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Fuel cell (FC) technology has been identified as a technically attractive solution to decarbonize the transportation sector, especially for heavy-duty vehicles. In this context, the industry and the scientific community are in need of advanced fuel cell systems (FCS) models that are able to replicate real-world operating conditions. Due to the scarcity of said models in the open literature, this study aimed to develop a comprehensive methodology to calibrate and validate multi-physics dynamic FCS models. Therefore, the key contribution of this paper is the detailed description of the calibration process for each component and the calibration order. The specific focus here was to accurately describe the behavior of the FC stack as well as the cathode, anode, and cooling circuits of the balance of plant. The model was calibrated with the aid of experimental data from a Toyota Mirai FC electric vehicle, which was predominantly retrieved from the vehicle's Controller Area Network (CAN) bus system thereby negating the need for major intrusion into the powertrain system. The validation process was deemed successful with the model being able to truthfully replicate the characteristics of the FC vehicle operated on the World-wide harmonized Light duty Test Cycle (WLTC) 3b and US06 driving cycle. The time-resolved physical parameters such as the cathode pressure, mass flow, or the FC stack temperature were captured with high fidelity, while the overall performance parameters such as the H 2 consumption in the stack and the system, and the compressor energy consumption were predicted accurately with a deviation lower than 0.47%, 1.75% and 1.89% with respect to the experimental data, respectively. • A comprehensive methodology to calibrate multi-physics dynamic FCS models is proposed. • The calibration method can use data from the CAN bus of an FCV in real operation. • The methodology is compatible with both warm and cold realistic driving. • The transient evolution of the key physical variables was predicted accurately. • The key performance indicators were predicted with a deviation lower than 2%. [ABSTRACT FROM AUTHOR]

The detection and isolation of early and minor faults in vehicle battery systems is vital to safe driving and improving power utilization. This paper proposes a data-driven model to achieve accurate, early, and economical fault detection and isolation. The model is based on kernel principal component analysis (KPCA), which maps complex nonlinear data from the input space into a high-dimensional feature space to gain a detection model with good performance. To overcome the difficulty of hyperparameter selection, KPCA is trained using Bayesian Optimization (BO) iterations with a small amount of labeled data and a large amount of unlabeled data. This step can obtain the optimal hyperparameter to greatly improve the model fault detection capability, which is beneficial for detecting both early faults and minor faults. In addition, a unified contribution graph based on the partial differentiation of KPCA was adopted to build a reasonable isolation scheme. The semi-supervised model of KPCA based on Bayesian Optimization and contribution graph is developed to reveal the relationship between fault and variable. Finally, the proposed method is fully tested on four fault datasets and the results prove the excellent detection capability in the early stage of faults compared with other methods and the accurate fault isolation capability from the occurrence to the end of the fault. • A novel data-driven semi-supervised fault detection and isolation for electric vehicle battery systems. • The proposed data-driven method greatly saves the cost of manual labeling and is an efficient and economic technical scheme. • The proposed data-driven method has a more accurate detection performance than other data-driven methods. • The proposed data-driven method solves the problem that unsupervised algorithms are difficult to isolate the fault. • The proposed method is highly versatile and scalable, and it may be used with a variety of battery systems from various vehicle models, as well as energy storage plants. [ABSTRACT FROM AUTHOR]

• Expert demonstration data of hybrid electric vehicle energy management are represented by domain adaptive meta-learning. • By embedding expert demonstration model, an apprenticeship-reinforcement learning framework is established to improve fuel economy. • It is verified under three hybrid vehicle models with different powertrain architectures and several driving cycles. • Compared to traditional reinforcement learning, the apprenticeship-reinforcement learning can effectively improve the fuel emergency and CO 2 emissions. Deep reinforcement learning (DRL) is a potential solution to develop efficient energy management strategies (EMS) for hybrid electric vehicles (HEV) that can adapt to the changing topology of electrified powertrains and the uncertainty of various driving scenarios. However, traditional DRL has many disadvantages, such as low efficiency and poor stability. This study proposes an apprenticeship-reinforcement learning (A-RL) framework based on expert demonstration (ED) model embedding to improve DRL. First, the demonstration data, calculated by dynamic programming (DP), were collected, and domain adaptive meta-learning (DAML) was used to train the ED model with the adaptive capability of working conditions. Then combined apprenticeship learning (AL) with DRL, and the ED model was used to guide the DRL to output action. The method was validated on three HEV models, and the results show that the training convergence rate increases significantly under the framework. The average increase that the apprenticeship-deep deterministic policy gradient (A-DDPG) based method applied to three HEVs achieved was 34.9 %. Apprenticeship-twin delayed twin delayed deep deterministic policy gradient (A-TD3) achieved 23 % acceleration in the power-split HEV. Because A-DDPG's EMS is more forward-looking and can mimic ED to some extent, the frequency of engine operation in the high-efficiency range has increased. Therefore, A-DDPG can improve the fuel economy of the series hybrid electric bus (HEB) by 0.2–2.7 %, and improvements averaged to about 9.6 % in the series–parallel HEV while maintaining the final SOC. This study aims to improve the sampling efficiency and optimal performance of EMS-based DRL and provide a basis for the design and development of vehicle energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

• Model-free decentralized coordination control of electric vehicle aggregator and energy hub entities is proposed. • Novel multi-agent DRL framework for continuous control is utilized. • LSTM and safety networks are introduced to stabilize the training process. • Developed algorithm exhibits effective and fast control against uncertainties. • Developed method outperforms 3 advanced benchmark algorithms. Energy hub (EH) is an independent entity that benefits to the efficiency, flexibility, and reliability of integrated energy systems (IESs). On the other hand, the rapid emerging of electric vehicles (EVs) drives the EV aggregator (EVAGG) as another independent entity to facilitate the electricity exchange with the grid. However, due to privacy consideration for different owners, it is challenging to investigate the optimal coordinated strategies for such interconnected entities only by exchanging the information of electrical energy. Besides, the existence of parameter uncertainties (load demands, EVs' charging behaviors, wind power and photovoltaic generation), continuous decision space, dynamic energy flows, and non-convex multi-objective function is difficult to solve. To this end, this paper proposes a novel model-free multi-agent deep reinforcement learning (MADRL) -based decentralized coordination model to minimize the energy costs of EH entities and maximize profits of EVAGGs. First, a long short-term memory (LSTM) module is used to capture the future trend of uncertainties. Then, the coordination problem is formulated as Markov games and solved by the attention enabled MADRL algorithm, where the EH or EVAGG entity is modeled as an adaptive agent. An attention mechanism makes each agent only focus on state information related to the reward. The proposed MADRL adopts the forms of offline centralized training to learn the optimal coordinated control strategy, and decentralized execution to enable agents' online decisions to only require local measurements. A safety network is employed to cope with equality constraints (demand–supply balance). Simulation results illustrate that the proposed method achieves similar results compared to the traditional model-based method with perfect knowledge of system models, and the computation performance is at least two orders of magnitudes shorter than the traditional method. The testing results of the proposed method are better than those of the Concurrent and other MADRL method, with 10.79%/3.06% lower energy cost and 17.11%/6.82% higher profits of aggregator. Besides, the electric equality constraint of the proposed method is only 0.25 MW averaged per day, which is a small and acceptable violation. [ABSTRACT FROM AUTHOR]

• A co-simulation platform for Hybrid Electric Vehicles is developed for virtual assessment of Energy Management, performance, CO 2 & pollutant emissions. • CO 2 & pollutant emissions models are based on convolutional neural network for accurate transient state prediction. • A continuous deep reinforcement learning based EMS of HEV is proposed. • DDQL is trained with random generated driving cycles based on Markov Chain approach from real recordings. • Cumulative d error for predicted pollutants emissions is below 8.5% while HEV simulation is running up to 10 times faster than real time in a standard laptop. The Energy Management Strategy (EMS) in an HEV is the key for improving fuel economy and simultaneously reducing pollutant emissions. This paper presents a methodology for developing hybrid models that enable EMS testing as well as the evaluation of fuel consumption, CO 2 and pollutant emissions (CO, NO x and THC). In this context, pollutant emissions are hard to quantify with static models such as the well-known map-based approach which is mainly due to the pronounced impact of transient effects. The novelty of this paper primarily comes from the characterization of pollutant emissions through Convolutional Neural Networks (CNN), providing high accuracy for both, instantaneous and cumulative values. The input parameters are classical Internal Combustion Engine (ICE) measurements such as engine speed, air mass flow, torque and exhaust temperature. The proposed CNNs are reduced to a minimum for low complexity and fast computability. These models are developed with experimental data from chassis dyno testing of a conventional turbo-charged gasoline engine vehicle. The pollutant emission models are used in conjunction with physical models of the remaining powertrain allowing for real time simulations of the complete HEV vehicle. The Double Deep-Q learning algorithm is proposed for the EMS and compared to the Dynamic programming (DP) solution. The introduced methodology is developed in a co-simulation framework between MATLAB-Simulink and AMESIM. The resulting model runs between 8 and 10 times faster than real time in an off-the-shelf PC. This provides the capability for developing models suitable for HIL (hardware-in-the-loop) and SIL (software-in-the-loop) applications. The final error in predicted CO 2 remains below 2.5% while the final cumulative error for pollutants is below 8.5% in the case of CO and HC emissions. [ABSTRACT FROM AUTHOR]

• WPT combined with pavement materials for long durability. • Detuning of resonant circuit and deficiency of power and efficiency caused by pavement materials as transmission media. • Adjust the voltage frequency to restore the resonance of the circuit to enhance the power and efficiency. • Power and efficiency prediction formulae containing pavement material factors. Wireless power transfer (WPT) is one of the ways to alleviate the charging difficulties of electric vehicles, accelerating the growth of ownership of electric vehicles to replace petrol vehicles towards carbon net zero in transportation. In order to ensure the durability of the primary circuits of WPT and prevent the circuits to become obstacles for driving, the primary circuits need to be buried in the pavement structure. However, pavement materials can adversely affect the resonant induction coupling process of WPT, reducing the power and efficiency of charging. In order to explore the influence of the type and thickness of pavement materials on the WPT, vibrating sample magnetometer was used to test the magnetization performance of raw materials of pavement. The effective relative permeability of the three most commonly used pavement materials, including AC-13, SMA-13 and PCC, was calculated according to the magnetic circuit model. The simulation models of primary coil, secondary coil and pavement materials were established in Ansys Maxwell software. The self-inductance and mutual inductance of the coils after inserting pavement materials were obtained. The output power and transmission efficiency of WPT were calculated according to the theoretical calculation formulae. The results showed that inserting pavement materials between the primary and secondary coil as transmission media enhanced the primary self-inductance, secondary self-inductance and mutual inductance, leading to the detuning of resonant circuit and the reduction of output power. After adjusting the frequency of the high-frequency voltage to the resonant frequency, the output power of WPT was improved. Among the three materials, the output power was the highest when AC-13 was used. In addition, the calculation formulae of resonance frequency, output power and efficiency after introducing pavement materials were given, providing calculation basis for the design of WPT pavement. [ABSTRACT FROM AUTHOR]

• Develop an agent-based model of electric vehicle (EV) and vehicle-to-grid (V2G) adoption. • The EV-V2G model can explicitly simulate social influences on the EV and V2G adoption. • Only 3.3% of survey participants were familiar with V2G. • V2G selling price and advertisement were not very influential to the diffusion of V2G. The potential widespread adoption of Electric Vehicles (EVs) has received considerable attention across the globe. However, as a promising technology for both EVs and smart grid, Vehicle-to-Grid (V2G) tended to receive much less attention. This paper developed an agent-based joint EV and V2G model to simultaneously simulate how EVs and V2G might diffuse across space and over time, with empirical findings from a questionnaire survey in Beijing. In particular, random forest models were developed with the survey data to generate each agent's preferences and attitudes towards EVs and V2G. The joint model also considered three typical levels of social influence, i.e., global influence, neighbor effect, and friendship effect, in the diffusion of EVs and V2G. Finally, the joint model was tested through several "what-if" scenarios, considering different V2G prices, EV/V2G advertisement intensities, and vehicle purchase restrictions. The survey results suggested that 67.7% of the respondents were familiar with EVs, but only 3.3% of them were familiar with V2G. However, over 70% of them would/might try V2G given that they had an EV. The model results suggested that the number of CV applicants was 6.19 times that of BEV applicants in 2030 in the baseline scenario, and only 27.8% of BEV users adopted V2G. Furthermore, V2G selling price, EV/V2G advertisement, and dedicated PHEV purchase permits were not very influential to the diffusion of V2G. The outcomes would be helpful for EV- and V2G-related stakeholders in policy making and technology investment. [ABSTRACT FROM AUTHOR]

Exergy, or availability, is a thermodynamic concept representing the useful work that can be extracted from a system evolving from a given state to a reference state. It is also a system metric, formulated from the first and the second law of thermodynamics, encompassing the interactions between subsystems and the resulting entropy generation. In this paper, an exergy-based analysis for ground vehicles is proposed. The study, a first to the authors' knowledge, defines a comprehensive vehicle and powertrain-level modeling framework to quantify exergy transfer and destruction phenomena for the vehicle's longitudinal dynamics and its energy storage and conversion devices (namely, electrochemical energy storage, electric motor, and ICE). To show the capabilities of the proposed model in quantifying, locating, and ranking the sources of exergy losses, two case studies based on an electric vehicle and a parallel hybrid electric vehicle are analyzed considering a real-world driving cycle. This modeling framework can serve as a tool for the future development of ground vehicles management strategies aimed at minimizing exergy losses rather than fuel consumption. • Development of an exergy-based modeling framework for ground vehicles. • All the energy storage/conversion devices are modeled relying on exergy principles. • Formal technique for location/quantification of vehicle powertrain irreversibility. [ABSTRACT FROM AUTHOR]