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Accurate and reliable prediction of the battery capacity degradation is vital for predictive health management. This paper proposes a novel framework to improve the accuracy and reliability of battery health prognostic. Firstly, sequential information-ensembled health indicators, which have high correlations with battery capacity and lifetime, are proposed based on partial voltage and capacity sequences. Then, the Gaussian mixture model is adopted for lifetime clustering to verify the effectiveness of the proposed health indicators and an automatic reference batteries selection method is proposed to find out the most relative candidates for degradation base model training. A long short-term memory network with probabilistic regression is leveraged for battery health prognostic, which provides the predicted mean value and confidence interval via Bayesian inference. Finally, the model migration is presented to further improve the accuracy and reliability, with only a few checkpoints used for re-training. The proposed framework for battery health prognostic is validated against four battery datasets, showing high accuracy and reliability. Specifically, the root mean square error and mean absolute error of health prognostic on all the battery cells in four battery dataset can be within 2% and 1.5%, respectively. The mean relative reductions of the above two errors reach 43.7% and 45.3% respectively compared to the conventional method.

Reusing retired electric vehicle batteries (REVBs) in renewable energy systems is a relatively new concept, and the presented PV-hydrogen-REVB hybrid energy system is a promising way to exploit REVBs’ residual capacities. This paper focuses on the design and sizing optimization of the entire system and delivers three main contributions. First, this paper proposes a REVB model based on the model of capacity fading of lithium battery cells, which could allow a more realistic result for the design. Second, a power management strategy is presented to regulate the energy flow, for protecting the REVB and other system components. Third, multiple objectives are considered in the optimization model, including minimizing loss of power supply, system cost, and a new indicator, namely, potential energy waste. Then, using the simulation results of a five-year working period to calculate the objective functions, a multi-objective evolutionary algorithm NSGA-II is applied to generate the Pareto set of a case for residential usage. In further discussions, the influences of ignoring REVB’s capacity fading and removing the objective of potential energy waste possibility are presented, as well as the comparison of performances between NSGA-II and MOEA/D. The results reveal that the reliability of the system is impaired if ignoring the REVB’s capacity loss, and the proposed indicator is crucial for the design. NSGA-II has a better performance regarding the distribution of solutions and gives better results in this study.

Fuel economy of hybrid vehicles is affected by their powertrain configurations, powertrain parameters, and energy management strategies. It is most beneficial to optimizing all the three factors simultaneously. However, when the design search space is large, an exhaustive, optimal control strategy, such as dynamic programming (DP), is too computationally expensive. Hence, a faster optimization method with higher computational efficiency and acceptable accuracy is required. Based on the DP approach, an approximate optimization method, called rapid dynamic programming (Rapid-DP), is developed and discussed in this paper. This method effectively reduces the decision-making time (the time can be reduced by a factor of 700, compared to the DP approach) for optimal control. The optimization processes and results are described and then compared with the original DP and PEARS + methods under two different driving cycles: FTP72 and HWFET. In conjunction with particle swarm optimization (PSO), the rapid-DP is leveraged, for the first time, to optimize key powertrain parameters for power split hybrid electric vehicles. Based on two power-split hybrids: Toyota Prius and Prius++, the joint optimization approach is exploited to examine vehicular fuel savings attributed to synergistic parameters optimization and operating-mode increase. The multi-mode configuration with optimal component parameters is demonstrated to be most fuel-efficient, with 6.56% and 3.15% fuel reductions under FTP72 and HWFET cycles, respectively, with respect to the original Prius 2010.

Inconsistencies between the cells in a battery pack can greatly limit the pack's cycle life and performance. This is why an integrated equalization management system (EMS) is necessary to limit these inconsistencies. This paper presents a novel multivariable fusion equalization strategy utilizing a fuzzy logic controller (FLC). This strategy begins by firstly analyzing the voltage inconsistencies within the battery pack and by proposing different equalization objectives. Secondly, a simplified model is created, which consists of a battery model and an equalization circuit model, which is then used as a predictive model. Thirdly, a top-bottom method and a predictive control method are proposed to the EMS based on the battery voltage and its state of charge (SOC). Lastly, the FLC is used to fuse these two strategies depending on the ratio of the current over the capacity (RCC). A battery pack setup is built to acquire experimental data, and a series of tests are used to verify the proposed method. The proposed multivariable fusion equalization achieves a 14.9% increase in computational efficiency, and the battery performance increases by 4.9% over a traditional equalization strategy.

Energy management strategies (EMSs) are the most significant components in hybrid electric vehicles (HEVs) because they decide the potential of energy conservation and emission reduction. This work presents a transferred EMS for a parallel HEV via combining the reinforcement learning method and driving conditions recognition. First, the Markov decision process (MDP) and the transition probability matrix are utilized to differentiate the driving conditions. Then, reinforcement learning algorithms are formulated to achieve power split controls, in which Q-tables are tuned by current driving situations. Finally, the proposed transferred framework is estimated and validated in a parallel hybrid topology. Its advantages in computational efficiency and fuel economy are summarized and proved., Comment: 6 pages, 5 figures

By introducing the concept of “data coupling” into the existing hardware-in-the-loop (HIL) for hybrid electric vehicles (HEVs), several distributed subsystems are now able to be coupled together to constitute an Internet-Distributed Vehicle-in-the-Loop (ID-VIL) simulation platform. These subsystems include: the cloud server (Cloud) which acts as the central control node, the Electric Vehicle (EV) which serves as the Vehicle-in-the-Loop (VIL), and the HIL simulation platforms for engine (EIL) and battery (BIL). The proposed ID-VIL configuration makes it possible that the EV which does not contain any hybrid powertrain can simulate the output of an HEV's power system, meanwhile the subsystems of HEV (such as engine and battery) that are actually deployed on HIL platforms can work just the same as on board. The feasibility and reliability of ID-VIL system are verified by a 10 s acceleration test, a 7 km urban road tests, and the transparency analysis of the test result.

In order to significantly reduce vehicular fuel consumption and emission pollutants, power-split hybrid electric vehicles are increasingly deployed to ensure that internal combustion engines work at their high-efficiency regions. Because of multiple power components (internal combustion engine, two electric machines, and a driveshaft to the wheels), configurations of such vehicular powertrains are typically very complicated. In order to systematically analyze and design a fuel-optimal powertrain, an innovative hierarchical topological graph approach is proposed. This method comprises four major design processes: (1) modeling of hybrid vehicle powertrain systems, (2) generation of a configuration pool, (3) identification of isomorphism, and (4) classification of configuration modes. Potential power-split hybrid vehicle designs are rigorously examined via a dynamic programming algorithm to estimate their acceleration performance (0–100 km/h) and fuel economy in various driving cycles.

The K2Ti6O13 nanobeams (K-TNBs) with tunnel structure have been prepared by simple hydrothermal method. To attain the flexible property and the high cycle stability, the carbon absorbed on K-TNBs (C@K-TNBs) supercapacitor (SC) has been encapsulated by the flexible PDMS (polydimethylsiloxane). The solid SC device shows excellent rate capability, high cycle stability and high flexibility at any degrees. The capacitance of the device can attain 301 F g−1 at current of 3 mA, energy density of 26.8 Wh kg−1 at power density 400 W kg−1 and power density of 1.47 kW kg−1 at energy density 16.3 Wh kg−1. The retention of specific capacitance remains 82.7% after 10000 cycles at high current value of 21 mA. Three supercapacitors in series can light 100 green color commercial light emitting diodes (LEDs) for about 16 minutes. All these results indicate that the C@K-TNBs nanomaterial could provide the potential application in high performance supercapacitor.

Holistic energy management of plug-in hybrid electric vehicles (PHEVs) in smart grid environment constitutes an enormous control challenge. This paper responds to this challenge by investigating the interactions among three important control tasks, i.e., charging, on-road power management, and battery degradation mitigation, in PHEVs. Three notable original contributions distinguish our work from existing endeavors. First, a new convex programming (CP)-based cost-optimal control framework is constructed to minimize the daily operational expense of a PHEV, which seamlessly integrates costs of the three tasks. Second, a straightforward but useful sensitivity assessment of the optimization outcome is executed with respect to price changes of battery and energy carriers. The potential impact of vehicle-to-grid (V2G) power flow on the PHEV economy is eventually analyzed through a multitude of comparative studies.

It is imperative to explore the full carbon dioxide-saving potential for plug-in hybrid electric vehicles. This paper seeks to examine the role of renewable energy and powertrain optimization in minimizing daily carbon emissions of plug-in hybrid electric vehicles. A spectrum of influencing factors are investigated, including charging protocol, timing, on-road power management strategy, battery size, and carbon-emission intensity of the grid. A high-efficiency convex programming framework is harnessed to optimize a plug-in hybrid powertrain. Two originally important contributions evidently distinguish this work from existing efforts. First, diverse heuristic scenarios and concomitant weaknesses are elucidated, and the carbon reductions arising from renewable energy integration and the convex programming framework are quantified. The great importance of their synergy is accentuated, i.e., swiftly adapting charging/power management controls to wind intermittency. Second, battery health implication is explored for the optimal and heuristic scenarios, via a dynamic battery State-of-Health model.

To fulfill reliable battery management in electric vehicles (EVs), an advanced State-of-Charge (SOC) estimator is developed via machine learning methodology. A novel genetic algorithm-based fuzzy C-means (FCM) clustering technique is first used to partition the training data sampled in the driving cycle-based test of a lithium-ion battery. The clustering result is applied to learn the topology and antecedent parameters of the model. Recursive least-squares algorithm is then employed to extract its consequent parameters. To ensure good accuracy and resilience, the backpropagation learning algorithm is finally adopted to simultaneously optimize both the antecedent and consequent parts. Experimental results verify that the proposed estimator exhibits sufficient accuracy and outperforms those built by conventional fuzzy modeling methods.

Due to limited flexibility of engine operation, it is uneasy to improve the fuel economy of parallel hybrid electric vehicles (HEVs) with a single electric machine (SEM) from control perspective only. However, from design perspective, it is likely to devise multiple operating modes on a powertrain system to achieve better fuel economy. Therefore, in this paper, a velocity coupling HEV system with a SEM has been proposed, which has eleven modes, including an electric vehicle (EV) mode, three regenerative braking modes, an engine-start mode, an electrically variable transmission (EVT) mode, a parallel HEV mode, an engine-only mode, and three charging modes. Dynamic Programming (DP), as a global optimization algorithm, is leveraged to minimize the fuel consumption of this system. For comparison purposes, the P1 and P2 HEV systems are also examined by DP using the same vehicular parameters. Comparative results indicate that the P2 HEV saves about 6.68% fuel consumption over the P1 HEV, while more improvement can be observed from the proposed velocity coupling HEV, with 13.82% fuel consumption reduction over the P1 HEV.

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.

Cost effectiveness of plug-in hybrid electric vehicles (PHEVs) highly depends on their energy management strategies. This paper aims to maximize PHEVs economy through optimum coordination of three critical control missions, i.e., recharge from the grid, on-road supervisory power-flow control, and battery health management. A convex optimization framework is formulated to implement such coordination. The sensitivity of optimization result is then examined, with respect to price changes of energy carriers.

With the rapid development of the smartphone market, future cars seem to have more connections with intelligent cell phone and Internet. Intelligent transportation system (ITS) and telematics system have become research focus in recent years. There is an increasing demand for remote monitoring and diagnostic system as the further research of hybrid electric vehicle (HEV) goes on. In this paper, a remote controller area network bus (CAN-Bus) data monitor and diagnostic system for HEV is presented using on board diagnostic version-II (OBD-II) and Android-based smartphone. It is low-cost, convenient, and extensible with smartphone used in the system to realize communication with ELM327 and remote monitoring center wirelessly. The prototype of client and server is developed in Java language, and it is proved by the test that the system works stably and the collected data have practical values.

An accurate geometry and contact finite element model of double enveloping hourglass worm gearing with manufacturing errors are constructed. The hybrid method of finite element method and penalty function method is employed to investigate the contact pattern and contact pressure on tooth surface as well as the load share among contacting tooth pairs with the coexistence of manufacturing error and load. The influences of different values of load and different types of errors on contact pattern and contact pressure as well as load share are studied. The mutual influences of the load and the error on tooth contact and load share are also analyzed. Some results useful for production and application of the worm gearing are presented.