Connected Hybrid Electric Vehicles Research Articles

A data-driven solution for intelligent power allocation of connected hybrid electric vehicles inspired by offline deep reinforcement learning in V2X scenario

The proper power allocation between multiple energy sources is crucial for hybrid electric vehicles to guarantee energy economy. As a data-driven technique, offline deep reinforcement learning (DRL) solely exploits existing data to train energy management strategy (EMS), which becomes a promising solution for intelligent power allocation. However, current offline DRL-based strategies put high demands on the quality of datasets, and it is difficult to obtain numerous high-quality samples in practice. Thus, a bootstrapping error accumulation reduction (BEAR)-based strategy is proposed to enhance the energy-saving performance with different kinds of datasets. After that, based on the advanced V2X technology, a data-driven energy management updating framework is proposed to improve both fuel economy and adaptability of EMS via multi-updating. Specifically, the framework deploys multiple V2X-based buses to collect real-time information, and updates the strategy periodically making full use of offline data. The results show that the proposed BEAR-based EMS performs better than state-of-the-art offline EMSs in terms of fuel economy, especially realizing an improvement of 2.25% when training with mixed datasets. It is also validated that the offline EMS with the updating mechanism can reduce energy costs step by step under two different kinds of initial datasets.

Deep Reinforcement Learning-Based Hierarchical Energy Control Strategy of a Platoon of Connected Hybrid Electric Vehicles Through Cloud Platform

Due to the features of strong non-linearity and hybrid driving with multi-power sources, a novel deep reinforcement learning(DRL)-based hierarchical energy control architecture is presented for a group of connected hybrid electric vehicles (HEVs) in the platoon through cloud platform. Firstly, a higher-level model predictive control (MPC) law is designed to determine the desired acceleration of connected HEVs in a platoon. Secondly,a reward function with the change of state of charge (SOC) and instantaneous fuel consumption as independent variables is constructed, and the expert knowledge-based deep deterministic policy gradient (DDPG) method with prioritized experience replay (PER) is used for designing the lower-level energy management strategy of connected HEVs platooning. Then, to illustrate the rationality of decision-making by the presented DDPG strategy, the influence of state variables such as vehicle speed, desired acceleration and battery SOC on the agent action values of presented DDPG method is discussed. Finally, the test results show that the proposed control scheme can reasonably allocate the engine and motor powers in real time, and finally achieve the safe and energy-saving driving of connected HEVs in the platoon.

Integrated Thermal and Energy Management of Connected Hybrid Electric Vehicles Using Deep Reinforcement Learning

Integrated Thermal and Energy Management of Connected Hybrid Electric Vehicles Using Deep Reinforcement Learning

Hierarchical Optimization of Speed Planning and Energy Management for Connected Hybrid Electric Vehicles Under Multi-Lane and Signal Lights Aware Scenarios

Hierarchical Optimization of Speed Planning and Energy Management for Connected Hybrid Electric Vehicles Under Multi-Lane and Signal Lights Aware Scenarios

Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning

Review Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning Min Hua 1, Bin Shuai 1,2, ∗ , Quan Zhou 1, Jinhai Wang 1, Yinglong He 3, and Hongming Xu 1 1 School of Engineering, University of Birmingham, Birmingham B152TT, UK 2 School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China 3 School of Mechanical Engineering Sciences, University of Surrey, Guildford, GU27XH, UK * Correspondence: shuaib@mail.tsinghua.edu.cn Received: 29 August 2023 Accepted: 11 December 2023 Published: 19 December 2023 Abstract: The growing adoption of hybrid electric vehicles (HEVs) presents a transformative opportunity for revolutionizing transportation energy systems. The shift towards electrifying transportation aims to curb environmental concerns related to fossil fuel consumption. This necessitates efficient energy management systems (EMS) to optimize energy efficiency. The evolution of EMS from HEVs to connected hybrid electric vehicles (CHEVs) represent a pivotal shift. For HEVs, EMS now confronts the intricate energy cooperation requirements of CHEVs, necessitating advanced algorithms for route optimization, charging coordination, and load distribution. Challenges persist in both domains, including optimal energy utilization for HEVs, and cooperative eco-driving control (CED) for CHEVs across diverse vehicle types. Reinforcement learning (RL) stands out as a promising tool for addressing these challenges. Specifically, within the realm of CHEVs, the application of multi-agent reinforcement learning (MARL) emerges as a powerful approach for effectively tackling the intricacies of CED control. Despite extensive research, few reviews span from individual vehicles to multi-vehicle scenarios. This review bridges the gap, highlighting challenges, advancements, and potential contributions of RL-based solutions for future sustainable transportation systems.

Energy Management Strategy Combining Double Deep Q‐Networks and Demand Torque Prediction for Connected Hybrid Electric Vehicles

This paper proposes an energy management strategy (EMS) based on double‐deep Q‐Networks (DDQN) with demand torque prediction (DTP) to optimize the fuel consumption of hybrid electric vehicles (HEVs) by online utilizing vehicle‐to‐vehicle (V2V) information. The main framework of the EMS is designed as DDQN, combining Q‐learning with the deep neural network to realize real‐time training and online optimization of the torque distribution between the internal combustion engine and the electric motor, and given that the model training easily falls into local minima and overestimation, two different Q‐networks are designed to decouple action selection and target evaluation. Meanwhile, BP‐network predicts future demand torque using the ego vehicle speed, front vehicle speed, and distance between the two vehicles to reduce the burden of model training due to the introduction of traffic information. The predicted demand torque is introduced into the DDQN‐based EMS as states, together with the distance traveled in the driving cycle and the current information on HEV. The effectiveness and adaptability to actual driving cycles of the proposed strategy are verified by comparison with DDQN‐based EMS without DTP and rule‐based EMS. The results show that the fuel consumption using DDQN‐based EMS without DTP is reduced by 4.1% compared with rule‐based EMS. The introduction of demand torque prediction resulted in a further 3.9% reduction in fuel consumption. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Mode transition prescribed performance control of connected hybrid electric vehicles based on driver intention fuzzy prediction

A novel hierarchical mode transition control (NHMTC) algorithm utilizing traffic information is investigated for realizing a superior dynamic and ride comfort performance of a connected hybrid electric vehicle (CHEV). The upper level includes the target vehicle velocity planning by model predictive control (MPC) considering less vehicle jerk based on the vehicle-to-infrastructure (V2I) information and the driving intention prediction by a type-2 fuzzy neural network (T2FNN) model using the planned target velocity. The lower level involves the automatic clutch system’s prescribed performance fixed-time controller (PPFTC) design and the desired velocity of the clutch engagement calculated by the predicted driving intention to improve the clutch position trajectory’s tracking accuracy and stability under the actual mode transition process. The comparison results with existing schemes under a co-simulation environment demonstrate the advantage and effectiveness of the designed NHMTC method.

A novel engine and battery coupled thermal management strategy for connected HEVs based on switched model predictive control under low temperature

Under a low-temperature environment, electric vehicles face serious environmental adaptability problems, and efficient vehicle thermal management strategies are urgently needed. This paper presents a novel engine–battery coupled thermal management strategy for connected hybrid electric vehicles (HEVs). An improved system structure for an engine–battery coupled thermal management system (engine–battery CTMS) is designed to avoid unnecessary heat loss. The control requirements of the engine–battery CTMS include minimum engine fuel consumption, minimum power battery aging damage and minimum system energy consumption, which constitutes a multi-objective optimal control problem in a finite time domain. Based on model predictive control (MPC) theory, a switched nonlinear MPC (NMPC) control strategy is proposed to solve the optimal control problem of the complex coupled multi-input multi-output system. To verify the effectiveness of the proposed strategy, three comparative experiments of the centralized NMPC-based and rule-based methods combined with the improved system structure and the unimproved system structure are designed. The results of the cosimulation experiment between MATLAB/Simulink and AMEsim under various driving cycles and different ambient temperatures show that the improved structure and switched control strategy confer great advantages in reducing the controller computation burden, engine fuel consumption, and power battery aging damage.

A Data-Driven Spatio-Temporal Speed Prediction Framework for Energy Management of Connected Vehicles

We present an integrated spatio-temporal framework for multi-range traction power and speed prediction for connected vehicles (CVs). It combines data-driven and model-based strategies to enable CVs energy efficiency optimization. The proposed framework focuses on urban arterial corridors with signalized intersections, and leverages the historical and real-time data collected from CVs and infrastructure to predict location-specific traction loads (e.g. acceleration at intersections), and augment them with time-specific speed profiles (e.g., stop duration at intersections). A Bayesian network is developed to provide a long-term load prediction informed by probabilistic analysis of historical traffic data at intersections and between intersections. Moreover, a shockwave profile model is adopted for modeling the queuing process at intersections by leveraging vehicle-to-infrastructure (V2I) communications, providing a short-range prediction of the vehicle speed with an enhanced accuracy. The benefits of the proposed load prediction framework are demonstrated for energy management of connected hybrid electric vehicles (C-HEVs). By incorporating the predicted loads into a multi-horizon model predictive controller (MPC), integrated power and thermal management of light-duty C-HEVs is enabled over real-world driving cycles, demonstrating a near globally-optimal fuel consumption over the entire trip with a < 1% deviation from dynamic programming (DP) results.

Real-Time NMPC for Speed Planning of Connected Hybrid Electric Vehicles

Eco-cruising is considered an effective approach for reducing energy consumption of connected vehicles. Most eco-cruising controllers (ECs) do not comply with real-time implementation requirements when a short sampling interval is required. This paper presents a solution to this problem. Model predictive control (MPC) framework was applied to the speed-planning problem for a power-split hybrid electric vehicle (HEV). To overcome the limitations of time-domain MPC (TMPC), a nonlinear space-domain MPC (SMPC) was proposed in the space domain. A real-time iteration (RTI) algorithm was developed to accelerate nonlinear SMPC computations via generating warm initializations and subsequently forming the SMPC-RTI. Proposed speed controllers were evaluated in a hierarchical EC, where a heuristic energy management strategy was selected for powertrain control. Simulation results indicated that the proposed SMPC yields comparable fuel savings to the TMPC and the globally optimal solution. Meanwhile, SMPC reduced MPC computation time by 41% compared to TMPC, and SMPC-RTI further reduced MPC computation time without compromising optimization. During the hardware-in-loop (HIL) test, the mean computation time was 9.86 ms, demonstrating potential for real-time applications.

Online mixed-integer optimal energy management strategy for connected hybrid electric vehicles

In this paper, an online mixed-integer optimal energy management strategy is proposed for connected hybrid electric vehicles. Firstly, a predictive framework is constructed based on the backpropagation neural network, aiming to predict the future information utilizing the connected vehicle technology. Subsequently, for the mixed-integer programming problem in the predictive horizon, a novel optimal algorithm is proposed in the predictive framework. Finally, the proposed strategy is verified under both simulation and hardware-in-the-loop system environments. The results show that the proposed strategy reduces fuel consumption by 25.34% and 1.13% compared with the rule-based EMS and equivalent consumption minimization strategy (ECMS)-based EMS, and reduces fuel consumption by 25.79% and 1.78% compared with the rule-based EMS and ECMS-based EMS in two typical conditions. The proposed strategy can reduce 84% computation time than the particle swarm optimization-based EMS in the same typical condition. Using real-word conditions, the proposed strategy can reduce fuel consumption by 9.2% compared with ECMS-based EMS. The proposed strategy achieved satisfactory results in a hard-in-loop experiment.

Pedestrian-Aware Supervisory Control System Interactive Optimization of Connected Hybrid Electric Vehicles via Fuzzy Adaptive Cost Map and Bees Algorithm

Electrified vehicles are increasingly being seen as a means of mitigating the pressing concerns of traffic-related pollution. Due to the nature of engine-assisted vehicle exhaust systems, pedestrians in close proximity to these vehicles may experience events where specific emission concentrations are high enough to cause health effects. To minimize pedestrians’ exposure to vehicle emissions and pollutants nearby, we present a pedestrian-aware supervisory control system for connected hybrid electric vehicles by proposing an interactive optimization methodology. This optimization methodology combines a novel fuzzy adaptive cost map and the bees algorithm to optimize power-split control parameters. It enables the self-regulation of interobjective weights of fuel and exhaust emissions based on the real-time pedestrian density information during the optimization process. The evaluation of the vehicle performance by using the proposed methodology is conducted on the realistic trip map involving pedestrian density information collected from the University College Dublin campus. Moreover, two bootstrap sampling techniques and effect of communication quality are both investigated in order to examine the robustness of the improved vehicle system. The results demonstrate that 14.42% mass of exhaust emissions can be reduced for the involved pedestrians, by using the developed fuzzy adaptive cost map.

Distributed Cooperative Energy Management System of Connected Hybrid Electric Vehicles With Personalized Non-Stationary Inference

This article develops a distributed cooperative energy management system (EMS) with two distributed control layers for speed-coupling plug-in hybrid electric vehicles (PHEVs). By introducing personalized non-stationary inference, this system can fuse driving behavior and vehicle state information to adaptively adjust power-split control parameters for the improvement of vehicle energy economy. In the on-board control layer, five sets of personalized control parameters are optimized offline using chaos-enhanced accelerated particle swarm optimization (CAPSO). In the distributed control layer, interval type 2 (IT2) fuzzy sets are applied to develop a real-time driving style recognition function. The driving behavior is detected remotely, via the vehicle-to-everything (V2X) network, and downloaded to adaptively adjust the power-split control parameters in the on-board vehicle controller. Hardware-in-the-loop testing is carried out based on the four laboratory driving cycles and four personal driving cycles. The proposed system has been demonstrated with strong robustness that saves energy by up to 5.25% over the equivalent consumption minimization strategy (ECMS), especially for gentle drivers. Even under harsh communication conditions (with signal loss 80+%), it still performs better than the ECMS (by 0.57%) and the series–parallel (SP) control strategy (by 2.66%).

Special issue on on-board optimization strategy design methods for connected hybrid electric vehicles

Special issue on on-board optimization strategy design methods for connected hybrid electric vehicles

Multihorizon Model Predictive Control: An Application to Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

In this article, we propose a multihorizon model predictive control (MH-MPC) approach with applications to integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs). The proposed MH-MPC leverages preview and optimization over a short receding and a long shrinking horizon, where the accuracy of preview, model, and integration can be different over different horizons. Compared with a conventional MPC-based approach with a short prediction horizon and terminal cost, the MH-MPC improves fuel consumption to a level comparable to dynamic programming (DP) while still being computationally affordable. A statistical sensitivity analysis over real-world city driving cycles is conducted to demonstrate the robustness of MH-MPC to moderate levels of uncertainty in the long-term preview.

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

AbstractConnectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

Improving Ride Comfort and Fuel Economy of Connected Hybrid Electric Vehicles Based on Traffic Signals and Real Road Information

Wireless communication technology has promoted the development of connected hybrid electric vehicles (CHEVs). With traffic signal information, the fuel economy of CHEVs can be improved via optimal speed planning. However, the road environment in most existing studies is unreal and riding comfort is ignored. Therefore, this paper uses the real phase and position information of traffic lights to establish a road model and proposes a multi-objective hierarchical optimal (MOHO) strategy. First, a speed planning module is developed as the upper layer. By integrating speed constraints, slope, and traffic light information, a model predictive control (MPC)-based speed planning strategy (SPS) is developed, which improves riding comfort. Second, an energy management module is developed as the lower layer. An adaptive equivalent consumption minimization strategy (A-ECMS)-based energy management strategy (EMS) is proposed, which achieves the optimal power distribution. The results show that the proposed MOHO strategy can improve riding comfort and fuel economy while avoiding vehicle stopping at the signalized intersection under two different road conditions.

Energy management strategy of connected hybrid electric vehicles considering electricity and oil price fluctuations: A case study of ten typical cities in China

Energy management strategy (EMS) plays an important role in improving energy economy of plug-in hybrid electric vehicles (PHEVs). The frequently fluctuations of electricity and oil prices have significant impacts on EMS optimization. In this study, the influence law of the varying prices on the optimal control policy is revealed. The statistical analysis of electricity and fuel prices for typical cities in China is proposed as a case study, and then a varying prices-conscious EMS for PHEVs is developed. The presented control strategy is optimized based on the simulated annealing particle swarm optimization (SA-PSO) algorithm. Taking into account the above efforts, the price influence surfaces (PIS) are proposed and a PIS-based adaptive EMS is finally established. The presented control strategy can achieve better energy economy under real-world driving condition than traditional EMS that doesn't concern the impacts of price variations. The results indicate that the energy cost of PHEV can be further reduced by up to 9.88% under certain driving situations.

Decentralized Optimal Merging Control With Optimization of Energy Consumption for Connected Hybrid Electric Vehicles

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.