraints are explicitly imposed on physically meaningful state variables to protect the battery from hazardous operations. A moving horizon estimation algorithm is employed to monitor battery internal state information. Illustrative results demonstrate that the proposed charging strategy is able to largely reduce the charging time from its benchmarks while ensuring the satisfaction of health-related constraints.
  •  
39.
  • Zou, Changfu, 1987, et al. (författare)
  • Electrothermal dynamics-conscious lithium-ion battery cell-level charging management via state-monitored predictive control
  • 2017
  • Ingår i: Energy. - : Elsevier BV. - 0360-5442 .- 1873-6785. ; 141, s. 250-259
  • Tidskriftsartikel (refereegranskat)abstract
    • Lithium-ion battery charging management has become an enabling technology towards a paradigm shift of electrified mobility. Fast charging is desired for convenience improvements but may excessively degrade battery's health or even cause safety issues. This paper proposes a novel algorithm to manage battery charging operations using a model-based control approach. Based on a fully coupled electrothermal model, the fast charging strategy is formulated as a linear-time-varying model predictive control problem, for the first time. Constraints are explicitly imposed to protect the battery from overcharging and overheating. To enable the state-feedback control, unmeasurable battery internal states including state-of-charge and core temperature are estimated via a nonlinear observer using noisy measurements of current, voltage, and surface temperature. Illustrative results demonstrate that the proposed approach is able to optimally balance time and temperature increase. In addition, it is shown from simulations that the model predictive control based charging algorithm appears promising for real-time implementation.
  •  
40.
  • Zou, Changfu, 1987, et al. (författare)
  • Nonlinear fractional-order model based battery state estimation with guaranteed robustness and stability
  • 2018
  • Ingår i: IEEE Transactions on Industrial Electronics. - 0278-0046 .- 1557-9948. ; 65:7, s. 5951-5961
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper proposes a new estimator design algorithm for state-of-charge (SoC) indication of lithium-ion batteries. A fractional-order model-based nonlinear estimator is first framed including a Luenberger term and a sliding mode term. The estimator gains are designed by Lyapunov’s direct method, providing a guarantee for stability and robustness of the error system under certain assumptions. This generic estimation algorithm is then applied to lithium-ion batteries. A fractional-order circuit model is adopted to predict battery dynamic behaviours. Assumptions based on which the estimation algorithm is developed are justified and remarked. Experiments corresponding to electric vehicle applications are conducted to parameterise the battery model and demonstrate the estimation performance. It shows that the proposed approach is able to estimate SoC with the errors less than 0.03 in the presence of initial deviation and persistent noise. Furthermore, the benefits of using the proposed estimator relative to other estimators are calculated over different cycles and conditions.
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