Plug-in Hybrid Electric Vehicles Energy Research Articles

Hierarchical eco-driving control for plug-in hybrid electric vehicles under multiple signalized intersection scenarios

With the development of vehicle-to-everything (V2X) and autonomous driving technologies, plug-in hybrid electric vehicles (PHEVs) enable to absorb surrounding information to enhance economic driving. This study proposes a dynamic inverse hierarchical optimization method, which incorporates traffic-signal phase and timing as well as road data, to plan economic velocity and improve PHEV energy consumption. The hierarchical control framework determines the desired speed and arrival time in the upper layer using the shortest path faster algorithm. The lower level accounts for multi-objective velocity planning based on particle swarm optimization and Pareto theory. The inverse layering method solves the economic velocity optimization problem. With the support of V2X and autonomous driving technologies, the method enhances energy efficiency and computational efficiency in PHEVs through dynamic inverse hierarchical optimization. Simulation results highlight that the proposed algorithm leads to 7.43% improvement in energy consumption economy and the reduced calculation time, compared to the existing solutions. The hardware-in-the-loop experiments validate the real-time applicability of the proposed algorithm.

Deep Reinforcement Learning Based PHEV Energy Management With Co-Recognition for Traffic Condition and Driving Style

As one of the key technologies of plug-in hybrid electric vehicle (PHEV), the energy management strategy (EMS) is interactively influenced by driving style, traffic condition as well as vehicle operation state. In this paper, a novel twin delayed deep deterministic police gradient (TD3) algorithm based EMS integrating co-recognition for driving style and traffic condition is proposed, aiming to improve the generalization ability of EMS in various application scenarios with superior energy saving performance and higher self-learning efficiency. In particular, the TD3 based energy management architecture combining delayed policy update and smooth regularization technologies is studied to achieve simultaneous improvement for PHEV energy efficiency and strategy convergence speed. Furthermore, the traffic conditions are recognized by fuzzy C-means method, while the local minimum problem is effectively avoided by incorporating simulated annealing (SA) and genetic algorithm (GA). Sequentially, the driving styles are decoupled from recognized traffic condition, which are further recognized as three typical styles. The comparison results of the proposed strategy with several representative deep reinforcement learning based EMSs indicate that the TD3 based EMS outperforms DDQN and DDPG based EMSs in terms of convergence speed and energy saving performance. With considering the recognized traffic condition and driving style, the energy efficiency of TD3 based EMS is further improved with an ideal robustness to various driving cycles.

Markov velocity predictor based on state space optimization and its applications in PHEV energy management

Predictive energy management (PEM) strategy has shown great advantages in improving fuel economy for plug-in hybrid electric vehicles (PHEV). A Markov velocity predictor optimization method and its applications in PHEV energy management is studied in this paper. The initial Markov velocity predictor is constructed using complete driving cycle information and the state space of the Markov velocity predictor is then optimized for specified driving conditions using simulated annealing algorithm (SAA). The practical driving conditions are identified using a multi-feature driving condition recognition unit by using the support vector machine (SVM) method. Based on the driving conditions identified, velocities are predicted using the proposed method and optimized using dynamic programming (DP) algorithm in conjunction with the state of charge (SOC) reference and vehicle state. The energy management strategy derived is then implemented in the vehicle controllers. Comparing with the traditional rule-based energy management strategy, simulation results indicate that the PEM strategy proposed herein can reduce fuel consumption.

Application of a new type of lithium‑sulfur battery and reinforcement learning in plug-in hybrid electric vehicle energy management

The continuous increase in vehicle ownership has caused overall energy consumption to increase rapidly. Developing new energy vehicle technologies and improving energy utilization efficiency are significant in saving energy. Plug-in hybrid electric vehicles (PHEVs) present a practical solution to the arising energy shortage concerns. However, existing battery technologies restrict PHEV application as the most popular lithium-ion battery has a relatively high capital cost and degradation during service time. This paper studies the application of a new type of lithium‑sulfur (LiS) battery with bilateral solid electrolyte interphases in the PHEV. Compared with metals such as cobalt and nickel used in conventional lithium-ion batteries, sulfur utilized in LiS is cheaper and easier to manufacture. The high energy density of the new LiS battery also provides a longer range for PHEVs. In this paper, a PHEV propulsion system model is introduced, which includes vehicle dynamics, engine, electric motor, and LiS battery models. Dynamic programming is formulated as a benchmark energy management strategy to reduce energy consumption. Besides the offline global optimal benchmark from dynamic programming, the real-time performance of the LiS battery is evaluated by Q-learning and rule-based strategies. For a more comprehensive validation, both light-duty vehicles and heavy-duty vehicles are considered. Compared with lithium-ion batteries, the new LiS battery reduces the fuel consumption by up to 14.63 % and battery degradation by up to 82.37 %.

Discrete Mixed-Integer Shooting (DMIS): Algorithm and Application to Plug-In Hybrid Electric Vehicle Energy Management Accounting for Fuel Cranking and Actual Powertrain Efficiency Maps

This article presents an algorithm for mixed-integer nonlinear optimal control problems (OCPs) formulated directly in discrete time. In our current application, we address the energy management of a plug-in hybrid electric vehicle (PHEV) seeking to achieve minimum trip fuel consumption under a terminal state-of-charge (SOC) equality constraint while avoiding powertrain efficiency map approximations in order to maximize its fuel economy (FE). An additional engine cranking state with a nonsmooth state and control transition cost is then incorporated to prevent undesired engine on/off behaviors. We pursue a single shooting-based numerical algorithm, motivated by the success of the computationally efficient Pontryagin’s Minimum Principle (PMP)-based single shooting in achieving similar FE results as dynamic programming (DP) for the power-split optimization problem without the additional engine cranking state shown in prior work. The Discrete Mixed-Integer Shooting (DMIS) algorithm, with costate backward-in-time propagation, is presented here, avoiding unstable shooting iterations that are typically encountered in PMP-based single shooting. When applying DMIS to solve the power-split problem with cranking fuel consideration, we show through simulation that it leads to better FE results than the PMP-based single shooting without the cranking state, thus demonstrating the effectiveness of the DMIS algorithm. Finally, experimental dynamometer test results with the model predictive powertrain controller solved by DMIS for minimum trip fuel consumption are presented and discussed, demonstrating its real-time execution and the cranking fuel reduction capability.

From shifting gears to changing modes: The impact of driver inputs on plug-in hybrid electric vehicle energy use & emissions

This study measures the energy use and emission impacts of driver-induced changes to drivetrain configurations, known as drive modes. Drive modes allow drivers to alter their vehicles’ operating characteristics including propulsion energy sources, responsiveness of the steering wheel/accelerator pedal, etc. Plug-in Hybrid Electric Vehicles (PHEVs) can use energy from a combination of a battery and a fuel source to propel themselves, so drive modes that can shift vehicle propulsion power sources can directly impact energy usage and emissions in these vehicles. This study examines the use of drive modes in PHEVs. We use empirical, on-road vehicle data to explore whether drivers are using drive modes as intended by vehicle manufacturers and to develop a regression model and engine start analysis to quantify the impact of mode usage on PHEV energy efficiency and emissions. We find that incorrect use of drive modes leads to a 15–30% increase in energy usage and higher engine start emissions.

Real driving energy consumption and CO2 & pollutant emission characteristics of a parallel plug-in hybrid electric vehicle under different propulsion modes

PHEVs (Plug-in hybrid electric vehicles) are thought to be energy and environment friendly, while these conclusions are seldom verified during real driving tests. To evaluate PHEV real driving energy consumption, CO2 emission, and pollutants emission, one parallel PHEV was tested under real driving condition. The results indicate that the distance-specific energy consumption of CD (charge depleting) mode, compared with the CS (charge sustaining) mode, is 45% lower for WTW (Well to Wheel) evaluation. The CD WTW CO2 emission is 50% higher than the CS due to the electricity generation CO2 intensity. CD and CS mode could have similar CO2 emission when electricity CO2 intensity reaches 397.50 g/kWh. The limited charging reduced the PHEV energy conservation by around 50%, but the WTW CO2 emission is also reduced. Contrary to common belief, the PHEV CD mode real driving emission is underestimated, and it could be over 30 times higher than the CS mode. The deteriorated CD emission is caused by the inaccurate power demand prejudgments and frequent engine cold high-power re-start. The driving pattern comparison reveals that the deteriorated CD emission should not be a local but global concern. These results could be used for PHEV propulsion strategy optimization and test cycle design.

Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEVs). The approach is to identify route features from historical and realtime traffic data, in which information fusion model and traffic prediction model are used to improve the information accuracy. Then, dynamic programming combined with equivalent consumption minimization strategy is used to compute an optimal solution for real-time energy management. The solution is the reference for PHEV energy management control along the route. To improve the system's ability of handling changing situation, the study further explores predictive control model in the realtime control of the energy. A simulation is performed to model PHEV under above energy control strategy with route preview. The results show that the average fuel consumption of PHEV along the previewed route with model predictive control (MPC) strategy can be reduced compared with optimal strategy and base control strategy.

Research on innovative plug-in hybrid electric vehicle comprehensive energy consumption evaluation method based on statistic energy consumption.

The plug-in hybrid electric vehicle not only has the advantages of low emissions from electric vehicles, but also takes advantage of the high specific energy and high specific power of petroleum fuels, which can significantly improve the emissions and fuel economy of traditional vehicles. Studying its comprehensive energy consumption evaluation method is an important part of analyzing the economics of plug-in hybrid electric vehicles. This paper first puts forward the concept of statistical energy consumption and then proposes an innovative calculation method of plug-in hybrid electric vehicle energy consumption based on statistical energy consumption by referring to and analyzing the energy consumption test regulations of the United States, the European Union, and China. Given the two use case assumptions of charge depleting mode priority and charge sustaining mode only, considering the fuel consumption and the energy consumption that converts electrical energy consumption to fuel consumption, the probability density function of travel mileage distribution and energy consumption is derived. Finally, the interpretation and analysis of statistical energy consumption evaluation results are carried out.

Integrated Predictive Powertrain Control for a Multimode Plug-in Hybrid Electric Vehicle

Due to the complexity of a multimode plug-in hybrid electric vehicle (PHEV) powertrain, the energy management strategy of the said powertrain is a prime candidate for the application of optimal control methods. This article presents a predictive control strategy for optimal mode selection and powertrain control for a multimode PHEV capable of real-time control. This method utilizes predictions of future vehicle behavior in order to plan an optimal path of vehicle powertrain modes that minimizes energy consumption. This article also presents the integration of the developed optimal mode control strategy with an optimal powersplit strategy using nonlinear model predictive control to create a real-time integrated predictive powertrain controller (IPPC) responsible for all aspects of multimode PHEV powertrain supervisory control. The IPPC provides a real-time optimal solution to address the major challenge of a multimode HEV powertrain control: an integrated discrete and continuous optimization. Testing in simulation has shown the IPPC to be capable of reducing PHEV energy consumption by 4%–10% across real-world and standard drive cycles. In addition, the presented IPPC was deployed onto a rapid prototyping embedded controller where on-road, real-time testing has shown the IPPC to be capable of providing an energy reduction of 5%, thus confirming the energy savings observed in simulation.

Model Prediction and Rule Based Energy Management Strategy for a Plug-in Hybrid Electric Vehicle With Hybrid Energy Storage System

This article presents an energy management strategy (EMS) design and optimization approach for a plug-in hybrid electric vehicle (PHEV) with a hybrid energy storage system (HESS) which contains a Li–Ti–O battery pack and a Ni–Co–Mn battery pack. The EMS shares power flows within the hybrid powertrain, and it employs a dual fuzzy logical controller whose inputs are predictions for PHEV powertrain states. An elitist nondominant genetic algorithm using a model in loop simulation approach as fitness functions is implemented to multiobjective optimization for the EMS under worldwide light-duty test cycles. The optimal objectives are improving PHEV mileage, minimizing battery packs capacity fades, reducing HESS degradation inconsistency, and minimizing driving cost unit distance. A hardware in loop test bench has been established to verify EMS performances in embedded systems. The test results under new European driving cycles demonstrate that optimized EMSs remain appropriate for different driving cycles and their performances are close to dynamic programming based offline optimal solutions. Due to the contributions of both the HESS and the optimized EMS, the PHEV energy efficiency has been improved by 1.6%–2.5% and the PHEV energy storage system cycle life can be improved by 159%–203%.

Combined Prediction for Vehicle Speed with Fixed Route

Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles. Nowadays, people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning, but the prediction accuracy still needs to be improved. The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy; problems, such as over fitting, occur in the process of improving prediction accuracy. The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction. By combining the two prediction algorithms, the fusion of prediction performance is achieved, the limit of the single prediction performance is crossed, and the goal of improving vehicle speed prediction performance is achieved. In this paper, an extraction method suitable for fixed route vehicle speed is designed. The application of Markov and back propagation (BP) neural network in predictions is introduced. Three new combined prediction methods, all named Markov and BP Neural Network (MBNN) combined prediction algorithm, are proposed, which make full use of the advantages of Markov and BP neural network algorithms. Finally, the comparison among the prediction methods has been carried out. The results show that the three MBNN models have improved by about 19%, 28%, and 29% compared with the Markov prediction model, which has better performance in the single prediction models. Overall, the MBNN combined prediction models can improve the prediction accuracy by 25.3% on average, which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction

In the research regarding plug-in hybrid electric vehicle energy management strategies, the use of global positioning system and intelligent transportation system information to optimize control strategy will be the future trend, and this is relatively scarce in the existing researches. Therefore, an adaptive energy management strategy of plug-in hybrid electric vehicle based on trip characteristic prediction was investigated in this paper, and the main achievement is to suggest a way to determine the reference state of charge for control strategy using global positioning system or intelligent transportation system information. First, given the historical driving data of a driver by global positioning system, the important location points of the commuting routes were discovered. Second, a Markov trajectory prediction model based on the key points was established to predict and identify the driving routes. As such, the trip characteristics, such as information of mileage and driving cycles, were collected. Then, five typical driving cycles were extracted. According to the trip characteristic information, the optimal battery state of charge consumption regulation of plug-in hybrid electric vehicle was realized using a dynamic programming algorithm. This algorithm was applied to the research of state of charge trajectory planning algorithm. Moreover, an adaptive equivalent consumption minimization strategy based on state of charge planning trajectory was developed. The comparison of different control strategies proved that the developed strategy uses battery power reasonably and reduces fuel consumption of the vehicle.

Variability in Measured Real-World Operational Energy Use and Emission Rates of a Plug-In Hybrid Electric Vehicle

Compared to comparably sized conventional light duty gasoline vehicles (CLDGVs), plug-in hybrid electric vehicles (PHEVs) may offer benefits of improved energy economy, reduced emissions, and the flexibility to use electricity as an energy source. PHEVs operate in either charge depleting (CD) or charge sustaining (CS) mode; the engine has the ability to turn on and off; and the engine can have multiple cold starts. A method is demonstrated for quantifying the real-world activity, energy use, and emissions of PHEVs, taking into account these operational characteristics and differences in electricity generation resource mix. A 2013 Toyota Prius plug-in was measured using a portable emission measurement system. Vehicle specific power (VSP) based modal average energy use and emission rates are inferred to assess trends in energy use and emissions with respect to engine load and for comparisons of engine on versus engine off, and cold start versus hot stabilized running. The results show that, compared to CLDGVs, the PHEV operating in CD mode has improved energy efficiency and lower CO2, CO, HC, NOx, and PM2.5 emission rates for a wide range of power generation fuel mixes. However, PHEV energy use and emission rates are highly variable, with periods of relatively high on-road emission rates related to cold starts.

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

Energy management is a critical technology in plug-in hybrid-electric vehicles (PHEVs) for maximizing efficiency, fuel economy, and range, as well as reducing pollutant emissions. At the same time, deep reinforcement learning (DRL) has become an effective and important methodology to formulate model-free and realtime energy-management strategies for HEVs and PHEVs. In this article, we describe the energy-management issues of HEVs/PHEVs and summarize a variety of potential DRL applications for onboard energy management. In addition to the control objective and constraints, an elaborate model of the powertrain components is necessary as part of the solution. For example, the modeling of an engine involves the calculation of the fuel consumption, an estimate of efficiency, and the derivation of the torque and angular speed. Also, a computation of efficiency and an expression of the power balance are required for motor modeling. The transfer process of the speed and power from the motor/generator to the final drive is part of the transmission modeling.

Development of Global Optimization Algorithm for Series-Parallel PHEV Energy Management Strategy Based on Radau Pseudospectral Knotting Method

The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified.

Distributed Neuro-Dynamic Algorithm for Price-Based Game in Energy Consumption System

In this paper, a plug-in hybrid electric vehicles energy consumption system is studied. In order to protect each player’s privacy, the information exchange is going on the neighboring players, and a connected undirected graph is used to pattern the information flow between the players. Hence, it is impossible for each player to access the aggregate electricity consumption directly, which determines the electricity price. Under the noncooperative game frame, a distributed neuro-dynamic algorithm is proposed to optimize the benefit of each individual player base on the pricing strategies. A dynamic average consensus is applied to estimate the aggregate consumption and a projection neural network is employed to seek the Nash equilibrium point. The convergence of the proposed distributed algorithm is analyzed through the Lyapunov stability analysis. Finally, the effectiveness of the distributed neuro-dynamic algorithm is manifested in the simulation.

Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle

Vehicle energy economy is affected by different driving styles of individual drivers. To improve energy economy of plug-in hybrid electric vehicles (PHEVs), it is of great importance to develop the driving style adaptive optimal control strategy. In fact, driving styles are often influenced and restricted by different driving cycles. Therefore, to recognize driving style more accurately, this paper decouples driving styles from driving cycles. Based on classification and identification of driving cycles, the accelerator pedal opening and its change rate in different driving cycles are analyzed and the fuzzy-logic recognizer is built to identify driving styles. Afterwards, the driving style adaptive optimal control strategy is realized by combining the recognized driving style with the equivalent consumption minimization strategy (ECMS) and adopting a hybrid particle swarm optimization-genetic algorithm (PSO-GA) to optimize the relationship between the driving style and the equivalence factor (EF). The effectiveness of proposed driving style adaptive control strategy is validated by real vehicle test, which indicates that, compared with the original ECMS, the proposed driving style recognition based adaptive optimal control strategy improves the energy economy by 3.69% in the New European Driving Cycle (NEDC). This adaptive optimal strategy provides guidance for incorporating driving style into PHEV energy management strategy to improve fuel economy.

Deep reinforcement learning enabled self-learning control for energy efficient driving

To address the air pollution problems and reduce greenhouse gas emissions (GHG), plug-in hybrid electric vehicles (PHEV) have been developed to achieve higher fuel efficiency. The Energy Management System (EMS) is a very important component of a PHEV in achieving better fuel economy and it is a very active research area. So far, most of the existing EMS strategies are either just simply following predefined rules that are not adaptive to changing driving conditions; or heavily relying on accurate prediction of future traffic conditions. Deep learning algorithms have been successfully applied to many complex problems and proved to even outperform human beings in some tasks (e.g., play chess) in recent years, which shows the great potential of such methods in practical engineering problems. In this study, a deep reinforcement learning (Deep Q-network or DQN) based PHEV energy management system is designed to autonomously learn the optimal fuel/electricity splits from interactions between the vehicle and the traffic environment. It is a fully data-driven and self-learning model that does not rely on any prediction, predefined rules or even prior human knowledge. The experiment results show that the proposed model is capable of achieving 16.3% energy savings (with the designed PHEV simulation model) on a typical commute trip, compared to conventional binary control strategies. In addition, a dueling Deep Q-network with dueling structure (DDQN) is also implemented and compared with single DQN in particular with respect to the convergence rate in the training process.

Microscopic series plug-in hybrid electric vehicle energy consumption model: Model development and validation

Plug-in hybrid electric vehicles (PHEVs) significantly improve vehicle fuel efficiency compared to conventional internal combustion engine vehicles (ICEVs) and also eliminate the “range anxiety” associated with battery-only electric vehicles (BEVs). This paper develops a simple PHEV energy consumption model that can be used in a real-time in-vehicle and smartphone eco-driving application, an eco-routing navigation system, and/or a microscopic traffic simulation software. The majority of PHEV studies have centered on the evaluation of energy consumption to analyze vehicle control strategies or the behavior of the battery system assuming an average constant value for the regenerative braking energy efficiency or regenerative braking factors that are principally dependent on the vehicle’s average speed. The proposed series PHEV energy consumption model estimates a PHEV’s instantaneous energy consumption using second-by-second vehicle speed, acceleration, and roadway grade data as input variables accounting for the regenerative braking efficiency using instantaneous vehicle parameters. The model developed in this study computes the vehicle’s energy consumption producing an average error of 4% relative to independently collected field data. Results show that PHEVs can recover a higher amount of energy in urban driving when compared to high speed highway driving. Finally, it is important to highlight the fact that this model is flexible and general and thus can model different PHEVs as there is no need for efficiency maps for the electric motor or the internal combustion engine.