Search

The eco‐driving strategy is of great significance in driving cost for plug‐in hybrid electric vehicles in driving trips, especially at signalized intersections. To address the issue of further energy saving, this study proposes an ecological approach and departure‐driving strategy by using syncretic learning with trapezoidal collocation algorithm. First, a syncretic learning‐based speed predictor is built by merging back propagation neural networks and radial basis function neural networks. Second, the syncretic learning‐based speed predictor and trapezoidal collocation algorithm are combined to optimize the speed trajectory. Third, the torque between the engine and the motor is distributed by the dynamic programming algorithm. Then, model predictive control optimizes torque output in the control time domain. Finally, the driving interval optimization method is designed to avoid mixed‐integer programming problems and redundant constraints, which make vehicles cross intersections without stopping. The numerical verification results show that the trapezoidal collocation algorithm with syncretic learning has more advantages than other methods in speed trajectory planning. Compared with the original trajectory, the driving time through the intersection is reduced and the total driving cost is lowered by 19.82%. Validation results confirm the effectiveness of the proposed strategy in energy consumption management at signalized intersections. [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]

This work proposes a predictive equivalent consumption minimization (P-ECMS) strategy for a power-split hybrid electric vehicle (HEV) using predicted driving cycle speed based on Monte Carlo (MC) algorithm. The proposed P-ECMS fully takes advantage of the predicted speed profiles by the MC algorithm to optimally determine the power split among energy sources. In this study, to validate the workings of the MCbased P-ECMS scheme, a series of simulations under a total of seven replicated driving cycles including New European Driving Cycle (NEDC), Worldwide Harmonised Light Vehicles Test Procedure (WLTP), Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), New York City Cycle (NYCC), California Unified Cycle (LA-92), and a combination of all (ALL-CYC) are conducted. The MC-based P-ECMS strategy is compared with a baseline ECMS in terms of fuel-saving. The fuel economy saving up to 6.01% under NEDC, 9.09% under WLTP, 6.33% under UDDS, 5.14% under HWFET, 1.96% under NYCC, 11.47% under LA-92, and 7.92% under ALL-CYC is achieved. The results in this article put forward that the proposed strategy delivers competitive fuel savings compared to the widely used baseline method. [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]

To exploit the energy-saving potential and optimize the battery state of charge (SOC) maintaining capability of energy management strategies for fuel cell hybrid vehicles in specific driving scenarios, this study proposes a scenario-oriented adaptive equivalent consumption minimization strategy (SA-ECMS) based on a Nanjing-oriented driving cycle (NODC) and future speeds predicted via a hybrid neural network model. The proposed strategy determines the initial value of the equivalent factor (EF) and the proportional coefficient of the adaptive increment based on the NODC. Then, it periodically adjusts the EF via local optimization process according to the predicted speed to enhance scenario-specific adaptability and energy efficiency performance. Simulation results show that the hybrid neural network model achieves an average calculation time of 0.0033 s with a root-mean-square error of 0.85 m/s for 10 s prediction horizon, outperforming existing speed prediction models. Compared with the existing SOC feedback-based ECMS, the proposed SA-ECMS effectively suppresses the battery SOC within a narrower fluctuation range of −0.12% to 0.33%, achieves a deviation of only 0.0026 from the SOC reference value, and reduces the equivalent hydrogen-fuel consumption by 2.49% to 7.06 g/km. • A scenario-oriented driving cycle construction method is proposed. • A novel hybrid neural network model with higher prediction accuracy is proposed. • The scenario-oriented adaptability of the proposed strategy is enhanced. • Both battery SOC maintaining capability and fuel economy are improved. [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]

Resource scarcity, global climate change and environmental pollution are increasingly constraining the development of the automotive industry. China proposes to reach the carbon peak by 2030; to reach the carbon neutral double carbon target by 2060 and gradually promote a green and low-carbon transition in energy development. The development of new energy vehicles is an important approach for China to realize its energy structure transformation in the automobile industry. HEV, as a transitional product of automobile energy transformation, has the advantages of both internal combustion engine vehicles and electric vehicles, which can improve the fuel efficiency and the emission problem of internal combustion engine vehicles and the range is longer compared to electric vehicles. One of the important aspects of HEV research is the design of whole vehicle energy management strategy based on the model predictions. Particularly, model-based predictive control is one of the mainstream energy management strategies nowadays, and its optimization effect is mainly subject to the model prediction accuracy. In this study, we constructed the ITS environment of a local roadway through simulation, compared the speed prediction effects of different speed prediction methods in different prediction time domains, and fused the historical information of vehicles (speed of the vehicle in front, distance, signal status, distance, and remaining time). It is found that N-BEATS is more effective in predicting vehicle speed in different prediction time domains, and the prediction accuracy of the speed prediction model is effectively improved after its fusion of multivariate information. • Speed prediction based on real driving data. • Comparing the accuracy of different speed prediction methods in different prediction time domains. • Fusion of multivariate information (vehicle history, front vehicle speed, distance, signal light status, remaining time, and other factors) for speed prediction. [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]

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]

With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver's intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R2 are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy. [ABSTRACT FROM AUTHOR]

Vehicle stability and energy efficiency are important considerations in vehicle engineering. In this context, the current paper presents an energy saving strategy for hybrid electric vehicles that incorporates vehicle lateral dynamic control in conjunction with energy efficiency. To this end, we first model the nonlinear vehicle lateral dynamics of a hybrid electric bus via a Takagi–Sugeno approach and combine this model with an H_{\infty } state-feedback controller via parallel distributed compensation. The controller matrices are obtained using linear matrix inequalities through an optimal energy-to-energy performance norm of the nonlinear vehicle model. Second, we propose a reference side-slip angle generating method and a set of tire force distribution rules, which under the premise of ensuring vehicle stability, minimize the overall energy consumption of the vehicle. Finally, we put forward a new speed prediction method based on vehicle lateral dynamics for hybrid electric vehicle energy saving. Human-in-the-loop simulated driving experiments are conducted where the bus performs lane-changing maneuvers with enhanced control properties under various driving conditions, demonstrating the reliability of the proposed energy-saving performance measures. • We model the nonlinear vehicle lateral dynamics of a bus with the so-called Takagi-Sugeno fuzzy approach and combine with an H_{\infty } state-feedback controller. • We propose a novel vehicle power system layout, a reference side-slip angle generating method, and a set of tire force allocation algorithms. • A new speed prediction algorithm is introduced. • Human-in-the-loop experiment results show that the proposed method has enhanced control properties and reliable energy-saving performance. [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]