The emerging connected and automated vehicle (CAV) technologies present opportunities to improve traffic safety, economy, and efficiency. However, the diverse uncontrollable human-driven vehicles (HDVs) will continue to predominate traffic for a long time, resulting in coexisting CAVs and HDVs in the form of mixed vehicle platoons. This study proposes a mixed vehicle platoon forming method based on a two-stage control framework to adapt to dynamic mixed traffic environments. The platoon formation generation stage creates feasible formation (i.e., a spatially coordinated mix of CAVs and HDVs ensuring safe and efficient platoon control) appropriate for mixed traffic based on the empirical formation method. The multiagent reinforcement learning is used in the second stage for realizing the platoon forming control safely and efficiently with the guidance of feasible formation. Finally, extensive simulations are conducted using the Highway-env simulator to evaluate the effectiveness of the proposed method. The results demonstrate that the proposed method can effectively control CAVs in conjunction with HDVs to form a mixed vehicle platoon, achieving an average energy efficiency improvement of up to 10.69% and a reduction in travel time by up to 2.73% compared to benchmark strategies.

The equivalent circuit model (ECM) is widely used in lithium-ion battery management systems because of its well-balanced simplicity and accuracy. However, it remains unclear whether the ECM can accurately model the dynamics of parallel-connected battery cells when the individual cells are heterogeneous. To address this issue, an approximate-parameter ECM (AECM) has been proposed to represent the input–output characteristics of parallel batteries with inconsistent capacity and internal resistance in the frequency domain. Additionally, a first-order ECM for parallel individual cells has been established for comparison. The transfer functions of the two models are calculated to analyze how model errors change with increasing inconsistencies among cells in parameters such as individual cell capacity and internal resistance. Finally, electrochemical impedance spectroscopy is used to validate the errors of the AECM under different operating conditions. Theoretical analysis and experimental results indicate that even with a 6% capacity variation in a battery pack with heterogeneous cells connected in parallel, the AECM can still accurately characterize the battery pack dynamics with an error of less than 2% under various conditions, including several representative operation profiles for batteries in electric vehicle and power system applications, demonstrating the broad effectiveness of the proposed AECM.

The excessively high temperature poses a significant risk to battery health, accelerating degradation and causing damage. Despite the recognized importance of battery thermal management (BTM), numerous studies in this domain often overlook the distinct timescales associated with vehicle and battery thermal dynamics. This oversight can compromise the efficacy and cost-effectiveness of BTM strategies in efficiently controlling battery temperature. This study proposes a novel predictive battery thermal and energy management (p-BTEM) strategy for connected and automated electric vehicles. The p-BTEM leverages a cloud-enabled predictive control framework to synthesize the look-ahead constant and time-varying factors, e.g., vehicle, road, and traffic information. This synthesis aims to achieve global optimization of battery temperature in the Cloud while enabling local adaptations for vehicle acceleration and compressor power on the Vehicle End. This approach ensures proactive and economical regulation of battery temperature, especially in high temperature conditions, thereby maintaining the battery within optimal temperature ranges and reducing energy consumption in dynamic traffic scenarios. To assess the effectiveness of the p-BTEM, representative route simulations are conducted utilizing real-world data. The results reveal the exceptional performance of the p-BTEM in reducing battery cooling energy when compared to two benchmark strategies, with a minimum improvement of 8.58% and 10.31%, respectively. Moreover, the sensitivity analysis is performed to elaborate on the p-BTEM under the influence of traffic, communication, and algorithmic factors.

With the growing emphasis on sustainable transportation, enhancing vehicular energy efficiency in mixed traffic scenarios has become a key focus, particularly in cooperative control strategies for Connected and Automated Vehicles (CAVs) amidst the unpredictability of Human-Driven Vehicles (HDVs). These strategies are essential for improving driving safety, traffic efficiency, and reducing energy consumption. This study introduces a novel Physics-Enhanced Data-Enabled Predictive Control (PE-DeePC) method to optimize CAV control in mixed traffic flows, where the presence of HDVs introduces significant uncertainty. By incorporating partial system physics, such as velocity and acceleration, into a unified state-space equation framework, PE-DeePC effectively combines data-driven insights with physical models, enabling optimal control decisions that are both accurate and robust. This method tackles the challenges of improving energy efficiency in the face of noisy measurements and the unpredictable behaviors of HDVs. Extensive simulation results show that the PE-DeePC achieves superior performance in robust CAV control under noisy, unpredictable conditions, and reduces energy consumption in the entire mixed traffic flow. The framework demonstrates energy reductions of 8.57%, 3.59%, and 13.17% over existing benchmark algorithms, highlighting its potential as a reliable and efficient solution for improving traffic management and energy efficiency in real-world mixed traffic scenarios.

This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing system constraints during real-system operations. Even though reinforcement learning (RL) is capable of optimizing energy efficiency in intricate environments, it is challenged by safety requirements during both the training and deployment stages. The lack of safety guarantees impedes the application of RL to real-world problems. Compared with RL, model predicted control (MPC) can handle constrained dynamics systems, ensuring safe driving. However, the major challenges lie in complicated eco-driving tasks and the presence of disturbances, which pose difficulties for MPC design and constraint satisfaction. To address these limitations, the proposed framework incorporates the tube-based enhanced MPC (RMPC) to ensure the safe execution of the RL policy under disturbances, thereby improving the control robustness. RL not only optimizes the energy efficiency of the connected and automated vehicle in mixed traffic but also handles more uncertain scenarios, in which the energy consumption of the human-driven vehicle and its diverse and stochastic driving behaviors are considered in the optimization framework. Simulation results demonstrate that the proposed algorithm achieves an average improvement of 10.88% in holistic energy efficiency compared to the RMPC technique, while effectively preventing inter-vehicle collisions when compared to the RL algorithm.

The integration of look-ahead information into Model Predictive Control (MPC) frameworks has shown promise for intelligent transportation systems. However, transitioning Predictive Cruise Control (PCC) system research into practical application poses challenges due to numerous weighting parameters and increased computational demands in complex driving environments. Although the Weighted Sum Method is commonly used in PCC system research to balance fuel consumption and trip time objectives, it requires time-consuming weight tuning and often results in suboptimal performance due to fixed weighting parameters. To address this, this paper proposes a Utopia-tracking Model Predictive Control (UTM-MPC) controller, where the cost function is reformulated as the sum of the distances between the objectives and the average Utopia point over the prediction horizon. By analyzing the Pareto front of the PCC optimization problem under varying slope profiles extracted from digital map data, we demonstrate that the proposed UTM-MPC effectively leverages the geometric characteristics of the Pareto front to identify preferred trade-off solutions. The adaptive weighting mechanism—derived from the online-calculated Utopia point—enhances the robustness of the PCC system under complex and dynamic driving conditions. To mitigate the computational burden associated with integrating UTM-MPC into the MPC framework, we introduce a tailored neighboring extremal-based solving algorithm. Leveraging the receding horizon nature of MPC, this method requires only minimal updates to efficiently identify an optimal solution near the nominal trajectory from the previous sampling instance. Simulation results show that the UTM-MPC controller, with its adaptive weighting strategy, consistently outperforms the traditional Weighted Sum Method in terms of both fuel efficiency and trip time.

Optimal control schemes have achieved remarkable performance in numerous engineering applications. However, they typically require high computational cost, which has limited their use in real-world engineering systems. To address this challenge, Neighboring Extremal (NE) has been developed to adapt a pre-computed nominal control solution to perturbations from the nominal trajectory. The resulting control law is a time-varying feedback gain that can be pre-computed along with the original optimal control problem, and it takes negligible online computation. However, existing NE frameworks only deal with state perturbations while in modern applications, optimal controllers frequently incorporate preview information. Therefore, a new NE framework is needed to adapt to such preview perturbations. In this work, an extended NE (ENE) framework is developed to systematically adapt the nominal control to both state and preview perturbations. We show that the derived ENE law is two time-varying feedback gains on the state and preview perturbations. We also develop schemes to handle nominal non-optimal solutions and large perturbations to retain optimal performance and constraint satisfaction. Case study on nonlinear model predictive control is presented due to its popularity but it can be easily extended to other optimal control schemes. Promising simulation results on the cart inverted pendulum problem demonstrate the efficacy of the ENE algorithm. Note to Practitioners—Due to the vast success in predictive control and advancement in sensing, modern control applications have frequently been incorporating preview information in the control design. For example, the road profile preview obtained from vehicle crowdsourcing is exploited for simultaneous suspension control and energy harvesting, demonstrating a significant performance enhancement using the preview information despite noises in the preview (Hajidavalloo et al., 2022). Another example is thermal management for cabin and battery of hybrid electric vehicles, where traffic preview is employed in hierarchical model predictive control to improve energy efficiency (Amini et al., 2019). In Laks et al. (2011), light detection and ranging systems are used to provide wind disturbance preview to enhance the controls of turbine blades. In Yazdandoost et al. (2022), virtual water preview is employed using integrated water resources management modelling to optimize agricultural patterns and control level of water in lakes. In this work, we develop an extended neighboring extremal framework that can adapt a nominal control law to state and preview perturbations simultaneously. This setup is widely applicable as in many applications, a nominal preview is available while the preview signal can also be measured or estimated online.

Lithium-ion batteries are crucial power sources in many industries. When they are used at low temperatures, their performance decreases greatly. Thus, heating is required. This article proposes a health-aware heating strategy based on the ac current. The strategy combines the use of the electro-thermal model for predicting the temperature increment in the battery caused by the ac current and a diagnostic method for lithium plating based on the charging transfer impedance. To obtain an accurate electro-thermal model for ac heating, various equivalent circuit models (ECMs) are tested at pulses with different frequencies and amplitudes, and the second-order ECM with a constant phase element is found to obtain the smallest error. Then, the model and the lithium plating criterion are used to establish the boundary map of the lithium plating and voltage limitations. Based on the map, the optimal ac frequency and amplitude are selected for the heating strategy. Finally, an ac heating experiment is implemented to verify the strategy. The results show that the strategy not only increases the battery temperature at a maximum rate of 5.25 °C/min, but also achieves a capacity decrease of 0.3% in 80 heating cycles. Moreover, according to the battery disassembly results, there is no lithium plating on the electrode. These results prove that the proposed heating strategy can prevent lithium plating and achieve a high heating efficiency.

Preceding vehicles typically dominate the movement of following vehicles in traffic systems, thereby significantly influencing the efficacy of eco-driving control that concentrates on vehicle speed optimization. To potentially mitigate the negative effect of preceding vehicles on eco-driving control at the signalized intersection, this study proposes an overtaking-enabled eco-approach control (OEAC) strategy. It combines driving lane planning and speed optimization for connected and automated vehicles to relax the first-in-first-out queuing policy at the signalized intersection, minimizing the host vehicle’s energy consumption and travel delay. The OEAC adopts a two-stage receding horizon control framework to derive optimal driving trajectories for adapting to dynamic traffic conditions. In the first stage, the driving lane optimization problem is formulated as a Markov decision process and solved using dynamic programming, which takes into account the uncertain disturbance from preceding vehicles. In the second stage, the vehicle’s speed trajectory with the minimal driving cost is optimized rapidly using Pontryagin’s minimum principle to obtain the closed-form analytical optimal solution. Extensive simulations are conducted to evaluate the effectiveness of the OEAC. The results show that the OEAC is excellent in driving cost reduction over constant speed and regular eco-approach and departure strategies in various traffic scenarios, with an average improvement of 20.91% and 5.62%, respectively.

This paper presents an adaptive leading cruise control strategy for the automated vehicle (AV) and first considers its impact on the following human-driven vehicle (HDV) with diverse driving characteristics in the unified optimization framework for improved holistic energy efficiency. The car-following behaviors of HDV are statistically calibrated using the Next Generation Simulation dataset. In a typical single-lane car-following scenario where AVs and HDVs share the road, the longitudinal speed control of AVs can substantially reduce the energy consumption of the following HDV by avoiding unnecessary acceleration and braking. Moreover, apart from the objectives including car-following safety and traffic efficiency, the energy efficiencies of both AV and HDV are incorporated into the reward function of reinforcement learning (RL). The specific driving pattern of the following HDV is learned in real-time from historical speed information to predict its acceleration and power consumption in the optimization horizon. A comprehensive simulation is conducted to statistically verify the positive impacts of AV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors. In freeway driving scenarios, simulation results indicate that the holistic energy efficiency is improved by an average of 6.03% and 6.41% compared to the reference control algorithms, specifically, RL without HDV consideration and model predictive control. These improvements highlight the significance of our approach in optimizing energy efficiency for mixed traffic on freeways.

Integrated power system combines electrical power for both ship service and electric propulsion loads by forming a microgrid. In this article, a battery/flywheel hybrid energy storage system (HESS) is studied to mitigate load fluctuations in a shipboard microgrid. This article focuses on how to determine the reference operation state of the flywheel, which depends on both future power load and the power split between the battery and flywheel. Two control strategies are proposed-an optimization-based approach and a lookup-table-based approach. Case studies are performed in different sea conditions, and simulation results demonstrate that the proposed control strategies outperform baseline control strategies in terms of power fluctuation mitigation and HESS power-loss reduction. A comparison between the two proposed approaches is performed, where their performances are quantified, the advantages and disadvantages of each strategy are analyzed, and the cases where they are most applicable are highlighted.

Microscopic traffic models describe how cars interact with their neighbors in an uninterrupted traffic flow and are frequently used for reference in advanced vehicle control design. In this paper, we propose a novel mechanical system-inspired microscopic traffic model using a mass-spring-damper-clutch system. This model naturally captures the ego vehicle's resistance to large relative speed and deviation from a (driver- and speed-dependent) desired relative distance when following the lead vehicle. Compared with the existing car-following (CF) models, this model offers physically interpretable insights into the underlying CF dynamics and is able to characterize the impact of the ego vehicle on the lead vehicle, which is neglected in the existing CF models. Thanks to the nonlinear wave propagation analysis techniques for mechanical systems, the proposed model, therefore, has great scalability so that multiple mass-spring-damper-clutch systems can be chained to study the macroscopic traffic flow. We investigate the stability of the proposed model on the system parameters and the time delay using the spectral element method. We also develop a parallel recursive least square with inverse QR decomposition (PRLS-IQR) algorithm to identify the model parameters online. These real-time estimated parameters can be used to predict the driving trajectory that can be incorporated into advanced vehicle longitudinal control systems for improved safety and fuel efficiency. The PRLS-IQR is computationally efficient and numerically stable, and therefore, it is suitable for online implementation. The traffic model and the parameter identification algorithm are validated on both the simulations and naturalistic driving data from multiple drivers. Promising performance is demonstrated.

This paper presents a preview servo-loop speed control algorithm to achieve smooth, accurate, and computationally inexpensive speed tracking for connected automated vehicles (CAVs). Differing from methods neglecting the future road slope and target speed information, the proposed controller focuses on taking advantages of this accessible future information to achieve better speed tracking performance. It integrates the future slope and target speed into an augmented optimal control problem, by solving which we obtain the optimal control law in an analytical form. The brake/throttle control laws consist of five parts,i.e., three feedback controls of system states and two feedforward items-preview of road slope and preview of target speed. This controller and its degenerate form,i.e., a classic PID, are implemented and applied to our automated vehicle platform, a Hybrid Lincoln MKZ. Experimental results show three major benefits of the proposed control-lower speed tracking errors, more gentle operations, and smoother brake/throttle behaviors.