Plug-in Hybrid Electric Vehicles Energy Management Research Articles

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.

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.

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.

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.

Battery‐lifetime‐conscious energy management strategy based on SP‐SDP for commuter plug‐in hybrid electric vehicles

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. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Comparative study of different types of PHEV optimal control strategies in real-world conditions

With increasingly serious global environmental issues and energy shortages, energy conservation in transportation has become a significant, fundamental objective. The objective of the current research is to investigate the impacts of different types of optimal control strategies on the plug-in hybrid electric vehicle (PHEV) performance in real-world conditions. The optimal control strategies according to Pontryagin’s minimum principle (PMP) and optimized rule-based approaches are developed for the optimal pattern of a PHEV energy management system to reduce fuel consumption and emissions simultaneously, without sacrificing the vehicle performance. For this purpose, first, using test data for engine and battery, an experimental map-based model of the parallel PHEV is developed. Then, the powertrain components are sized by using a genetic algorithm (GA), over the real-world driving cycles. Subsequently, GA-fuzzy and PMP controllers are developed for energy management of the PHEV. Simulation results show the significant effectiveness of the proposed optimal control approaches on the fuel consumption and emissions reduction in various driving cycles. The convergence speed and global searching ability of PMP are significantly better than GA-fuzzy for the design of control strategy parameters. The sensitivity of battery initial state of charge, driving cycle, and road grade are analyzed on vehicle emissions and fuel consumption. The findings reveal that PMP could be adapted to different conditions by tuning co-state in a short time. This advantage makes it more adaptable to variation of real-world conditions. On the other hand, a fuzzy controller needs less computational effort and so is more appropriate for a certain condition.

Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.

Mode Integration Algorithm Based Plug-In Hybrid Electric Vehicle Energy Management Strategy Research

One of the reasons which cause poor fuel consumption of plug-in hybrid electric vehicle (PHEV) is that working modes switch frequently. In order to solve this problem, the single-shaft PHEV and fuel consumption are regarded as research object and research goal, respectively. The method which integrated many modes into one module is used to improve fuel economy of PHEV. And this paper proposed mode integration algorithm based PHEV energy management strategy. Simulation results indicate that this paper proposed mode integration algorithm based energy management strategy can improve fuel economy largely and it's meaningful for researching and development of PHEV.

Long-term assessment of economic plug-in hybrid electric vehicle battery lifetime degradation management through near optimal fuel cell load sharing

This work evaluates the performance of a plug-in hybrid electric vehicle (PHEV) energy management process that relies on the active management of the degradation of its energy carriers – in this scenario, a lithium-ion battery pack and a polymer electrolyte membrane fuel cell (PEMFC) – to produce a near economically-optimal vehicle operating profile over its entire useful lifetime. This solution is obtained through experimentally-supported PHEV models exploited by an optimal discrete dynamic programming (DDP) algorithm designed to efficiently process vehicle usage cycles over an extended timescale. Our results demonstrate the economic and component lifetime gains afforded by our strategy when compared with alternative rule-based PHEV energy management benchmarks.

Battery Sizing for Plug-in Hybrid Electric Vehicles in Beijing: A TCO Model Based Analysis

This paper proposes a total cost of ownership (TCO) model for battery sizing of plug-in hybrid electric vehicles (PHEVs). The proposed systematic TCO model innovatively integrates the Beijing driving database and optimal PHEV energy management strategies developed earlier. The TCO, including battery, fuel, electricity, and salvage costs, is calculated in yearly cash flows. The salvage cost, based on battery degradation model, is proposed for the first time. The results show that the optimal battery size for PHEVs in Beijing is 6–8 kWh. Several additional scenarios are also analyzed: (1) 10% increase in battery price or discount rate leads to an optimal battery size of 6 kWh, and 10% increase in fuel price shifts the optimal battery size to 8 kWh; (2) the longer and more dispersive daily range distribution in the U.S. increases the optimal battery size to 14 kWh; (3) the subsidy in China results in an optimal battery size of 13 kWh, while that in the U.S. results in 17 kWh, and a fuel savings rate based subsidy policy is innovatively proposed; (4) the optimal battery size with Li4Ti5O12 batteries is 2 kWh, but the TCO of Li4Ti5O12 batteries is higher than that of LiFePO4 batteries.

Trip-Oriented Energy Management Control Strategy for Plug-In Hybrid Electric Vehicles

This paper presents a trip-oriented energy management control strategy for plug-in hybrid electric vehicle (PHEV). The proposed strategy provides system optimization and control methods to improve real-world fuel economy (FE) by optimizing the power demand distribution between fuel and battery electricity and the power delivery split between the mechanical and electrical paths in a PowerSplit PHEV architecture. A two degree of freedom system model is established to characterize the actuation dynamics and the power delivery properties of the powertrain. This paper achieves three important contributions to PHEV energy management control research: 1) the optimal control problem is solved considering both the nonlinearity of battery efficiency and the complexity of PowerSplit architecture; 2) a novel trip-oriented energy consumption preplanning method is proposed using a driving pattern-based dynamic programming approach; and 3) a feedback control system is designed to realize the optimal energy consumption process in real applications. The proposed energy management control strategy has been shown to improve FE in Ford Escape PHEVs.

Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles

Plug-in hybrid electric vehicles (PHEVs) are currently recognized as a promising solution for reducing fuel consumption and emissions due to the ability of storing energy through direct connection to the electric grid. Such benefits can be achieved only with a supervisory energy management strategy that optimizes the energy utilization of the vehicle. This control problem is particularly challenging for PHEVs due to the possibility of depleting the battery during usage and the vehicle-to-grid interaction during recharge. This paper proposes a model-based control approach for PHEV energy management that is based on minimizing the overall CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions produced-directly and indirectly-from vehicle utilization. A supervisory energy manager is formulated as a global optimal control problem and then cast into a local problem by applying the Pontryagin's minimum principle. The proposed controller is implemented in an energy-based simulator of a prototype PHEV and validated on experimental data. A simulation study is conducted to calibrate the control parameters and to investigate the influence of vehicle usage conditions, environmental factors, and geographic scenarios on the PHEV performance using a large database of regulatory and “real-world” driving profiles.