徐 福国

In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.

This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles (HEVs) on a road with a slope. We assume that HEVs are in a connected environment with real-time vehicle-to-everything information, including geographic information, vehicle-to-infrastructure information and vehicle-to-vehicle information. The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time. The proposed strategy includes multiple rules and model predictive control (MPC). The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode. To improve fuel economy, the optimal energy management strategy is primarily considered, and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints, a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon. Therefore, this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed. To validate the proposed optimization strategy, a powertrain control simulation platform in a traffic-in-the-loop environment is constructed, and case study results performed on the constructed platform are reported and discussed.

An Internal Combustion Engine (ICE) experiences increased friction loss and fuel injection in cold temperatures, which provides the potential to further improve the energy efficiency based on the control freedom in hybrid electric vehicles (HEVs). This paper presents an engine thermal model with the coefficients calibrated through bench tests under different engine operating points. Moreover, we propose an engine on/off strategy that considers both the power demand and engine temperature preservation. Simulation results verify that the proposed powertrain control with engine thermal dynamics can further improve the energy efficiency for HEVs.

For hybrid electric vehicles, optimization of energy consumption is achieved by assigning the operations among the combustion engine and electric machines promptly according to real-time driving condition. In this paper, a real-time energy optimization strategy with model predictive control (MPC) scheme is proposed satisfied by the driver's power demand, such that improve fuel economy and produce optimal power split. To solve the MPC-based energy optimization problem, the key step is to predict the power demand during the prediction horizon. Therefore, a learning-based Gaussian process (GP) predictor is designed to predict the driver's demand using vehicle-to-everything (V2X) information. Moreover, an on-board parameter identification using recursive least squares (RLS) method is used to on-line update the parameters of the vehicle model, such that adapt the real traffic conditions. The strategy is further demonstrated with a case study result.

This paper proposed a hierarchical energy management strategy (EMS) for the charge station with mobility consideration of large-scale electric vehicles (EVs). The mobility characteristics of EVs is determined through the analysis of history data. In the upper layer, a day-ahead power trade planning to maximum the incomes from the power grid and the charging power for EVs. In the lower upper, a MFG-based real-time charging control strategy is designed to guarantee both the charging power performance and state of charge demand at the terminal time for next traveling. Simulation results show the effectiveness of the proposed hierarchical EMS.

In this article, the optimal control problem for nitrogen oxide emission reduction is investigated for diesel engines with a lean nitrogen oxide trap. First, a control-oriented model is developed based on conservation laws. Then, the optimal control problem is formulated as a multistage decision problem and solved using a dynamic programming algorithm under dynamical model constraints. A trade-off between fuel economy and nitrogen oxide emission is considered in the cost function of optimization. To demonstrate the obtained optimal control scheme, the parameters of the lean nitrogen oxide trap model are identified with data obtained from a GT-power-based diesel engine simulator. The numerical simulation results for two standard driving cycles and a stochastically generated driving cycle in comparison to a conventional logic-based control scheme are provided using the identified model in the MATLAB/Simulink platform.

This paper presents a new approach for solving the optimal merging control problem for hybrid electric vehicles (HEVs) under a connected environment. To achieve a reduction in energy consumption and save travel time, this paper focuses on deriving a decentralized feedback control law that provides not only the optimal velocity trajectory for merging but also a torque distribution strategy for the HEV powertrain. For this purpose, a distance domain-based optimal control problem is first proposed to avoid a free end-time cost function formulation that usually arises due to considering the minimization of traveling time. Then, the vehicle dynamics take into account the constraint of the optimization problem to evaluate the energy consumption at the power device level instead of the acceleration, unlike the common method used for reducing energy consumption in previous studies of merging control with linear models. The proposed optimization problem is solved by Pontryagin's maximum principle, and a traffic-in-the-loop powertrain simulation platform with a real-world emulated traffic scenario and high-fidelity HEV powertrain model is constructed to eliminate the randomly generated merging scenario. Finally, the simulation results obtained on the platform are demonstrated to validate the effectiveness of the proposed decentralized merging control law.

This paper developed a look-ahead horizon based optimal control scheme to jointly improve the efficiencies of powertrain and vehicle for hybrid electric vehicles (HEVs) with connectivity and automated driving. Both a speed planning strategy and energy management strategy is provided by the proposed approach. A constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-distance between ego vehicle and preceding vehicle. The optimal solution is derived through the Pontryagins maximum principle and verified in a traffic-in-the-loop powertrain simulation platform to show the effectiveness of the proposed approach.

This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus. Large scale vehicles in a traffic flow is targeted instead of individual vehicles, and it is assumed that the propulsion power of vehicles is hybrid electric powertrain. The control scheme is designed in the following two stages. In the first stage, in order to achieve speed consensus, the acceleration control law is designed by applying the MFG (mean-field game) theory. In the second stage, optimal powertrain control for minimizing energy consumption is obtained through coordinate the engine and the motor under the acceleration constraint. The simulation is conducted to demonstrate the effectiveness of the proposed control strategy.

In hybrid electric vehicles, the energy economy depends on the coordination between the internal combustion engine and the electric machines under the constraint that the total propulsion power satisfies the driver demand power. To optimize this coordination, not only the current power demand but also the future one is needed for real-time distribution decision. This paper presents a prediction-based optimal energy management strategy. Extreme learning machine algorithm is exploited to provide the driver torque demand prediction for realizing the receding horizon optimization. With an industrial used traffic-in-the-loop powertrain simulation platform, an urban driving route scenario is built for the source data collection. Both of one-step-ahead and multi-step-ahead predictions are investigated. The prediction results show that for the three-step-ahead prediction, the 1st step can achieve unbiased estimation and the minimum root-mean-square error can achieve 100, 150 and 160 of the 1st, 2nd and 3rd steps, respectively. Furthermore, integrating with the learning-based prediction, a real-time energy management strategy is designed by solving the receding horizon optimization problem. Simulation results demonstrate the effect of the proposed scheme.

In this work, a real-time energy management problem for a parallel hybrid electric vehicle (HEV) is proposed. The considered powertrain is built from a commercial HEV model. First, a non-linear optimal control problem under model predictive control scheme is formulated. The designed controller aims to generate the optimal power split and gear ratio schedule with respect to minimise the energy consumption of fuel and electricity. Moreover, the multiple shooting algorithm is introduced to decouple the dynamic constraints with the ability of avoiding the strong non-linearity while solving the optimisation problem. After that the optimisation problem is solved using sequential quadratic programming solver. Then, to evaluate the performance of the proposed real-time optimisation strategy on different traffic scenarios, the controller is applied to an adaptive cruise control (ACC) under connected environment. In this case, a solution of ACC with consideration of minimising energy consumption and maintaining string stability is provided. Finally, the proposed controller can be implemented in the traffic-in-the-loop platform without the knowledge of the predefined driving route. Simulations reveal that the proposed real-time control scheme shows great optimisation performance under the designed scenarios.

This paper presents a real-time optimization strategy that targets on short-term energy consumption for the power-split hybrid electric vehicles (HEV) by focusing the mechanical motion of the powertrains. Instead of long-term energy optimization, which is usually investigated in the literatures on state of charge (SoC) management problem of HEVs, a short-term behavior of energy consumption is focused under the assumption that SoC of the battery is enough to provide the electric power required by the optimization. To this end, the transient mechanical motion of the powertrain is considered in the optimization problem instead of the battery SoC. The proposed strategy consists of two layers: the prediction of driver's power demand based on the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication and the optimization of energy consumption along the predicted power demand. To deal with the stochastic uncertainties in the driver's power demand, Gaussian process regression model is developed for the prediction, and the optimization is formulated as a model predictive control problem with the mechanical model of the powertrain dynamics forced by the predicted driver's demand. Finally, the simulation results are demonstrated where the driver's demand is generated by a professional simulator under randomly eliminated traffic environment.

This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle (HEV) that operates with adaptive cruise control (ACC). Real-time energy optimization is an essential issue such that the HEV powertrain system is as efficient as possible. With connected vehicle technique, ACC system shows considerable potential of high energy efficiency. Combining a classical ACC algorithm, a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon. The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle. The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.

To improve a parallel hybrid electric vehicle's (HEV's) fuel economy, this study develops a real-time optimisation strategy with a learning-based method that predicts the driver's power demand under the connected environment. This demand is strongly constrained by the total power generated by the energy sources. Therefore, a key issue of solving the energy management problem in real time by model-based predictive optimisation is to predict the power demand of each receding horizon. The proposed optimisation strategy consists of two layers. The upper layer provides the prediction of the driver's torque demand. Gaussian process regression (GPR) is used to predict the driver's demand with the uncertain and stochastic estimation between the traffic environment and torque demand. Vehicle-to-vehicle and vehicle-to-infrastructure data are used as the inputs of the GPR model. The lower layer performs finite-horizon optimisation based on the cost function of energy consumption. A receding horizon control (RHC) problem is formulated, and optimisation is achieved by a sequential quadratic programming algorithm. To validate the proposed optimisation strategy, a powertrain control co-simulation platform with a traffic-in-the-loop environment is constructed, and results validation with the platform is demonstrated. The comparisons with the dynamic programming and no-prediction RHC results show that the proposed strategy can improve fuel economy.

Within the headway distance constraints, the potential for reduction of energy consumption by hybrid electric vehicles (HEVs) with connectivity could be achieved by optimizing the ego vehicle motion. This paper proposes a look-ahead traffic information-based real-time model predictive control scheme to minimize total monetary cost of HEVs. A chain Gaussian process approach is employed to estimate the probability distribution of future increments of vehicle number over a look-ahead horizon from vehicle-to-vehicle and vehicle-to-infrastructure information. The future motion of preceding vehicles could be predicted by the evolution of the traffic density model and velocity tracking model. The above problem is formulated as a nonlinear optimal control problem with predicted disturbance input and dynamic constraints. Optimal solutions are derived through Pontryagin's maximum principle. The effectiveness of the proposed control scheme is evaluated on a traffic-in-the-loop powertrain simulation platform by integrating a commercial traffic platform and an enterprise-level powertrain simulator.

This paper presents a new architecture of real-time HEV's energy management problem under a V2V and V21 environment using policy-based deep reinforcement learning. The ideal energy management controller that minimizes HEV energy costs needs to run engines most efficiently in the whole running considering battery SoC. The controller needs to predict the future vehicle speed and plan the power distribution to achieve it because the thermal efficiency of engines is more efficient when its rotational speed is higher. The future vehicle speed has relationship with connectivity information such as the behavior of the car in front, the traffic light signals, crowd of cars, and so on. This paper assumes the connectivity environment in the future and applies proximal policy optimization (PPO) [5] that is known as policy-based deep reinforcement learning algorithm to achieve the optimal power distribution predicting the future behavior by using connectivity information. In addition, this paper shows that locating the local controller in the reinforcement learning loop enables the AI controller to learn robustly. The local controller corrects against an exploration that is obviously not optimal or doesn't satisfy the constraints.

This paper proposes a real-time look-ahead velocity planning and torque distribution ratio prediction for parallel hybrid electric vehicles(HEVs) with consideration of traffic conditions in minimization of fuel and electricity consumptions. Firstly, mathematical models that could depict vehicle dynamics and surrounding look-ahead traffic dynamics for vehicle following scenario are built. Then, a receding horizon optimization problem that trades off fuel and electricity consumptions with dynamics model constraints is formulated. The proposed nonlinear model predictive control optimal problem is decoupled by multiple shooting and solved by a sequential quadratic programming approach to obtain numerical solutions. Simulations are conducted in MATLAB/Simulink platform with different emulated real-world traffic scenarios. Simulation results demonstrate the effectiveness of proposed control performance. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

In this paper, a real-time energy management for parallel hybrid electric vehicles (HEVs) to improve fuel efficiency is presented. A two-level optimal controller using model predictive control (MPC) scheme is designed to realize the energy management. The first level is designed to decide the operating mode with a simple rule-based block. In order to realize the total minimization of fuel and power, a nonlinear optimal control problem based MPC is formulated to generate the optimal power split and gear ratio schedule in the second level. The multiple shooting algorithm is introduced to decouple the dynamic constraints. After that the proposed optimal problem is converted into a nonlinear optimization problem with the extra variables. Then the problem is solved using sequential quadratic programming (SQP) method. A virtual traffic simulation platform CarMaker is built to emulate the real driving conditions. A co-simulation platform can be designed with MATLAB/Simulink and CarMaker which is called traffic-in-the loop -platform (TILP). The proposed controller can implement in the TILP platform without relying on predefined route.

For both globally suboptimal solution and implementable strategy, a real-time energy management strategy, based on equivalent consumption minimization strategy (ECMS), is proposed for commuter hybrid electric vehicles (HEVs) running on fixed routes. The determination of the adaptive equivalence factor is a focus. By the statistical characteristics deriving from historical driving data, the infinite-horizon stochastic dynamic programming (SDP) optimization with a discount factor is first formulated for finding proper equivalence factor according to uncertain driving cycles on a fixed route. And then, a mapping of equivalent factor on the system state is established off-line by stochastic optimal solution deriving from SDP policy iteration algorithm. In the power splits online, the equivalence factor of the implemented adaptive ECMS is obtained from the mapping according to the real time driving condition to achieve the near global optimal control objective that fuel consumption is minimized and the battery state of charge (SOC) is maintained within the boundaries over the whole driving route. Based on the HEV test platform established by specialized GT-Suite, simulation results and comparisons in some real driving cycles are presented to verify the effectiveness of the proposed strategy and to evaluate the advantages over other strategies.

A traffic-in-loop powertrain simulation framework, aiming at real time driving simulation validation, is presented in this paper. Traffic scenario and powertrain model are built in different platforms with capability of information interchange. This bidirectional co-simulation is capable to imitate I2V (infrastructure to vehicle)communication, connected vehicle communication and to capture driver behavior under stochastic traffic environment. With these real driving information, interaction influence between traffic and powertrain could be reflected online. An application example of optimal control design for diesel engine with after-treatment system is illustrated. Simulation validation on deterministic and stochastic traffic scenario are conducted, respectively. It could be concluded the necessity of onboard control scheme for powertrain in real-world traffic consideration.

The energy management strategy used to split the energy flow among different energy resources of hybrid electric vehicles plays a critically important role in achieving fuel economy. Additionally, battery degradation and high production cost lead to the necessary consideration of the battery lifetime in the energy management strategy design for a plug-in hybrid electric vehicle (PHEV). This paper investigates the PHEV energy management problem taking into consideration battery lifetime on how to distribute power between the engine and the electric equipment during the driving cycle to achieve the whole economy for a commuter PHEV. Shortest path stochastic dynamic programming (SP-SDP) is employed to address this energy management problem, which is formulated as a stochastic optimal control problem with the minimization of a weighted combination of the fuel and electricity consumption and the battery degradation rate for a stochastic process model with the statistic characteristics captured from the historical traffic speed profiles. The solution of this optimization problem, derived from a modified policy iteration algorithm, is a time-invariant, state-dependent power split strategy, which can be directly applied on the actual running vehicle. Simulation results carried on a PHEV Prius model in MATLAB/Simulink environment over some driving cycles are presented to demonstrate the effectiveness of the proposed energy management strategy. (c) 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

In this paper, optimization problem of aftertreatment system is investigated for diesel engines with lean NOx trap (LNT). First, a control-oriented LNT model is developed based on energy and mass balancing rules. Then, the optimization problem is formulated as a dynamic programming (DP) problem under developed dynamical model constraint and a trade-off between fuel economy and NOx emission is considered in the cost function. To demonstrate the optimal control obtained by solving the proposed DP problem numerically, the parameters of LNT model are identified with a GT-power diesel engine simulator, and numerical simulation results with comparison to a conventional rule-based control strategy are shown by using the identified model.

This paper focus on the investigation of solving the energy management optimization problem by the stochastic dynamic programming (SDP) policy iteration approach for commuter hybrid electric vehicles. In view of transition probabilities property of Markov chain while considering system physical constraints, the calculation method is given to several key parameter matrixes in the procedure of policy improvement when the SDP policy iteration algorithm is applied to stochastic system with states and inputs constraints. And then a modified energy management strategy for commuter hybrid electric vehicles is derived from the proposed calculation method. Moreover, several contrastive simulation results are presented to demonstrate the comparative advantage of the modified energy management strategy on the engine operating more efficiently with satisfactions of drivability demand and battery charge-sustaining.

This paper focus on the investigation of solving the energy management optimization problem by the stochastic dynamic programming (SDP) policy iteration approach for commuter hybrid electric vehicles. In view of transition probabilities property of Markov chain while considering system physical constraints, the calculation method is given to several key parameter matrixes in the procedure of policy improvement when the SDP policy iteration algorithm is applied to stochastic system with states and inputs constraints. And then a modified energy management strategy for commuter hybrid electric vehicles is derived from the proposed calculation method. Moreover, several contrastive simulation results are presented to demonstrate the comparative advantage of the modified energy management strategy on the engine operating more efficiently with satisfactions of drivability demand and battery charge-sustaining.

In view of battery deterioration and cost of fuel and electricity, this paper mainly focuses on how to distribute power between the engine and electric equipment for commuter plug-in hybrid electric vehicles(PHEVs) in order to improve the total economy in driving process. A battery lifetime model deriving from models of battery cycle degradation and calendar degradation is first given so that this lifetime model can be applied to energy management optimization directly. Then the Pareto energy optimization problem considering battery wear model is converted into a single-objective optimal control problem. Later a stochastic dynamic programming(SDP) approach is utilized to solve this optimal control problem. Simulation results show that the proposed strategy would effectively benefit vehicle economy by reducing total cost significantly.