Power-split Hybrid Electric Vehicle Research Articles

Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles

Optimization studies for the energy management systems of hybrid electric powertrains have critical importance as an effective measure for vehicle manufacturers to reduce greenhouse gas emissions and fuel consumption due to increasingly stringent emission regulations in the automotive industry, strict fuel economy legislation, continuously rising oil prices, and increasing consumer awareness of global warming and environmental pollution. In this study, firstly, the mathematical model of the powertrain and the rule-based energy management system of the vehicle with a power-split hybrid electric vehicle configuration are developed in the Matlab/Simulink environment and verified with real test data from the vehicle dynamometer for the UDDS drive cycle. In this way, a realistic virtual test platform has been developed where the simulation results of the energy management systems based on discrete dynamic programming and Pontryagin’s minimum principle optimization can be used to train the artificial neural network-based energy management algorithms for hybrid electric vehicles. The average fuel consumption in relation to the break specific fuel consumption of the internal combustion engine and the total electrical energy consumption of the battery in relation to the operating efficiency of the electrical machines, obtained by comparing the simulation results at the initial battery charging conditions of the vehicle using different driving cycles, will be analyzed and the advantages of the different energy management techniques used will be evaluated.

Lithium-Plating Suppressed and Deep Deterministic Policy Gradient-Based Energy Management Strategy

Lithium plating and the formation of lithium dendrite are the main causes of battery internal short circuit and thermal runaway. This paper proposes a deep reinforcement learning-based energy management framework for power-split hybrid electric vehicles (HEVs) which effectively suppresses lithium plating inside batteries. First, single particle model with electrolyte (SPMe) and lithium-plating model are integrated into deep deterministic policy gradient based energy management strategy (DDPG-EMS) for the first time. Second, hybrid particle swarm optimization grey wolf optimizer (HPSOGWO) algorithm is applied to the parameter identification of the two models for the first time to ensure the optimal trade-off between convergence speed and identification accuracy. Third, the superiority of the control strategy is achieved by balancing among fuel consumption, lithium plating occurrence, and SOC sustainability effect under different initial cell temperatures, and verified by comparison with deep Q-network (DQN). The training and validation results both show that the proposed strategy with lithium plating considered not only slows down battery degradation by redressing the charging current in time before the negative overpotential drops to zero, but also reduces the fuel consumption cost as well as total cost.

Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete event system specification

Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete event system specification

Model predictive control for multimode power-split hybrid electric vehicles: Parametric internal model with integrated mode switch and variable meshing losses

Model predictive control (MPC) is one of the most promising energy management strategies for hybrid electric vehicles. However, owing to constructive complexity, the multimode power-split powertrain requires dedicated mathematical tools to model the mode switch and transmission power losses within the internal model of the controller. Thus, the transmission losses are usually neglected and the mode switch is optimised through offline simulations. This paper proposes an MPC internal model relying on a parametric approach available in the literature, which provides a unique formulation for modelling any power-split transmission and assesses the transmission meshing losses. The objectives, which cover a gap in the literature, are: 1) to integrate the discrete problem of the mode switch in a continuous formulation of the internal model; 2) to compare MPC internal models with different complexity, and evaluate how the consideration of meshing losses and efficiency of the electric machines affect the controller performance. The results on a case study vehicle, i.e., the Chevrolet Volt, suggest that a simplified internal model deteriorates the fuel consumption performance by less than 2 %, while the integrated mode switch is comparable to the offline strategy.

Dynamic Switching Control of Power-Split Hybrid Electric Vehicles Based on Time Delay Prediction and Interference Compensation

The deterioration of vehicle dynamics caused by the simultaneous action of time delay and interference is a key issue for the switching control system in a power-split hybrid electric vehicle (PS-HEV). This paper presents an innovative compound control method based on extended state observer (ESO) incorporating time delay during the mode transition process. First, the coupling effect of time-varying delay and external disturbance during the typical mode switching process is analyzed in depth, and the double deterioration mechanism of the two coupling effects on vehicle dynamics and ride comfort is revealed. To accurately estimate the system disturbance under the influence of time delay, an unconventional ESO is designed further by adopting the Markov chain time delay prediction model, and the main tool for interference observation is selected by comparing the practicability, reachable bandwidth and observation accuracy. Moreover, the stable mode switching performance and interference suppression are thoroughly investigated, and the smooth transmission of multi-power is realized through the multi-module fusion design of the compound coordinated controller. Finally, the hardware-in-the-loop test is implemented to validate the effectiveness of the control strategy, which also provides a novel idea and lays a theoretical foundation for the optimal design of the PS-HEV controller.

Topological Graph Representation and Configuration Synthesis for Power-Split Hybrid Transmissions of Multi-Planetary Gear Trains

Abstract Power-split hybrid transmissions are the core components of power-split hybrid electric vehicles (HEVs), and the quest for a more energy-efficient and higher-performing power-split hybrid transmission has long been the focus of study. In contrast to previously published methodologies, this paper proposes a novel approach for directly synthesizing power-split hybrid transmissions that makes use of the results of previously synthesized planetary gear trains (PGTs) rather than necessitating a resynthesis of their PGTs. A new topological graph that can construct a bridge between the PGTs and power-split hybrid transmission has been developed, reducing the computational complexity in the synthesis process. The new topological graph is obtained by adding topological characteristics of the power-split hybrid transmission to the PGT graph. A standard structure matrix is proposed to further screen out all the isomorphic configurations. The present method can generate various types of multi-PGT hybrid transmissions while avoiding mechanical structural interference. The design process of configurations for power-split hybrid transmission with three-column PGTs (3-PGT) is used as an example to prove the rationality of the method.

Safe Deep Reinforcement Learning-Based Constrained Optimal Control Scheme for HEV Energy Management

Considering physical constraints in online optimization and training safety is a challenge for implementation of deep reinforcement learning (DRL) algorithm. Especially for the non-linear system, the mapping relationship between output action of the agent and the control signals is difficult to obtain. This paper proposes a novel DRL framework for online optimization in energy management of a power-split hybrid electric vehicle (HEV) which combines a neural-network (NN)-based multi-constraints optimal strategy and a rule-based-restraints system (RBRS). The proposed method named Reward Directed Policy Optimization (RDPO) adopts exterior point method (EPM) and curriculum learning (CL) to direct the agent to recognize and avoid irrational control signals and optimize the fuel economy. The EMS considering fuel consumption minimization and irrational control signals avoidance is optimized by training the agent through WLTC. A competitive fuel economy, 4.495L/100km, is achieved with no irrational control signals. Based on the online adaptability evaluation conducted, the fuel consumption of the vehicle under NEDC and CTUDC has been reduced to 4.113L/100km and 3.221L/100km, respectively, with no irrational control signals. The superiority in optimization, calculation efficiency and safety is verified through the comparisons with various DRL agents.

Comparative study of hybrid architectures integrated with dual-fuel intelligent charge compression ignition engine: A commercial powertrain solution towards carbon neutrality

The dual-fuel intelligent charge compression ignition (ICCI) engine, equipped with double direct injection systems, can simultaneously improve fuel consumption and gaseous emissions by substituting low-carbon fuels for conventional fuels. This work investigates the optimal application of the ICCI engine on hybrid powertrains under various standard driving conditions. The purpose is to evaluate the profit of combining the advantages of hybrid powertrains with the advanced engine. Dynamic programming (DP) is applied to the energy management control, realizing the global optimization in the design phase. Three typical hybrid configurations are compared, including series (SHEV), parallel (PHEV), and power-split (PSHEV) hybrid electric vehicles. Except for the charging sustaining stage, the optimal state of charge trajectories are also researched to verify the reasonable parameters in the charging stage. Considering multiple objectives, a non-dominated sorting genetic algorithm (NSGA-II) is coupled to optimize fuel consumption, gaseous emissions, and battery cost. The ICCI engine is mapped by the test bench results, while the involved hybrid configurations are modeled in Matlab/Simulink. The results illustrate that PSHEV achieves the best fuel economy and emission purification among the involved structures. With the ideal solution provided by DP, fuel consumption and gaseous emissions for PSHEV are reduced by 10% and 50%, and the low-carbon fuel utilization ratio has tripled, contributing to carbon neutrality. The engine was operated in several working conditions for SHEV, achieving the cleanest gaseous emissions. For PHEV, the engine performance was rarely improved compared with the engine without the assistance of hybrid systems. The optimization provided by NSGA-II indicates that the minimum values of multiple objectives cannot be reached simultaneously. A further 50% reduction in emissions would cause fuel consumption to double the original.

Energy Management Strategy Based on Deep Reinforcement Learning and Speed Prediction for Power‐Split Hybrid Electric Vehicle with Multidimensional Continuous Control

An efficient energy management strategy (EMS) is significant to improve the economy of hybrid electric vehicles (HEVs). Herein, a power‐split HEV model is built and validated against test results, and then the EMS is proposed for this model based on vehicle speed prediction and deep reinforcement learning (DRL) algorithms. The rule‐based local controller and global optimal empirical knowledge are introduced to enhance the convergence speed. It is shown in the results that the twin delayed deep deterministic policy gradient algorithm (TD3) achieves more satisfactory performance on converge speed and energy efficiency. The networks of the DRL algorithm with continuous control update more robustly during iterations, in contrast to the discrete ones. Although the power‐split HEV with lower control dimension can reduce the learning burden for DRL EMS; however, the multidimensional control space shows greater optimization potential. As a result, the equivalent fuel consumption of TD3‐based EMS with multidimensional continuous control differences from the global optimal algorithm only by 4.92%. Herein, it is demonstrated in the results that long short‐term memory recurrent neural network (LSTM RNN) performs better for vehicle speed prediction than classical RNN and BP neural network, and the predictive vehicle speed feature helps improve fuel economy by 0.55%.

An apprenticeship-reinforcement learning scheme based on expert demonstrations for energy management strategy of hybrid electric vehicles

Deep reinforcement learning (DRL) is a potential solution to develop efficient energy management strategies (EMS) for hybrid electric vehicles (HEV) that can adapt to the changing topology of electrified powertrains and the uncertainty of various driving scenarios. However, traditional DRL has many disadvantages, such as low efficiency and poor stability. This study proposes an apprenticeship-reinforcement learning (A-RL) framework based on expert demonstration (ED) model embedding to improve DRL. First, the demonstration data, calculated by dynamic programming (DP), were collected, and domain adaptive meta-learning (DAML) was used to train the ED model with the adaptive capability of working conditions. Then combined apprenticeship learning (AL) with DRL, and the ED model was used to guide the DRL to output action. The method was validated on three HEV models, and the results show that the training convergence rate increases significantly under the framework. The average increase that the apprenticeship-deep deterministic policy gradient (A-DDPG) based method applied to three HEVs achieved was 34.9 %. Apprenticeship-twin delayed twin delayed deep deterministic policy gradient (A-TD3) achieved 23 % acceleration in the power-split HEV. Because A-DDPG's EMS is more forward-looking and can mimic ED to some extent, the frequency of engine operation in the high-efficiency range has increased. Therefore, A-DDPG can improve the fuel economy of the series hybrid electric bus (HEB) by 0.2–2.7 %, and improvements averaged to about 9.6 % in the series–parallel HEV while maintaining the final SOC. This study aims to improve the sampling efficiency and optimal performance of EMS-based DRL and provide a basis for the design and development of vehicle energy saving and emission reduction.

Economic Payback Time of Battery Pack Replacement for Hybrid and Plug-In Hybrid Electric Vehicles

This article investigates the economic viability of replacing the high-voltage battery pack of a power-split hybrid electric vehicle (HEV) and a plug-in hybrid electric vehicle (PHEV) by estimating the impact of battery aging on the fuel economy, drivability capability, and electric range. The HEV is modeled first, an optimal energy management strategy based on dynamic programming is then implemented, and experimental characterization data for the battery cell is presented. The batteries are tested to a heavily aged state, with up to an 84% loss of capacity. The battery pack payback period is estimated by assessing the vehicle operative costs in terms of fuel and electricity as obtained through numerical simulations as a function of battery aging. Replacing the battery pack at the conventional end-of-life limit of 80% residual capacity is suggested not to be convenient from an economic standpoint for both the HEV and the PHEV. On the other hand, acceptable payback periods (i.e., 2–5 years) can be achieved for the battery pack when being replaced at 20% to 40% residual capacity. The proposed methodology can be implemented to advise an HEV or PHEV user regarding the benefit of replacing the battery pack due to excessive aging.

Naturalistic data-driven and emission reduction-conscious energy management for hybrid electric vehicle based on improved soft actor-critic algorithm

Energy management strategies (EMSs) are critical to saving fuel and reducing emissions for hybrid electric vehicles (HEVs). Given that, this article proposes a naturalistic data-driven and emission reduction-conscious EMS based on deep reinforcement learning (DRL) for a power-split HEV. In this article, for the purpose of evaluating the practical fuel economy of an HEV driving in a certain city region, a specific driving cycle is constructed by using a naturalistic data-driven method. Furthermore, to realize the multi-objective optimization in terms of fuel conservation and emission reduction as well as the state of charge (SOC) sustaining, an intelligent EMS based on the improved soft actor-critic (SAC) algorithm with a novel experience replay method is innovatively proposed. Finally, the effectiveness and optimality of the proposed EMS are verified. Simulation results indicate that the constructed driving cycle can effectively reflect the real traffic scenarios of the test region. Moreover, the proposed EMS achieves 95.25% fuel economy performance of the global optimum, improving the fuel economy by 5.29% and reducing the emissions by 10.42% compared with the emission reduction-neglecting EMS based on standard SAC. This article contributes to energy conservation and emission reduction for the transportation industry through advanced DRL methods.

Predictive equivalent consumption minimization strategy for power-split hybrid electric vehicles using Monte Carlo algorithm

Bu çalışma, güç paylaşımlı bir hibrit elektrikli araç (HEA) için, Monte Carlo (MC) algoritmasına dayalı olarak tahmin edilen sürüş çevrimi hızlarını kullanan öngörülü eşdeğer tüketim minimizasyonu stratejisi (Ö-ETMS) önermektedir. Önerilen Ö-ETMS, enerji kaynakları arasındaki güç dağılımını en iyi şekilde belirlemek için MC algoritması tarafından tahmin edilen hız profillerinden tam olarak yararlanmaktadır. Bu çalışmada; MC tabanlı Ö-ETMS metodunu doğrulamak için, New European Driving Cycle (NEDC), Worldwide Harmonised Light Vehicles Test Procedure (WLTP), Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), New York City Cycle (NYCC), California Unified Cycle (LA-92) ve tüm döngülerin kombinasyonu (ALL-CYC) çevrimleri kullanılmış; toplam yedi tekrarlı sürüş döngüsü altında bir dizi simülasyon çalışması gerçekleştirilmiştir. MC tabanlı Ö-ETMS stratejisi, standart ETMS ile karşılaştırılmıştır. NEDC çevriminde %6,01, WLTP çevriminde %9,09, UDDS çevriminde %6,33, HWFET çevriminde %5,14, NYCC çevriminde %1,96, LA-92 çevriminde %11,47 ve ALL-CYC çevriminde %7,92 oranla yakıt tasarrufu elde edilmiştir. Bu makaledeki sonuçlar, önerilen stratejinin yaygın olarak kullanılan temel yönteme kıyasla, rekabetçi bir yakıt tasarrufu sağladığını ortaya koymaktadır.

System-Level Energy Management Optimization of Power-Split Hybrid Electric Vehicle Based on Nested Design

System-Level Energy Management Optimization of Power-Split Hybrid Electric Vehicle Based on Nested Design

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.

Predictive energy management with engine switching control for hybrid electric vehicle via ADMM

This paper studies energy management (EM) of a power-split hybrid electric vehicle (HEV) equipped with planetary gear sets. We first formulate a mixed-integer global optimal control problem that includes a binary switching variable. Convex modeling, including the fuel model for a compound energy conversion unit, is then presented to reformulate the mixed-integer EM as a two-step program. For optimizing the engine switching and battery power decisions in the first step, we employ the alternating direction method of multipliers (ADMM) algorithm where the solution of the convex relaxation is used to initialize the non-convex problem. On the standard driving cycle, simulation results indicate that the ADMM based EM method saves 7.63% fuel compared to a heuristic method, and shows 99% optimality compared to a dynamic programming method, while saving three orders of magnitude in computing time. An ADMM combined model predictive control (ADMM-MPC) method is also developed that is suitable for receding horizon control scenarios. The ADMM-MPC method shows 5.28% fuel saving when implemented using a prediction horizon of 15 samples. Meanwhile, the mean computing time for MPC updates is 3.53ms. Our results demonstrate that the proposed ADMM is capable of real-time control.

ADAPTIVE ENERGY MANAGEMENT STRATEGY FOR SUSTAINABLE OPERATION OF HYBRID ELECTRIC VEHICLE

Hybrid electric vehicles (HEVs) in the transportation sector is spearheading the industry towards embracing green technology to ensure the sustainability of the environment. HEVs offer significant reduction of exhaust emissions while retaining vehicle performance achieved via a revolutionary energy management strategy (EMS) which reforms the management of power flow from the dual energy sources of a HEV. However, researchers are faced with challenges to extract the maximum performance out of HEVs due to the contradicting nature between its main objectives, namely vehicle performance, fuel consumption and cost. In this study, an investigation focusing on the fuel-saving potential of a power-split type HEV using a fuzzy logic-based EMS is conducted. The purpose of this research is to explore methods to improve fuel efficiency of a HEV through a smart and adaptive EMS. The power flow in the proposed model is decided based on its current vehicle speed and the global discharge rate value derived from the real-time battery state-of-charge and remaining trip distance. From simulations over standard drive cycles, the proposed controller is able to outperform a rule-based EMS by an improvement of up to 65.4% in terms of fuel consumption which subsequently reduces the volume of pollutants released to the atmosphere.

Fuel Economy Improvement Analysis of Parallel and Power-Split Hybrid Electric Vehicles

Fuel Economy Improvement Analysis of Parallel and Power-Split Hybrid Electric Vehicles

Generation, Screening, and Optimization of Powertrain Configurations for Power-Split Hybrid Electric Vehicle: A Comprehensive Overview

Power-split hybrid electric vehicle (PSHEV) has outstanding energy-saving potential, which is one of the effective solutions to alleviate energy shortage and environmental pollution. The powertrain configuration of PSHEV is the basis of energy management, which determines the operating mode, control algorithm selection, optimization potential, and directly affects the vehicle’s comprehensive performance. Analyzing and summarizing the existing research results will be beneficial to promote the further development of the design theory of powertrain. In this article, the generation, screening, and optimization techniques of PSHEV powertrain configuration are systematically compared and analyzed. First, the classification and modeling of PSHEV are analyzed and summarized. Second, the generation method of the PSHEV powertrain is analyzed in detail. Thus, three types of formation methods for the design space of PSHEV powertrain configuration are summarized. Then, the feasible solution is explored via configurations screening, which further reduces the difficulty of industrial application. Meanwhile, the feasible design space is further optimized to obtain the competitive solution, so as to meet the design requirements. Finally, the technology challenges of PSHEV powertrain configuration research are discussed, which pointed out the application direction of configuration design method in electric vehicles and fuel cell vehicles.

Battery Health-Aware and Deep Reinforcement Learning-Based Energy Management for Naturalistic Data-Driven Driving Scenarios

This article proposes a battery health-aware and deep reinforcement learning (DRL)-based energy management framework for power-split hybrid electric vehicles (HEVs) in a naturalistic driving scenario. First, based on the data collected from the actual traffic flow, a data-driven method is used to establish driving scenarios that reflect different driving patterns and behaviors. Second, the expert knowledge is embedded into the deep deterministic policy gradient (DDPG) to achieve faster convergence with the guaranteed vehicle performance. Third, the superiority of the control strategy is achieved by optimizing the tradeoff among fuel consumption, battery aging cost, and state of charge (SoC) sustainability penalty under different weight coefficients, and verified by comparison with the existing state-of-the-art strategies including the deep Q-network (DQN) and dynamic programing (DP). The results show that the proposed strategy can slow down battery aging by lowering the operating severity factor with minimal fuel economy penalty while remaining accelerated iterative convergence compared with DQN. The benefits of proposed strategy become very evident when the vehicle is driving under the high power demand and it has good stability to cope with the change of operating conditions.