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Aluminum hydride molecules AlH are detected and identified by tunable dye-laser mass spectroscopy as collision-free UV excimer laser photoproducts of the Al alkyls TEA (triethylaluminum (C 2 H 5 ) 3 Al) and TIBA (triisobutylaluminum ( i -C 4 H 9 ) 3 Al) at 248 and 193 nm. An internal energy distribution analysis of the photoproducts shows only minor vibrational excitation of ν″ = 1 and a Boltzmann-type rotational energy distribution with a temperature of about 0.03 eV, compared to the incoming photon energy of 5.0 or 6.42 eV.

Large amounts of fossil fuels are consumed by motor vehicles annually, and hazardous exhaust emissions from the motor vehicles have caused serious problems to environment and human health. Eco-driving can effectively improve the fuel economy and decrease the exhaust emissions, which makes it vital to analyze the fuel consumption and exhaust emissions at given driving cycle, and investigate their sensitivities to eco-driving factors. In this paper, the fuel consumption and exhaust emissions of a Euro-6 compliant light-duty diesel vehicle were tested in Worldwide Harmonized Light Vehicles Test Cycles on a chassis dynamometer; further, the sensitivities of the eco-driving factors that influence the fuel economy and exhaust emissions were analyzed using validated vehicle model. For the vehicle model simulation, the effect of the coolant temperature on fuel consumption and exhaust emission only considered its effect on lubricating oil viscosity. The results showed that vehicle acceleration and velocity dominates the fuel consumption rates in Worldwide Harmonized Light Vehicles Test Cycles, where more than 50% of the exhaust emissions was emitted in the first 300 s; also, fuel economy and exhaust emission factors showed a significant dependency on the road grade, coolant temperature, vehicle velocity and mass. For the driver-controllable factors, high vehicle velocity and low road grade (via route-choice) were recommended to achieve low fuel consumption and exhaust emissions.

Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in intelligent battery management systems (BMSs). The current RUL prediction methods are mainly developed based on offline training, which are limited by sufficiency and reliability of available data. To address this problem, this paper presents a method for RUL prediction based on the capacity estimation and the Box-Cox transformation (BCT). Firstly, the effective aging features (AFs) are extracted from electrical and thermal characteristics of lithium-ion batteries and the variation in terms of the cyclic discharging voltage profiles. The random forest regression (RFR) is then employed to achieve dependable capacity estimation based on only one cell's degradation data for model training. Secondly, the BCT is exploited to transform the estimated capacity data and to construct a linear model between the transformed capacities and cycles. Next, the ridge regression algorithm (RRA) is adopted to identify the parameters of the linear model. Finally, the identified linear model based on the BCT is employed to predict the battery RUL, and the prediction uncertainties are investigated and the probability density function (PDF) is calculated through the Monte Carlo (MC) simulation. The experimental results demonstrate that the proposed method can not only estimate capacity with errors of less than 2%, but also accurately predict the battery RUL with the maximum error of 127 cycles and the maximum spans of 95% confidence of 37 cycles in the whole cycle life.

Hybrid electric vehicle is regarded as a promising type of transportation because of its low emission and good fuel economy. The control strategy plays an important role in achieving its finest performance. In this article, we investigate a series-parallel hybrid electric vehicle and make benchmarking test on a dynamometer. There is a HV button in the cockpit of the vehicle. In normal situations, the vehicle has charging lower limit. When the State of Charge (SOC) is too low, the engine will start to work to charge the battery. Besides, driver can also choose when and how to charge the battery by his own will via pressing the HV button. The problem is driver's choice is subjective and inappropriate decision may lead to deteriorate of the fuel economy. So in order to use the energy rationally, we present a new self-adaption control strategy based on vehicle-and-road incorporation for plug-in hybrid electric vehicle (PHEV), replacing the driver to make a decision. We make use of the GPS information to perform power distribution according to the driving route information. If the battery SOC is high enough to accomplish the future mission in urban area, we can make the engine output power following the road load. If not, we can make the engine output more power, fulfilling the driver's need and charging the battery or sustaining the battery SOC at the same time. Charging power should be adjusted according to the driving condition and power unit working status. Simulation is done to verify the feasibility of the proposed method and comparison is made between the proposed method and the original control strategy.

This paper mainly provide the discussion of the topologies applied in the fuel cell vehicles and related control strategy. The fuel cell vehicles acquires power from the fuel cell stack or from multi power sources like fuel cell stack and battery, so there are quite a few topologies can be applied in the fuel cell vehicle. According to the different topologies, there are many control strategies can be made use of to make sure the fuel cell vehicle can operate in a smooth and high efficiency state. Introduction Currently, the starving of the fossil energy and the deterioration of the environment have been widely focused. Meanwhile, more and more green gas has caused the increasing of the temperature of the earth. To solve the problem, many researchers have been involved. In the auto industry, the novel energy vehicle has been treated as an ideal solution to solve the problems. Among the novel energy vehicles, the fuel cell vehicles are deeply researched for its zero emission, high efficiency, and reasonable performance. Due to the characteristic of the fuel cell stack itself, the fuel cell vehicle with single fuel cell stack cannot fully meet the requirement of vehicle driving. Hence, researchers propose the hybrid power topologies, such as the fuel cell stack together with battery, fuel cell stack together with ultra-capacitor, .etc. The existence of the other power sources except the fuel cell stack can offset the drawbacks of fuel cell stack, such as the poor quick response performance, poor cold start performance, and fuel’s effect of hunger. Just like renewable energy vehicles, there are amount of control strategies can be applied in the fuel cell vehicles. Chang-jun Xie applies the neural network theory in the control of fuel cell vehicles [1]. Xiangjun Li employs the fuzzy logic theory in the control of fuel cell vehicles [2]. Omar Hegazy make use of the particle swarm theory to perform energy management in fuel cell vehicle [3]. By applying the advanced control theory, the fuel cell vehicle can operate in a desired state. In the following parts, the topologies of fuel cell vehicles and the control strategy of fuel cell vehicles are elaborative discussed. Topologies of Fuel Cell Vehicle According to the types of the power sources in the fuel cell vehicle, the topologies of fuel cell vehicle can be divided into two types, the single power source topology, and the multi power sources topologies. Single power source topology The fuel cell vehicle that with single power topology is driven by the fuel cell stack only. The illustration of this topology can be seen in figure 1; Motor Inverter DC/DC Fuel Cell Fig.1: Single power source topology In this topology, the fuel cell stack performs as single power source, making the structure quite simple. It also reduce the difficulty of the space layout and just requires simple energy management International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2015) © 2015. The authors Published by Atlantis Press 1651 or control strategy. However, this topology has some drawbacks that make it less likely be used in fuel cell vehicles. This topology requires high power fuel cell stack, which means the cost of the fuel cell stack should be quite high. The output power of the fuel cell stack follows the requirement of the vehicle driving. When the vehicle operating in low load mode, the efficiency of the fuel cell stack could be quite low, which is not benefit for the energy-saving effect. Moreover, the fuel cell stack cannot instantly follow the requirement of vehicle driving, the acceleration of the vehicle cannot be perfect. The multi power sources topology Depends on the type and the amount of the other power source except the fuel cell stack, the multi power sources topology can be divided into following types The full power topology. The full power topology is made up of the fuel cell stack and another type of power source that commonly is battery or ultra-capacitor. The illustration of this topology can be seen in figure 2 and figure 3. Motor Inverter DC/DC Fuel Cell

In recent years the world has come under increased pressure to reduce the harmful emissions caused by the burning of fossil fuels. In 2019 in the UK (pre-COVID), the transport sector was estimated to have emitted 27% of all greenhouse gas emissions, making it the highest emitting sector. This has led to government action, banning the sale of petrol and diesel cars by 2030, as well as the introduction of low and zero emission zones in our cities. Public transport modes, especially city buses, must therefore also transition towards zero emission technologies to continue to operate within city centres. Battery electric buses are currently the most common choice, with both single deck and double deck vehicles in regular use. However, long-term operational capabilities are still largely unknown and unreported. Hydrogen fuel cell electric buses are an emerging zero emission technology that have the potential to complement a battery electric bus fleet where the duty cycle is challenging for current battery electric configurations. In this study k-means cluster analysis is used to reduce a typical city bus network to a limited number of representative synthetic drive cycles, against which battery electric and hydrogen fuel cell bus technologies are evaluated. The resulting study derives energy consumption for a given chassis configuration, passenger load and heating requirement to determine the best technology for a cluster of routes. The methodology employed can provide bus operators with a robust mechanism to inform buying decision when transitioning to a zero emission fleet.

Adaptability to various driving conditions (TCs) is one of the essential indicators to assess the optimality of power management strategies (PMSs) of plug-in hybrid electric vehicles (PHEVs). In this study, a novel optimal PMS with the improved adaptability to TCs is proposed for PHEVs to achieve the energy-efficient control in momentary scenarios by virtue of advanced internet of vehicles (IoVs), thus contributing to remarkable promotion in fuel economy of PHEV. Firstly, the optimal control rules in the novel PMS, corresponding to diverse driving conditions, are optimized offline by the chaotic particle swarm optimization with sequential quadratic programming (CPSO-SQP), which can effectively endow the global optimization knowledge into the rule inspired method. Then, an online TC identification (TCI) method is designed by cooperatively exploiting multi-dimensional Gaussian distribution (MGD) and random forest (RF), where the MGD based analysis on the macrocosmic state of traffic contributes to valuable inputs for the RF based TC classification, and additionally the super regression ability of RF further improves the identification accuracy. Finally, the numerical simulation validations showcase that the novel optimal PMS can reasonably and instantly manage the power flow within power sources of PHEV under different TCs, manifesting its anticipated preferable controlling performance.

In this study, a machine learning method is proposed for online diagnosis of battery state of health. A prediction model for future voltage profiles is established based on the extreme learning machine algorithm with the short-term charging data. A fixed size least squares-based support vector machine with a mixed kernel function is employed to learn the dependency of state of health on feature variables generated from the charging voltage profile without preprocessing data. The simulated annealing method is employed to search and optimize the key parameters of the fixed size least squares support vector machine and the mixed kernel function. By this manner, the proposed algorithm requires only partial random and discontinuous charging data, enabling practical online diagnosis of state of health. The model training and experimental validation are conducted with different kernel functions, and the influence of voltage range and noise are also investigated. The results indicate that the proposed method can not only maintain the state of health estimation error within 2%, but also improve robustness and reliability.

Two-speed or multiple-speed automatic transmissions can obviously improve the overall manipulating performance in terms of shifting quality and energy efficiency when equipped in electric vehicles (EVs). This study details the design of a two-speed clutch-less automatic transmission (2AT) for EVs and the motor controlled shifting mechanism. Firstly, a novel two-speed clutch automatic transmission is devised with a motor-controlled shifting mechanism, which enables the shift motions and the speed control of the driving motor for synchronization during shifts. Secondly, a coordinated control strategy of the driving motor and controlling motor for shifting is detailed during different shifting processes to achieve fast and smooth shifting. The torque trajectory optimization during synchronizing process is attained by applying the Pontryagin's minimum principle. The simulation and experimental results verify the shifting mechanism design and the shift control algorithm in terms of shift response and smoothness for the designed 2AT.

In this article, a multi-objective optimization-oriented energy management strategy is investigated for fuel cell hybrid vehicles on the basis of rule learning. The degradation of fuel cells and lithium-ion batteries are considered as the objective function and translated into the equivalent hydrogen consumption. The optimal fuel cell power sequence and state of charge trajectory, considered as the energy management input, are solved offline via the Pontryagin’s minimum principle. The K-means algorithm is employed to hierarchically cluster the optimal data set for preparation of rules extraction, and then the rules are excavated by the improved repeated incremental pruning to production error reduction algorithm and fitted by the quasi-Newton method. The simulation results highlight that the proposed rule learning-based energy management strategy can effectively save hydrogen consumption and prolong fuel cell life with real-time application potential.

Prediction of short-term future driving conditions can contribute to energy management of plug-in hybrid electric vehicles and subsequent improvement of their fuel economy. In this study, a fused short-term forecasting model for driving conditions is established by incorporating the stochastic forecasting and machine learning. The Markov chain is applied to calculate the transition probability of historical driving data, by which the stochastic prediction is conducted based on the Monte Carlo algorithm. Then, a neural network is employed to learn the current driving information and main knowledge after the simplified correlation of characteristic parameters, and meanwhile the genetic algorithm is adopted to optimize the initial weight and thresholds of networks. Finally, the short-term velocity prediction is achieved by combining them, and the overall performance is evaluated by four typical criteria. Simulation results indicate that the proposed fusion algorithm outperforms the single Markov model, the radial basis function neural network and the back propagation neural network with respect to the prediction precision and the difference distribution between expectation and prediction values. In addition, a case study is conducted by applying the built prediction algorithm in energy management of a plug-in hybrid electric vehicle, and simulation results highlight that the proposed algorithm can supply preferable velocity prediction, thereby facilitating improvement of the operating economy of the vehicle.

Online optimal energy management of plug-in hybrid electric vehicles has been continually investigated for better fuel economy. This paper proposed a predictive energy management strategy based on multi neural networks for a multi-mode plug-in hybrid electric vehicle. To attain it, firstly, the offline optimal results prepared for knowledge learning are derived by dynamic programming and Pontryagin’s minimum principle. Then, the mode recognition neural network is trained based on the optimal results of dynamic programming and the recurrent neural network is firstly exploited to realize online co-state estimation application. Consequently, the velocity prediction-based online model predictive control framework is established with the co-state correction and slacked constraints to solve the real-time optimal control sequence. A series of numerical simulation results validate that the optimal performance yielded from global optimal strategy can be exploited online to attain the satisfied cost reduction, compared with equivalent consumption minimum strategy, with the assistance of estimated real time co-state and slacked reference. In addition, the computation duration of proposed algorithm decreases by 23.40%, compared with conventional Pontryagin’s minimum principle-based model predictive control scheme, thereby proving its online application potential.

This study investigates accurate state of charge estimation algorithms for lithium-ion batteries based on the long short-term memory recurrent neural network and transfer learning. The long short-term memory network with the five typical layer topology is firstly constructed to learn the dependency of state of charge on measured variables. The transfer learning algorithm with fine-tuning strategy is then exploited to regulate the parameters of fully connected layer and share the knowledge of other layers. By this manner, the information from the source data can be applied to predict state of charge of other batteries with less training data. Additionally, a rolling learning method is developed to update the model parameters when the battery capacity is degraded. The precision and robustness of the proposed framework are comprehensively validated through comparative analysis of multitudinous sets of hyperparameters and methods. The experimental results manifest that the developed framework highlights precise estimation capability of state of charge at different aging states and time-varying temperature conditions. In addition, the proposed algorithm is verified feasible when transferred to different batteries based on only 30% training data.

Unplanned charging supervision of electric vehicles may deteriorate their penetration in alleviating pollution and reducing the driving efficiency, and proper management is critical to reduce charging waiting time and efficiently design driving behaviours from spots to charging stations. Motivated by this, a novel bi-functional charging management strategy in virtue of the mobile edge computation based framework is proposed in this study to effectively book the charging piles with less waiting time and meanwhile achieve better energy efficiency during charging booking. First, a novel charging booking algorithm is developed to determine the most suitable charging station and optimally plan the shortest route to the preferred charging station. Second, a driving behaviour optimization method is designed to plan the efficient velocity profile of the trip to the selected station under the constrained time calculated by the charging booking algorithm. The simulation analysis validates that the proposed bi-functional management strategy can reasonably book suitable charging stations and efficiently reduce energy consumption in the charging booking process, highlighting its anticipated preferable performance.