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This paper presents an end-to-end deep reinforcement learning (DRL) approach aimed at efficiently determining energy-optimal routes for a group of electric logistic vehicles, with the objective of minimizing operating costs. First, an Energy-Minimization Electric Vehicle Routing Problem (EM-EVRP) is formulated with an energy consumption model for electric vehicles, rather than Distance Minimization EVRP commonly favored in the literature. The energy consumption model incorporates several factors such as vehicle dynamics, road information, and charging losses. Then, the problem is reformulated based on the Markov decision process and solved using the transformer-based DRL method. The policy network is designed following the Transformer structure, including an encoder, a feature embedding module, and a decoder, where the feature embedding module is added to provide contextual information. Finally, extensive experiments demonstrate the superior of the proposed DRL method over existing learning-based methods and conventional methods, in solving both EM-EVRP and DM-EVRP. Notably, the formulated EM-EVRP achieves greater cost reduction than the traditional DM-EVRP. • Build an energy consumption model of electric vehicle for Electric Vehicle Routing Problem. • Formulate a novel energy-oriented Electric Vehicle Routing Problem. • Propose a deep reinforcement learning with transformer-based neural network to solve the Electric Vehicle Routing Problem. • Feature embedding module is added between the encoder and decoder of Transformer to provide contextual information. [ABSTRACT FROM AUTHOR]

Multi-mode hybrid electric vehicles (HEVs) have become popular in recent years because of their high efficiency and excellent overall performance. However, the introduction of multiple operating modes has led to some issues, e.g., mode shift problems, which are not observed in conventional HEVs with a single mode. Similar to a gear shift map in conventional vehicles, the mode shift map in multi-mode HEVs needs to be properly designed. In this study, the energy management algorithm of a multi-mode HEV is optimized using dynamic programming (DP) considering the mode shift frequency and energy losses. A mode shift map is then extracted from the operating points of the DP results using the support vector machine (SVM). By combining the derived mode shift map and the equivalent consumption minimization strategy (ECMS), a real-time control strategy is proposed for the online energy management of the multi-mode HEV. The results of a case study show the effectiveness of the proposed mode shift map and energy management strategy. [ABSTRACT FROM AUTHOR]

• An eco-driving control strategy is proposed for vehicles approaching a signalized intersection. • The discharge of the vehicle queue is modeled and predicted. • The proposed strategy is based on a computationally efficient iterative dynamic programming algorithm. • Monte-Carlo simulation using real-world traffic data is performed for in-depth energy efficiency analysis. • Energy-saving potential enabled by the proposed strategy is investigated for both internal combustion engine and electric vehicles. This paper takes into consideration of vehicle queues at the intersection and proposes an energy-efficient driving strategy to improve vehicle energy efficiency and overall traffic throughput in an urban traffic environment. The proposed strategy is applicable for both electric vehicle and internal combustion engine vehicle, and the control framework is formed by three sections, a vehicle queue discharge predictor, a spatial-domain optimal control strategy for energy consumption minimization, and a speed tracker with consideration of collision avoidance constraints. The former is based on the intelligent driver model, which predicts an accurate vehicle queue discharge time. Then the iterative dynamic programming is utilized to find the optimal solutions with fast computational speed. Finally, the optimal speed profile is followed by a Proportion-Integration controller while keeping a safe inter-vehicular distance. A Monte-Carlo simulation is designed to evaluate the energy efficiency of the proposed strategy in the stochastic traffic environment. Compared to the regular eco-approach and departure and constant speed strategies that lack awareness of the queue, significant energy saving can be achieved of the proposed strategy. In addition, three typical cases are selected to study the energy efficiency when the proposed strategy is applied to internal combustion engine and electric vehicles, respectively. The results show the energy efficiency of electric vehicles is less sensitive to the queuing effect at the intersection because of regenerative braking and the overall higher efficiency of the electric motor in contrast to the internal combustion engine, especially in stop-and-go scenarios. [ABSTRACT FROM AUTHOR]

• HEV configurations are classified into series, parallel, power-split and multi-mode. • HEV configuration generation and modeling techniques are introduced. • Entire design space of HEV configuration is explored exhaustively. • 14 configuration types of multi-mode HEV are explored and identified. • Research gaps and future trends of HEV configuration studies are discussed. Global warming, air pollution, and fuel depletion have accelerated the deployment of hybrid electric vehicles (HEVs). Apart from the energy management, the configuration of hybrid powertrains plays a central role in achieving better fuel economy and enhanced drivability. This paper comparatively summarizes the configurations, modeling, and optimization techniques of HEVs. Four types of hybrid powertrain configurations available in the market, i.e., series, parallel, power-split and multi-mode, are introduced firstly, followed by their state-of-the-art and pros/cons. Among all configurations, multi-mode hybrid powertrains are observed to have the potential for utilizing the benefits of the other three types by switching the operating modes. Subsequently, the configuration generation and modeling techniques are summarized. By adopting the automated modeling method, the entire design space can be explored exhaustively, and 14 feasible configuration types are classified based on the binary tree. Finally, the research gaps and future trends of HEV configuration studies are discussed. [ABSTRACT FROM AUTHOR]

• Two semi-active hybrid battery systems combining LFP and LTO batteries are proposed. • A unified fuzzy-logic energy management strategy is designed to mitigate LFP degradation. • Hybrid battery systems were tested in simulated daily electric taxi operation. • One system increased battery life more, with 45.4% lifespan improvement. • The other achieved 12.49% annual average cost and 39.41% distance-based cost reduction. This paper proposes two semi-active configurations for hybrid battery system (HBS) in which L i F e P O 4 (LFP) and L i 4 T i 5 O 12 (LTO) batteries are combined to prolong the life span of LFP batteries used in electric vehicles (EVs). To protect the LFP batteries from frequent peak power demands, the first configuration (HBS #1) uses diodes and switches, while the second (HBS #2) adopts a bidirectional DC/DC convertor to decouple various batteries. To make a fair comparison of the two configurations, their component sizes are firstly determined using brute-force search considering energy capacity and cost. Then, a unified fuzzy-logic energy management strategy was designed and optimized for each configuration to mitigate LFP battery degradation. Simulated implementation of HBSs #1 and 2 in an electric taxi with standard daily operation (driving nearly 400 km per day) and charging patterns revealed that both have longer LFP lifespans (44.4% and 45.4% improvement, respectively), lower annual costs (12.49% and 11.52% reduction, respectively) and reduced distance-based costs (39.41% and 39.18% reduction, respectively) than a single-LFP battery configuration. Although HBS #2 demonstrated more battery life improvement, HBS #1 was found to be cheaper in EV application from the perspectives of total and distance-based cost. [ABSTRACT FROM AUTHOR]