| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
| 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.
|
|
