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Short vehicle speed prediction is important in predictive energy management strategies, and the accuracy of the prediction is beneficial for energy-saving performance. However, the nonlinear feature of the speed series hinders the improvement of prediction accuracy. In this study, a novel hybrid model that combines an autoregressive integrated moving average (ARIMA) and a long short-term memory (LSTM) model is proposed to handle the nonlinear part efficiently. Generally, the ARIMA component filters out linear trends from the speed series data, and the parameters of the ARIMA are determined with the analysis. Then the LSTM handles the residual normalized nonlinear items, which is the residual of ARIMA. Finally, the two parts of the prediction results are superimposed to obtain the final speed prediction results. To assess the performance of the hybrid model (ARIMA-LSTM), two tested driving cycles and two typical driving scenarios are subjected to rigorous analysis. The results demonstrate that the combined prediction model outperforms individual methods ARIMA and LSTM in dealing with complex, nonlinear variations, and exhibits significantly improved performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MAPE). The proposed hybrid model provides a further improvement for the accuracy prediction of vehicle traveling processes. [ABSTRACT FROM AUTHOR]

An efficient energy management strategy (EMS) is significant to improve the economy of hybrid electric vehicles (HEVs). Herein, a power‐split HEV model is built and validated against test results, and then the EMS is proposed for this model based on vehicle speed prediction and deep reinforcement learning (DRL) algorithms. The rule‐based local controller and global optimal empirical knowledge are introduced to enhance the convergence speed. It is shown in the results that the twin delayed deep deterministic policy gradient algorithm (TD3) achieves more satisfactory performance on converge speed and energy efficiency. The networks of the DRL algorithm with continuous control update more robustly during iterations, in contrast to the discrete ones. Although the power‐split HEV with lower control dimension can reduce the learning burden for DRL EMS; however, the multidimensional control space shows greater optimization potential. As a result, the equivalent fuel consumption of TD3‐based EMS with multidimensional continuous control differences from the global optimal algorithm only by 4.92%. Herein, it is demonstrated in the results that long short‐term memory recurrent neural network (LSTM RNN) performs better for vehicle speed prediction than classical RNN and BP neural network, and the predictive vehicle speed feature helps improve fuel economy by 0.55%. [ABSTRACT FROM AUTHOR]

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy. [ABSTRACT FROM AUTHOR]

This research aims to improve the performance and economics of fuel cell hybrid electric vehicles (FCHEVs), validated and established by introducing an innovative energy management strategy (EMS) based on a speed-predictive fusion model. Firstly, a mixed prediction model was built based on BiLSTM, TCN, and Self-attention (SA) mechanism to accurately search, capture and fuse multi-granularity features in time series. Then, Harris-Hawk Optimization (HHO) was used to optimize the dropout rate and model learning rate of the combined BiLSTM-TCN-SA time series model to improve the prediction accuracy and generalization ability of the model. Finally, stochastic model predictive control was combined with BiLSTM-TCN-SA to form SMPC-NSGA III algorithm, which was used for multi-objective optimization of fuel economy, fuel cell durability and battery durability. In this study, the effectiveness of the proposed strategy was verified under the condition of CLTC-P driving cycle. The experimental results showed that RMSE and R2 of HHO-BiLSTM-TCN-SA velocity prediction model are 1.169 and 0.998, respectively. In addition, the output of the model is within the confidence interval of 97.5 % of the real speed, and there is no significant difference, which is statistically significant. Under the SMPC-NSGA III strategy, the average efficiency of the fuel cell was increased by 12 % and 1 % respectively. • A new energy management strategy was created to improve fuel cell durability. • HHO-based optimisation of BiLSTM-TCN-Self-Attention hyperparameter fusion model improves speed prediction performance. • NSGA-III multi-objective optimization algorithm was combined with SMPC for collaborative optimization results. • The average efficiency of the fuel cell in the SMPC strategy is higher than the DP and FLC. [ABSTRACT FROM AUTHOR]

Given the vehicle speed during actual driving, it is possible to apply an advanced energy management strategy for achieving better efficiency and less emission. We conducted a study to predict the future speed while driving of city buses, where only a few bus driving data and bus stop IDs are used without external complex traffic information. The speed prediction models were developed based on long time short memory (LSTM) and a gated recurrent unit (GRU), and a deep neural network (DNN) is also adopted for the bus stop ID processing. The performances of the models were analyzed and compared such that we found the LSTM-based model presents remarkable and practical prediction ability in accuracy and time spent. Adopting the proposed speed prediction model would make it a reality sooner, application of the optimal energy control strategy in the real world. [ABSTRACT FROM AUTHOR]

Energy management strategies (EMSs) based on speed prediction are widely used in fuel cell hybrid electric vehicles (FCHEVs). However, two fundamental issues must be addressed: short-term speed prediction and power allocation across multi-scales. To address these issues, a hierarchical EMS for FCHEV based on multi-scale information fusion is developed in this study. In this strategy, a novel speed predictor that incorporates visual information and the motion states of a vehicle is used to predict the short-term speed. At the global level, the average power demand of each road segment is predicted based on real-time traffic information, which is used to plan the global reference state of charge (SOC) trajectory before departure. At the local level, fuzzy rules are utilized to determine the short-term reference SOC. The short-term optimal co-state of the Pontryagin minimal principle-based strategy is then updated. The results demonstrate that the proposed visual information speed predictor (VISP) enhances the prediction accuracy by 12%–32.5% and leads to a 3.36% reduction in the total cost compared with the EMS utilizing the conventional predictor. In addition, the proposed EMS makes the terminal SOC close to the target value and reduces the total cost by 20.33% compared to the benchmark strategy. • A hierarchical energy management strategy based on multi-scale information fusion is proposed. • A novel speed predictor that incorporates visual information and vehicle motion states is designed. • Global reference SOC trajectory method based on the real-time temporal and spatial traffic information of road segments is proposed. • Adaptive fuzzy rules are used to determine the shot-term reference SOC for each horizon. • Real urban driving cycle is used to verify the performance of the energy management strategies. [ABSTRACT FROM AUTHOR]

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is utilized to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43 % and 7.30 % in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123 ms, less than the sampling time interval 1s, proving its potential of real-time applications. • A cost-optimization energy management is devised for postal-delivery FCEVs. • A data-driven dual-loop spatial-domain battery SoC reference estimator is devised. • Both energy consumption and power source durability are accounted. • A comprehensive evaluation of power-allocation and operating costs is conducted. [ABSTRACT FROM AUTHOR]

• A hierarchical predictive energy management is designed for fuel cell trucks under vehicle-following scenarios. • The speed planning considering future changes of leading vehicle is designed to ensure safe inter-vehicle distance and minimize power demand. • A driving-habit-conscious fuzzy C-means clustering enhanced Markov Chain speed prediction method is proposed to forecast future speed of leading vehicle. • The proposed hierarchical predictive energy management for host vehicle can reduce fuel cell degradation cost by 1.31–3.48% and total operation cost by 0.75–1.94%. Fuel cell hybrid electric heavy-duty vehicles play a key role to realize green and low-carbon travel. To cope with actual traffic environment, an advanced energy management strategy (EMS) is crucial to ensure vehicle's efficiency and economy. In this paper, a predictive co-optimization control method is designed to achieve speed planning and energy management for the host vehicle with a fuel cell/Li-ion battery hybrid energy storage system under vehicle-following scenarios. Firstly, the host vehicle obtains the real-time speed of leading vehicle by communication technology, and speed prediction for the leading vehicle is implemented by fuzzy C-means clustering enhanced Markov Chain (FCM-MC) considering driving habits. Secondly, based on speed prediction results, the speed planning which aims to ensure safe inter-vehicle distance and minimize the demanded vehicular power is implemented to derive the speed curve of host vehicle. Finally, according to speed planning results, predictive energy management is applied to achieve power allocation between fuel cell (FC) and battery. The simulation results denote that the proposed speed prediction method can decrease forecast error effectively. The speed planning ensures the safe inter-vehicle distance. With speed prediction accuracy improved, the proposed hierarchical energy management strategy can reduce fuel cell degradation cost by 1.31%-3.48% and total operation cost by 0.75%-1.94%. [ABSTRACT FROM AUTHOR]

Driving intention and speed prediction are essential factors in the energy management of plug-in hybrid electric vehicles (PHEVs). This paper proposes an improved energy management strategy for the subject vehicle by speed prediction fused with driving intention and LIDAR data in a vehicle-following scenario. A driving intention recognition model is developed based on the gated recurrent unit (GRU), which takes the vehicle speed, throttle opening, and brake pedal force of the subject vehicle as input. Then integrating the LIDAR point cloud data and driving intention result of the subject vehicle to achieve more accurate speed prediction, where joint probabilistic data association and interacting multiple models methods are used to process LIDAR data. The more accurate speed prediction is then applied to design a prediction-informed adaptive equivalent consumption minimization strategy (PIA-ECMS) for real-time energy management optimization. Experimental results demonstrate the recognition accuracy of up to 88%, indicating that the driver's driving intention can be identified effectively. The speed prediction has an error margin of no more than 5.9 km/h. Compared with existing adaptive ECMS without speed prediction, the proposed PIA-ECMS can enhance fuel economy by 1.3–2.7% while achieving better SOC charge sustainability. • Integration of driving intention recognition and LIDAR into energy management. • Speed prediction is fused by driving intention recognition and LIDAR. • A new prediction-informed adaptive ECMS to achieve better fuel economy. [ABSTRACT FROM AUTHOR]

Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy. [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]