The development of vehicles-to-everything (V2X) communication and autonomous driving technologies offers novel opportunities for eco-driving of connected hybrid electric vehicles (HEVs). To enhance vehicle energy efficiency in complex traffic scenarios, this study proposes a novel interaction-aware eco-driving strategy utilizing a Safe Soft Actor-Critic (Safe-SAC) deep reinforcement learning (DRL) algorithm for connected HEVs, which jointly optimizes speed planning and lane-changing decisions. By leveraging V2X technology, multi-source traffic information is integrated into the DRL environment, where the state space encompasses the states of the ego vehicle, traffic flow, signal phase and timing (SPaT), and the states of surrounding vehicles, while the action space contains the target lane and desired acceleration of the ego vehicle. In contrast to previous works, the integration of traffic flow speed and lane occupancy for each lane as the state variables enables a more accurate representation of the dynamic characteristics of surrounding traffic. Furthermore, safety constraints and a multi-objective reward function are meticulously designed to balance energy efficiency, driving comfort, and travel efficiency while ensuring safety. To thoroughly evaluate the energy efficiency of the proposed eco-driving strategy, both dynamic programming (DP) and equivalent consumption minimization strategy (ECMS) are employed as the underlying energy management strategies (EMS). Finally, the effectiveness of the Safe-SAC strategy is successfully validated on the co-simulation platform based on the Simulation of Urban Mobility (SUMO) and Python under various traffic scenarios. Compared to the Krauss-LC2013 model, the findings highlight the superiority of the proposed Safe-SAC strategy in achieving an average energy saving of 38.5%. This strategy also enhances driving comfort and maintains higher travel efficiency.

Integrating range-extended electric vehicles (REEVs) in the automotive market is a key part of the drive toward environmental sustainability. This paper leverages an experience-shared approach to variable-step predictive control to improve REEV energy efficiency, where a transferable driver model is designed to accommodate varying driver experience levels via knowledge transfer. This model incorporates a confidence level factor to determine the effective length of speed prediction, ensuring a more accurate and reliable model predictive control system with lower requirement data. A grey wolf optimizer is employed as an advanced global solver in the model predictive control system of the studied REEV to seek better energy-saving performance. Experimental validation utilizes an industry-recognized driver-in-the-loop co-simulation platform to investigate the proposed approach’s performance. Compared to Gaussian mixture regression one, the transferable driver model achieves a 27.29% improvement in speed prediction accuracy. Incorporating the driver model, the proposed experience-shared variable-step predictive control approach helps a 3.9% reduction in fuel consumption compared to an LQR-driven MPC one.

Plug-in hybrid electric vehicles (PHEVs) with large battery packs have significant advantages in improving fuel efficiency and lowering harmful emissions. However, battery charging and discharging performance degrades dramatically at low temperatures, resulting in increasing vehicle operating expenses, which hinders the deployment of PHEVs in severe cold regions. To address this challenge, this paper proposes an energy management strategy (EMS) that combines a battery preheating strategy to preheat the battery to a battery-friendly temperature before vehicle operation. This study provides three specific contributions. First, a high-precision electro-thermal-aging coupled model for a wide temperature range is developed, considering the effect of temperature on the battery’s available capacity. Second, the grid- and battery-powered preheating strategies are established using a flexible polyimide heating film to preheat the batteries. Finally, the particle swarm optimization (PSO) algorithm is utilized to determine the preheating time, while Pontryagin’s minimum principle (PMP) is employed to solve the multi-objective energy management problem. The efficacy of the proposed method in low-temperature driving scenarios is validated, and the link between preheating needs, cost savings, driving mileage, and changes in the price of energy carriers is also explored. Simulation results indicate that at a −20 °C ambient temperature, grid- and battery-powered preheating solutions could cut energy usage by 48.30% and 44.89%, respectively, compared to the non-preheating option.

Fuel cell vehicles (FCVs) are considered a promising solution for reducing emissions caused by the transportation sector. An energy management strategy (EMS) is undeniably essential in increasing hydrogen economy, component lifetime, and driving range. While the existing EMSs provide a range of performance levels, they suffer from significant shortcomings in robustness, durability, and adaptability, which prohibit the FCV from reaching its full potential in the vehicle industry. After introducing the fundamental EMS problem, this review article provides a detailed description of the FCV powertrain system modeling, including typical modeling, degradation modeling, and thermal modeling, for designing an EMS. Subsequently, an in-depth analysis of various EMS evolutions, including rule-based and optimization-based, is carried out, along with a thorough review of the recent advances. Unlike similar studies, this paper mainly highlights the significance of the latest contributions, such as advanced control theories, optimization algorithms, artificial intelligence (AI), and multi-stack fuel cell systems (MFCSs). Afterward, the verification methods of EMSs are classified and summarized. Ultimately, this work illuminates future research directions and prospects from multi-disciplinary standpoints for the first time. The overarching goal of this work is to stimulate more innovative thoughts and solutions for improving the operational performance, efficiency, and safety of FCV powertrains.

Accurate and reliable degradation and lifetime prediction for lithium-ion batteries is the main challenge for smart prognostic and health management. This article proposes a novel semi-supervised self-learning method for battery lifetime prediction. First, three health indicators (HIs) are extracted from the partial capacity-voltage curve. Second, the capacity estimation model and lifetime prediction model are built using data from three randomly selected batteries in the source domain. Then, the HIs are used to reconstruct the historical capacities to provide pseudo values for self-training of the lifetime model. Finally, the self-trained lifetime model is used to predict future degradation. The uncertainty expression is also included to provide the probabilistic prediction of future capacities. Different application scenarios are considered in the verification. The mean lifetime prediction error is less than 23 cycles with only three known checkpoints for batteries aging under different profiles. Predictions for different battery types show that the errors are less than 50 cycles with relative errors less than 4.1% for long lifespan batteries, and less than 20 cycles with relative errors less than 5.21% for short lifespan batteries. This article guides proper solutions for lifetime prediction when the labeled capacities in the real world are limited.

The optimization-based energy management strategy (EMS) enables expertise to improve the performance of fuel cell vehicles (FCVs). Ongoing efforts are mostly focused on optimizing a centralized EMS using a variety of high-computing technologies without offering appropriate scalability and modularity for the onboard powertrain components. In real-time applications, the time-accomplishment capability of EMSs is crucial; hence, decentralized EMSs with low-cost components and limited processing capability are necessary. Local units handle the computation load on a modular platform. In addition, the decentralized system’s plug-and-play functionality minimizes the total cost. This paper presents a decentralized model predictive control (D-MPC) based on the consensus-based alternating direction method of multipliers (C-ADMM) that explicitly considers the coordination of the dynamic reactions of powertrain components and future driving profiles. In addition, a decentralized learning method is proposed to seek the optimal policy for the moving horizon dimensions in the D-MPC using the federated reinforcement learning (FRL) algorithm in order to improve processing time. Due to the deployment of a fully modular system in the proposed learning technique, agents are restricted from sharing their trajectories. Using a highly dynamic module-to-module communication layer in a fully decentralized arrangement, the powertrain components utilize the multi-step method to attain the global optimum. The performance of the proposed framework is evaluated with regards to its precision, convergence speed, and scalability. The results of numerical simulation and implementation demonstrated that the proposed method is superior to the centralized and fixed-horizon MPC approaches.

Connected and automated vehicle technology via vehicle-to-everything communication, can assist in improving energy efficiency for hybrid electric vehicles (HEVs). In particular, information about the timing of traffic lights and surrounding vehicles can be exchanged between traffic vehicles and in conjunction with vehicle state information, to improve the fuel economy of HEVs significantly. To this end, we propose a multi-lane hierarchical optimization (MLHO) algorithm based on a predictive control framework. The dynamic behaviors of the surrounding vehicles are first predicted, and then the traffic light information (e.g., signal phasing and timing) and vehicles’ state information are utilized in the design. MLHO is a two-level strategy wherein a multi-lane speed planning method for a host vehicle is formulated to plan the optimal speed and lane-change behaviors by considering vehicle power demand, driving comfort, and safety in the upper level. In the lower level, dynamic programming is adopted to devise energy management by tracking the optimal speed. Simulation results under real routes using the traffic simulation software Simulation of Urban Mobility show that the fuel economy of MLHO is improved by 32% on average compared to speed profile driven by a human driver model. In addition, traffic efficiency is enhanced significantly, i.e., different traffic occupancy results on the road indicate that the proposed MLHO is less affected by the traffic flow density. With different traffic densities, the maximum fuel consumption difference under the three considered scenarios is only 0.645L/100km.

Reliable and accurate velocity prediction can significantly contribute to the quality of connected vehicle control applications. Existing efforts focus on the velocity prediction without considering vehicle-to-vehicle (V2V) communication interruption. Hence, a stochastic velocity prediction method for connected vehicles considering V2V communication interruption is put forward for the first time. The missing V2V communication data are addressed by the piecewise cubic Hermite spline interpolation. Then, the processed data are used as the input variables of the best conditional linear Gaussian (CLG) prediction model. Specifically, the best CLG model is obtained by analyzing the influence of different input variables on the velocity prediction without V2V communication interruption. The results demonstrate that the prediction accuracy of CLG-based model is acceptable if the communication interruption time is less than 5 s compared to the non-interrupted V2V communication case. The sensitivity study of the best CLG model under multiple vehicles scenario indicates that choosing appropriate historical data substantially improve the prediction accuracy. Furthermore, the CLG-based predictor is proved to be an effective method to achieve higher prediction accuracy in two test road networks when compared with the Back-propagation and Long Short-Term Memory network.

Advances in connected automated technology allow for more efficient driving in heavy-duty transportation. By well coordinating the longitudinal movements of multiple vehicles driving in a string, eco-platooning control can significantly improve the driving comfort and fuel economy. Moreover, benefitting from the short following distances of the platoon members, the aerodynamic effects are believed to further reduce the overall energy consumption in heavy-duty applications. In this paper, we develop an aerodynamically aware cooperative adaptive cruise control (CACC) strategy based on nonlinear model predictive control (NMPC). The proposed strategy is implemented under different communication topologies: 1) predecessor following (PF), 2) leader following (LF), and 3) predecessor-leader following (PLF). The performance of three communication topologies is evaluated through several indexes, and the simulation results indicate that when the information of the platoon leader is broadcast to the other platoon members, resulting in a so-called LF or PLF topology, the string stability would be guaranteed, and the proposed strategy can improve the driving comfort of all three trucks by eliminating unnecessary accelerations. On the other hand, a remarkable decrement on demanded power can be derived due to the effect of air-drag reduction.

The large-scale retirement of electric vehicle traction batteries poses a huge challenge to environmental protection and resource recovery since the batteries are usually replaced well before their end of life. Direct disposal or material recycling of retired batteries does not achieve their maximum economic value. Thus, the second-life use of EV batteries has become the most economical and environmentally friendly solution. However, there are still many issues facing second-life batteries (SLBs). To better understand the current research status, this article reviews the research progress of second-life lithium-ion batteries for stationary energy storage applications, including battery aging mechanisms, repurposing, modeling, battery management, and optimal sizing. Energy management strategies are reviewed to maximize the economic benefits for SLBs, and the less-demanding applications of SLBs are presented. The technical challenges and future development trends of battery reusing technologies are also discussed. Finally, the conclusions and relevant recommendations for future studies are summarized.

As one of adaptive optimal controls, the Q-learning based supervisory control for hybrid electric vehicle (HEV) energy management is rarely studied for its adaptability. In real-world driving scenarios, conditions such as vehicle loads, road conditions and traffic conditions may vary. If these changes occur and the vehicle supervisory control does not adapt to it, the resulting fuel economy may not be optimal. To our best knowledge, for the first time, the study investigates the adaptability of Q-learning based supervisory control for HEVs. A comprehensive analysis is presented for the adaptability interpretation with three varying factors: driving cycle, vehicle load condition, and road grade. A parallel HEV architecture is considered and Q-learning is used as the reinforcement learning algorithm to control the torque split between the engine and the electric motor. Model Predictive Control, Equivalent consumption minimization strategy and thermostatic control strategy are implemented for comparison. The Q-learning based supervisory control shows strong adaptability under different conditions, and it leads the fuel economy among four supervisory controls in all three varying conditions.

E NERGY storage is a critical enabling technology for many complex mechatronic and power electronic systems, such as electrified vehicles, portable electronics, and smart grids. Unlocking its performance potential and reducing costs requires intelligent management systems, which are designed meticulously at the crossroads of multiple subjects, such as chemistry, materials, control theory, and electrical engineering. In addition to the benefits of improved efficiency and prolonged service life, advanced integration and management facilitate the storage of electrical energy from renewables, such as wind and solar energy, and accelerate a paradigm shift towards cleaner transport systems.

Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This article derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regression (GPR) technique is employed to capture the underlying mapping among capacity, storage temperature, and state-of-charge. By modifying the isotropic kernel function with an automatic relevance determination (ARD) structure, high relevant input features can be effectively extracted to improve prediction accuracy and robustness. Experimental battery calendar aging data from nine storage cases are utilized for model training, validation, and comparison, which is more meaningful and practical than using the data from a single condition. Illustrative results demonstrate that the proposed GPR model with ARD Matern32 (M32) kernel outperforms other counterparts and can achieve reliable prediction results for all storage cases. Even for the partial-data training test, multistep prediction test, and accelerated aging training test, the proposed ARD-based GPR model is still capable of excavating the useful features, therefore offering good generalization ability and accurate prediction results for calendar aging under various storage conditions. This is the first-known data-driven application that utilizes the GPR with ARD kernel to perform battery calendar aging prognosis.

This paper proposes a heuristic planning energy management controller, based on a Dyna agent of reinforcement learning (RL) approach, for real-time fuel saving optimization of a plug-in hybrid electric vehicle (PHEV). The presented method is referred to as the Dyna-H algorithm, which is a model-free online RL algorithm. First, as a case study, a detailed vehicle powertrain modeling of the Chevrolet Volt is built, where all the control components have been experimentally validated. Four traction operation modes are allowed by managing the states of two clutches and one brake. Furthermore, the Dyna-H algorithm is introduced via incorporating a heuristic planning strategy into a Dyna agent. This is the first time to apply the Dyna-H algorithm in the energy management field of PHEVs. Finally, a comparative analysis of the one-step Q-learning, Dyna, and Dyna-H algorithms is conducted in simulations. Numerous testing results indicate that the proposed algorithm leads to definite improvements in equivalent fuel economy and computational speed.

Plug-in hybrid electric buses (PHEBs), compared with traditional fuel-driven vehicles, can achieve higher fuel economy and lower pollution emissions. For a PHEB with a single-shaft parallel powertrain, a major challenge for researchers is to find approximate optimal energy management strategies that can run in real time. Motivated by this idea, this paper aims at minimizing PHEB fuel consumption with a temporal-difference (TD) learning method. First, historical driving cycle data from real-world bus routes are collected and processed and parameter variables of TD are introduced. Specially, this process is completed offline. Then, the configuration and main parameters of PHEB are presented, and a control-oriented dynamic system of the PHEB is constructed. Thereafter, the TD learning method based on historical data is introduced. Furthermore, the approximate optimal control strategy for energy management is proposed. Compared with the traditional optimal control strategy, the proposed method can realize real-time running without sacrificing the accuracy of optimization, because the learning method updates the estimates based on other learned estimates without calculating a final outcome. This method can learn directly from the data of running PHEBs without a simplified model of the PHEB, which can avoid the influence of model error. Finally, to verify this method, several different strategies are used for comparison. In addition, experimental results in real-world driving cycles demonstrate that the proposed method can improve the fuel economy obviously by up to 21% compared with a traditional charge-deleting, charge-sustaining scenario. Therefore, this novel method has great potential in realistic applications.

As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the unmeasurable hybrid states, including discrete backlash nonlinearity and continuous half-shaft torque, are of great importance. In this paper, a novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach. System models, including the electric powertrain and vehicle dynamics models, are established considering the drivetrain backlash and flexibility, and also calibrated and validated using vehicle road testing data. Based on the developed system models, the powertrain behavior is represented using hybrid automata according to the piecewise affine property of the backlash dynamics. A hybrid-state observer, which is comprised of a discrete-state observer and a continuous-state observer, is designed for the simultaneous estimation of the backlash position and half-shaft torque. In order to guarantee the stability and reachability, the convergence property of the proposed observer is investigated. The proposed observer are validated under highly dynamical transitions of vehicle states. The validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer.

The papers in this special section focus on cyber-physical systems in green transportation. Ground mobility is being in a paradigm shift toward more efficient and green transportation. Wireless networking, sensing, computing, and control advances have significantly changed the way the society interacts with the physical world. In the context of cyber-physical systems (CPS), transportation systems become highly multidisciplinary. They require an ever-increasing integration of mechanical, electrical/ electronic, control, and information disciplines. Emerging innovative technologies, such as automated driving and electrified vehicles, are also profoundly promoting connection, automation, and electrification of the current transportation sector.

In this paper, a bi-level control framework is proposed to improve the energy efficiency for a hybrid tracked vehicle. The higher-level discusses how to accurately predict power demand based on the Markov Chain. Specially, fuzzy encoding predictor is used for power demand prediction, and a real-time recursive algorithm is applied to fuse the future power demand information into transition probability matrix (TPM) computation. Furthermore, the Kullback-Leibler (KL) divergence rate is employed to decide the alteration of control strategy. The lower-level computes the relevant energy management strategy, based on the updated TPM and a model-free reinforcement learning (RL) technique. Simulation results illustrate that the vehicular energy efficiency in the proposed scheme exceeds the common RL control by tuning the KL divergence value. Comparative results also show that the developed control strategy outperforms the common RL one, in terms of energy efficiency and computational speed.

Ultracapacitors (UCs) are an important energy storage technology in automotive and grid applications. They have several advantages, including high power density and extraordinarily long lifespan. Accurate State-of-Charge (SOC) tracking of UCs is critical for the reliability, resilience, and safety in system operation. This paper presents a novel robust H infinity observer in order to realize the SOC estimation of a UC in real time. It is computationally efficient because the observer gain involved in the real-time computation can be readily synthesized offline. In comparison to state-of-the-art Kalman filtering (KF), the developed robust scheme can ensure high estimation accuracy even without prior knowledge of the process and noise measurement statistical properties. More significantly, the H infinity observer proves to be more robust and tolerant to modeling uncertainties arising from the change of operating conditions and/or cell health status. These benefits are experimentally verified.

This paper is concerned with model-based dynamic peak-power evaluation for LiNMC and LiFePO4batteries under different operating conditions. The battery test and our prior study on linear-parameter-varying (LPV) battery modeling are briefly introduced. The peak-power estimation method that incorporates an explicit prediction horizon and design constraints on the battery current, voltage, and SOC are elaborated, and its computational load is analyzed. The discharge and charge peak powers are quantitatively assessed under different dynamic characterization tests, in which a comparison with the conventional PNGV-HPPC method and approaches using the less accurate models is conducted. The robustness of the peak-power estimation approach against varying battery temperatures and aging levels is investigated. The methods to improve the credibility of the peak-power assessment in the context of battery degradation are explored.

This paper comparatively examines three different electrochemical energy storage systems (ESSs), i.e., a Li-ion battery pack, a supercapacitor pack, and a dual buffer, for a hybrid bus powertrain operated in Gothenburg, Sweden. Existing studies focus on comparing these ESSs, in terms of either general attributes (e.g., energy density and power density) or their implications to the fuel economy of hybrid vehicles with a heuristic/nonoptimal ESS size and power management strategy. This paper adds four original contributions to the related literature. First, the three ESSs are compared in a framework of simultaneous optimal ESS sizing and energy management, where the ESSs can serve the powertrain in the most cost-effective manner. Second, convex optimization is used to implement the framework, which allows the hybrid powertrain designers/integrators to rapidly and optimally perform integrated ESS selection, sizing, and power management. Third, both hybrid electric vehicle (HEV) and plug-in HEV (PHEV) scenarios for the powertrain are considered, in order to systematically examine how different the ESS requirements are for HEV and PHEV applications. Finally, a sensitivity analysis is carried out to evaluate how price variations of the onboard energy carriers affect the results and conclusions.