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Accurate capacity estimation is important to ensure the safe operation of battery. Current data-driven capacity estimation methods mainly rely on large volume charging or discharging data, which usually lasts for tens of minutes to hours, to extract effective battery aging features. The large volume data requirement restricts the application in real-world scenarios to some extent. In this paper, a novel capacity estimation method, which only requires several-minute discharging data to realize accurate capacity estimation, is proposed. Firstly, the idea of regression shapelet is introduced and shapelet distance is defined to capture the battery degradation trend. Then maximum relevance minimum redundancy algorithm is used to select the representative shapelet feature set, which not only has strong correlation with battery capacity, but also contains least redundancy among each other. Finally, eXtreme gradient boosting is adopted as the final regressor mapping from shapelet distance to battery capacity. The proposed method is verified on the public Oxford battery dataset. Results show that 4min 1C discharging data is sufficient for the proposed method to realize accurate capacity estimation with mean absolute relative error of 0.86%, which is superior to existing methods in terms of data volume requirement or accuracy. Moreover, the proposed method is promising to be applied in vehicle-to-grid scenarios considering its light computational burden. • Shapelet is introduced to exploit battery degradation characteristics. • Maximum relevance minimum redundancy algorithm is used for shapelets selection. • XGBoost is used to map from shapelet distance to battery capacity. • 4min 1C discharging data could achieve SoH estimation with 0.86% error. [ABSTRACT FROM AUTHOR]

Abstract Developing electric vehicles, as one of the most effective measures in constructing clean transportation, has been vigorously prompted by China's government recently through series of beneficial policies. Thus, both the sales and manufacturing of electric vehicles have witnessed prosperous growth in the last decade, especially in Beijing, whose pure electric vehicles ownership has exceeded 170,000 by the end of 2017 and ranked first in China. However, large-scale deployment of electric vehicles may also bring about troublesome problems concerning the construction of charging infrastructure and stability of electric grid. A comprehensive analysis of usage pattern of electric vehicles, especially the majority for private usage, is useful in predicting the charging load and understanding the driving characteristics, thus guiding location of charging facilities and assisting energy management of electric grid. By collecting operational data of forty-one private electric vehicles with 33,041 trips and 4738 charging events, sixteen characteristic parameters including charge consumption, state of charge before/after charging, single-trip distance, daily distance travelled, specific energy consumption, etc. are analyzed in detail. The analysis results are useful in facilitating charging infrastructures construction, management of state gird, evaluation of emerging vehicular technology and so forth in Beijing and even other metropolises with similar situation. Highlights • Operational data of 41 private electric vehicles in Beijing were collected. • The usage pattern of Beijing private electric vehicles is investigated. • Usage pattern of electric vehicles is related to drivers' daily routine. • Inter-quartile range analysis is used to investigate the charging characteristics. [ABSTRACT FROM AUTHOR]

Combining eco-driving optimization and simultaneous proper energy management, this paper proposes an efficient co-optimization strategy of unmanned hybrid electric tracked vehicles (HETVs) based on a hierarchical control framework to achieve accurate path tracking and optimal energy management simultaneously. Constrained by a pre-known reference path, a deep Q-learning (DQL) algorithm with the AMSGrad optimizer is designed in the upper layer to optimize the velocity of both side tracks to find the best trade-off between energy economy and accurate path tracking. Based on the optimal velocity profile obtained from the upper layer, an explicit model predictive control method is designed in the lower layer to distribute the power between the engine generator and battery in real time to achieve approximate optimal fuel economy. Simulation results verify that the designed DQL method only requires 0.67 s on average for real-time velocity planning, which is markedly lower than the dynamic programming algorithm. In addition, the proposed method also exhibits higher rapidity and optimality for velocity planning than the traditional DQL algorithm. Compared with the model predictive control, dynamic programming and a process without velocity planning, the proposed co-optimization strategy achieves good fuel economy, accurate path tracking and high computational efficiency. • Co-optimization strategy is designed for path tracking and energy management. • Deep Q-learning algorithm with AMSGrad optimizer is developed to plan velocity. • EMPC controller is designed to distribute the power in real time. • The proposed strategy is compared with different methods and analyzed in depth. [ABSTRACT FROM AUTHOR]

In this paper, an energy management strategy (EMS) based on model predictive control (MPC) is proposed to minimize fuel cost, electricity usage and battery ageing. To fulfil the MPC framework, a novel speed predictor with a variable horizon based on a K-means algorithm and a radius basis function neural network, which contains various predictive submodels, is designed to cope with different input drive states. In addition, a Q-learning algorithm is applied to construct an adaptive multimode state-of-charge (SOC) reference generator, which takes advantage of velocity forecasts for each prediction horizon. The algorithm fully considers the model nonlinearities and physical constraints and requires less computational effort. Based on the SOC reference and predictive velocity, the MPC problem is formulated to coordinate fuel consumption and battery degradation. Moreover, considering the influence of real-time traffic information, a traffic model that simulates actual road conditions is constructed in VISSIM to evaluate the performance of the proposed EMS. The simulation results show that the proposed speed predictor can effectively improve the predictive accuracy, and the multimode control laws based on drive condition classification present superior adaptability in SOC reference generation compared to single-mode law. With the aforementioned two improvements, the proposed EMS achieves desirable performance in fuel economy and battery lifetime extension. • Cost-optimal problem is built for coordinating fuel economy and battery lifetime. • A novel speed predictor with variable horizon is constructed. • Q-learning algorithm is applied as the adaptive multimode SOC reference generator. • A traffic model is constructed in VISSIM to evaluate the performance of the proposed EMS. • Influences of SOC reference and predictive speed accuracy are discussed in depth. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time predictive energy management strategy (PEMS) of plug-in hybrid electric vehicles for coordination control of fuel economy and battery lifetime, including velocity predictor, state-of-charge (SOC) reference generator, and online optimization. In velocity predictor, the radial basis function neural network algorithm is adopted to accurately estimate the future drive velocity. Based on predictive velocity and current driven distance, the SOC reference in predictive horizon can be determined online by reference generator. To coordinate fuel consumption and battery degradation, a model predictive control problem of cost minimization including fuel consumption cost, electricity cost of battery charging/discharging, and equivalent cost of battery degradation, is formulated. To mitigate the huge calculation burden in optimization, the continuation/generalized minimal residual (C/GMRES) algorithm is delegated to find the expected engine power command in real time. Since original C/GMRES algorithm cannot directly handle inequality constraints, the external penalty method is employed to meet physical inequality limits of powertrain. Numerical simulations are carried out and yield the desirable performance of the proposed PEMS in fuel consumption minimization and battery aging restriction. More importantly, the proposed C/GMRES algorithm shows great solving quality and real-time applicability in PEMS by comparing with sequence quadratic programming and genetic algorithms. • Battery degradation is considered in predictive energy management. • RBF-NN algorithm is adopted to accurately predict future velocity. • SOC reference in predictive horizon is obtained by a modified method. • C/GMRES algorithm is applied to achieve real-time predictive energy management. • Performance and computational efficiency by proposed strategy are verified. [ABSTRACT FROM AUTHOR]

As a hybrid electric vehicle (HEV) key control technology, intelligent energy management strategies (EMSs) directly affect fuel consumption. Investigating the robustness of EMSs to maximize the advantages of energy savings and emission reduction in different driving environments is necessary. This article proposes a soft actor-critic (SAC) deep reinforcement learning (DRL) EMS for hybrid electric tracked vehicles (HETVs). Munchausen reinforcement learning (MRL) is adopted in the SAC algorithm, and the Munchausen SAC (MSAC) algorithm is constructed to achieve lower fuel consumption than the traditional SAC method. The prioritized experience replay (PER) is proposed to achieve more reasonable experience sampling and improve the optimization effect. To enhance the "cold start" performance, a dynamic programming (DP)-assisted training method is proposed that substantially improves the training efficiency. The proposed method optimization result is compared with the traditional SAC and deep deterministic policy gradient (DDPG) with PER through the simulation. The result shows that the proposed strategy improves both fuel consumption and possesses excellent robustness under different driving cycles. • A robust energy management strategy is established based on the SAC algorithm. • Munchausen reinforcement learning method is adopted to the SAC algorithm. • Prioritized experience replay is applied to improve training efficiency. • DP-assisted training method is proposed to enhance the "cold start" performance. • The proposed framework realizes better performance in fuel-saving and robustness. [ABSTRACT FROM AUTHOR]

Cellulose was used as feedstock to investigate the effect of electrolysis on the molten salt pyrolysis process. The mechanism of cellulose transformation in the electrochemically assisted molten carbonate pyrolysis (EMCP) system was evaluated by comparison with the molten carbonate pyrolysis (MCP) system. The gas product yield from the EMCP system reached 70.5 wt%, at the expense of the liquid product and tar. The maximum H 2 yields up to 8.31 mol/kg of cellulose at low temperatures without the catalyst in the EMCP system. Cellulose decomposition was catalyzed by the molten salt to produce a large number of volatiles, where the aldehyde groups in the volatiles were electrochemically oxidized to carboxyl groups. Subsequently, the carboxyl compounds combined with alkali metal cations to release hydrogen radicals and then underwent secondary cracking, along with the volatiles having low chemical bond energies. The EMCP system follows the Shrink Cylinder model, which is dominated by the two-dimensional reaction on the surface of the graphite electrode, where the electrolysis process is the determining step. [Display omitted] • The H 2 yields up to 6.95 mol/kg-cellulose at 550 °C by electrolysis and pyrolysis. • The aldehyde group in intermediate took oxidation into carboxylic acids at 0.5 V. • Electrolysis benefits the secondary cracking of intermediates to produce H 2. • The EMCP system was controlled by the electrochemical oxidation of aldehyde group. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality and meet the increased charging demand of electric vehicles, microgrids incorporating renewable energy and charging stations are considered one of the potential solutions. However, the inevitable uncertainties of renewable energy and load demand become a challenge for the online charging dispatch of the microgrid. Considering these uncertainties, this paper proposes a two-stage optimal framework for the online dispatch of a grid-connected DC microgrid. The first stage presents a power coordination model to obtain the schedule plans of the main grid, the energy storage unit and the charging station, where the combined robust and stochastic model predictive control approach with different granular models is developed to solve this problem and effectively deal with these uncertainties. In the second stage, the charging station allocation model is designed to determine the charging power for every EV, which takes into account the max-min fairness of the charging power. Numerical cases in the presence of uncertainties are studied to evaluate the proposed dispatch framework and the solving approaches. The simulation results show its superiority in both computational efficiency and operating cost. • A novel two-stage optimal framework for a microgrid including charging station is proposed. • The proposed considers uncertainties introduced by EV, PV, WT, and load. • RS-MPC with different granular models is adopted to deal with uncertainties. • Power allocation model of EV charging station considers the max-min fairness of charging power. [ABSTRACT FROM AUTHOR]

A novel deep reinforcement learning (DRL) control framework for the energy management strategy of the series hybrid electric tracked vehicle (SHETV) is proposed in this paper. Firstly, the powertrain model of the vehicle is established, and the formulation of the energy management problem is given. Then, an efficient deep reinforcement learning framework based on the double deep Q-learning (DDQL) algorithm is built for the optimal problem solving, which also contains a modified prioritized experience replay (MPER) and an adaptive optimization method of network weights called AMSGrad. The proposed framework is verified by the realistic driving cycle, then is compared to the dynamic programming (DP) method and the previous deep reinforcement learning method. Simulation results show that the newly constructed deep reinforcement learning framework achieves higher training efficiency and lower energy consumption than the previous deep reinforcement learning method does, and the fuel economy is proved to approach the global optimality. Besides, its adaptability and robustness are validated by different driving schedules. • The powertrain model of the series hybrid electric tracked vehicle is established. • A new control framework based on double deep Q-learning algorithm is constructed. • Modified prioritized experience replay is designed to improve training efficiency. • An adaptive optimization method is applied to update weights of the neural network. • The proposed deep reinforcement learning framework realizes better performance. [ABSTRACT FROM AUTHOR]

Accurate regional distributed PV power forecasting provides data support for power grid management and optimal operation. Distributed PV has the characteristics of large quantity, small capacity and difficulty in obtaining meteorological data. Statistical upscaling method is commonly used to forecast regional power. However, the current research ignores how to reasonably divide the sub-regions with similar output characteristics and mine the spatial and temporal correlation between different sub-regions. Therefore, this paper proposes a short-term regional distributed PV power forecasting method based on sub-region division considering spatio-temporal correlation. Firstly, the representative power plant is selected after dividing the sub-region by the AP clustering algorithm. Then, the GCN is used to extract spatial correlation features, and the LSTM is used to extract the evolution features of dynamic spatial correlation features, and the power forecasting models of representative plants in different weather types are established. Finally, the data integrity and similarity of the sub-region are scored, and the upscaling weight is determined to realize the power forecasting of the whole region. The distributed PV power generation data of Pingshan County, Hebei Province, China is used for simulation test. The results show that the forecasting method proposed has higher forecasting accuracy than the traditional model. • The spatio-temporal correlation between distributed PV power plants is studied. • Introduce how to divide appropriate distributed PV sub-regions. • A power forecasting method considering spatio-temporal correlation is proposed. • Sub-regional data evaluation improves the forecasting accuracy of regional PV. [ABSTRACT FROM AUTHOR]

This research proposes a reinforcement learning-based algorithm and a deep reinforcement learning-based algorithm for energy management of a series hybrid electric tracked vehicle. Firstly, the powertrain model of the series hybrid electric tracked vehicle (SHETV) is constructed, then the corresponding energy management formulation is established. Subsequently, a new variant of reinforcement learning (RL) method Dyna, namely Dyna-H, is developed by combining the heuristic planning step with the Dyna agent and is applied to energy management control for SHETV. Its rapidity and optimality are validated by comparing with DP and conventional Dyna method. Facing the problem of the "curse of dimensionality" in the reinforcement learning method, a novel deep reinforcement learning algorithm deep Q-learning (DQL) is designed for energy management control, which uses a new optimization method (AMSGrad) to update the weights of the neural network. Then the proposed deep reinforcement learning control system is trained and verified by the realistic driving condition with high-precision, and is compared with the benchmark method DP and the traditional DQL method. Results show that the proposed deep reinforcement learning method realizes faster training speed and lower fuel consumption than traditional DQL policy does, and its fuel economy quite approximates to global optimum. Furthermore, the adaptability of the proposed method is confirmed in another driving schedule. • The powertrain model of the series hybrid electric tracked vehicle is constructed. • A novel reinforcement learning-based energy management strategy is proposed. • The rapidity and optimality of the reinforcement learning method are validated. • A new optimization method is applied to update the weights of the neural network. • The proposed deep reinforcement learning method realizes better performance. [ABSTRACT FROM AUTHOR]