Efficiency Of Hybrid Electric Vehicle Research Articles

A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon

Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%.

A Hybrid Algorithm Combining Data-Driven and Simulation-Based Reinforcement Learning Approaches to Energy Management of Hybrid Electric Vehicles

Energy management strategy (EMS) is one of the key technologies that improves the fuel efficiency of hybrid electric vehicles (HEVs) by governing the energy flow between the fuel tank and the electric energy storage. With the rapid development of artificial intelligence especially after the great success of AlphaGo, reinforcement learning (RL) has opened up a new window for EMS. Although many RL-based solutions have been successfully applied to EMS tasks, most current RL-based approaches only consider RL as an offline optimization tool, i.e., RL is used to solve optimization problems given a simulation model. This method may be utilized to realize optimal control in simulation, but there is no guarantee that a well-behaved performance can also be achieved in the real-physical world due to the simulation-to-real gap. Meanwhile, for many industrial EMS tasks especially when optimizing an existing controller, a high-fidelity simulator may be unavailable, but some logging data generated by some suboptimal existing controller and simple models of the principal powertrain components neglecting detailed component dynamics could exist. In this context, a hybrid algorithm combining data-driven and simulation-based RL approach is proposed that tries to learn experience from both the real logging data and simulated simple model. Based on the hardware-in-the-loop (HIL) results, a near optimal policy can be obtained (fuel consumption is decreased by approximately 6.10%) by this hybrid algorithm from a small batch logging data and a simple simulated model when compared with the dynamic programming (DP) method using a high-fidelity simulation model mimicking the real-physical world.

A Comparative Study of Vehicle Velocity Prediction for Hybrid Electric Vehicles Based on a Neural Network

Vehicle velocity prediction (VVP) plays a pivotal role in determining the power demand of hybrid electric vehicles, which is crucial for establishing effective energy management strategies and, subsequently, improving the fuel economy. Neural networks (NNs) have emerged as a powerful tool for VVP, due to their robustness and non-linear mapping capabilities. This paper describes a comprehensive exploration of NN-based VVP methods employing both qualitative theory analysis and quantitative numerical simulations. The used methodology involved the extraction of key feature parameters for model inputs through the utilization of Pearson correlation coefficients and the random forest (RF) method. Subsequently, three distinct NN-based VVP models were constructed comprising the following: a backpropagation neural network (BPNN) model, a long short-term memory (LSTM) model, and a generative pre-training (GPT) model. Simulation experiments were conducted to investigate various factors, such as the feature parameters, sliding window length, and prediction horizon, and the prediction accuracy and computation time were identified as key performance metrics for VVP. Finally, the relationship between the model inputs and velocity prediction performance was revealed through various comparative analyses. This study not only facilitated the identification of an optimal NN model configuration to balance prediction accuracy and computation time, but also serves as a foundational step toward enhancing the energy efficiency of hybrid electric vehicles.

A Data-Driven Solution for Energy Management Strategy of Hybrid Electric Vehicles Based on Uncertainty-Aware Model-Based Offline Reinforcement Learning

Energy management strategy (EMS) is the key technology to improving the fuel efficiency of hybrid electric vehicles (HEV). In recent years, the development of artificial intelligence has enabled tremendous advances by utilizing reinforcement learning (RL) for training and deploying deep neural network-based EMS. However, in contrast to the fields of deep learning such as computer vision and natural language processing which mainly rely on large-scale offline datasets, most RL-based policies must be trained online by trial-and-error with the initial performance being almost arbitrary. Such a paradigm is considered inefficient and unsafe for industrial automation and can only be used to tackle the EMS problems in the simulation world. Considering large historical interactive datasets are readily available in EMS domain, if a RL algorithm can be used to extract a policy purely offline from the prior collected dataset and improve upon data logging policy, the current issues including sample inefficiency, unsafe exploration, and simulation-to-real gap that prevent the widespread use of RL methods could be mitigated to a great extent. To this end, this paper presents a feasible algorithmic framework for model-based offline RL. Unlike vanilla RL approaches without any consideration against distributional shift, a data-driven dynamic model is built before the policy training using RL. After that, two techniques namely conservative MDP and state regularization are augmented which is proved to be effective against model over exploitation. A hardware-in-the-loop (HIL) test is used to evaluate the real-time performance and effectiveness of different strategies on a real controller, and it is found that the proposed naïve data-driven model-based offline RL algorithm can already solve the distributional shift problem inherent in vanilla RL to a large extent. Furthermore, by incorporating the guidance of uncertainty-awareness, a near optimal policy can be obtained by using only the dataset from a sub-optimal controller.

Hybrid Propulsion Efficiency Increment through Exhaust Energy Recovery—Part 2: Numerical Simulation Results

The efficiency of hybrid electric vehicles may be substantially increased if the energy of exhaust gases, which do not complete the expansion inside the cylinder of the internal combustion engine, is efficiently recovered using a properly designed turbo-generator and employed for vehicle propulsion. Previous studies, carried out by the same authors of this work, showed a potential hybrid vehicle fuel efficiency increment up to 15% employing a 20 kW turbine on a 100 HP-rated power thermal unit. The innovative thermal unit proposed here is composed of a supercharged engine endowed with a properly designed turbo-generator, which comprises two fundamental elements: an exhaust gas turbine expressly designed and optimized for the application, and a suitable electric generator necessary to convert the recovered energy into electric energy, which can be stored in the on-board energy storage system of the vehicle. In this two-part work, the realistic efficiency of the innovative thermal unit for hybrid vehicles is evaluated and compared to a traditional turbocharged engine. In Part 1, the authors presented a model for the prediction of the efficiency of a dedicated radial turbine, based on a simple but effective mean-line approach; the same paper also reports a design algorithm, which, thanks to some assumptions and approximations, allows fast determination of the right turbine geometry for a given design operating condition. It is worth pointing out that, being optimized for quasi-steady power production, the exhaust gas turbine here considered is quite different from the ones commonly employed for turbocharging applications; for this reason, and in consideration of the required power size, such a turbine is not available on the market, nor has its development been previously carried out in the scientific literature. In this paper, Part 2, a radial turbine geometry is defined for the thermal unit previously calculated, employing the design algorithm described in Part 1; the realistic energetic advantages that could be achieved by the implementation of the turbo-generator on a hybrid propulsion system are evaluated through the performance prediction model under different operating conditions of the thermal unit. As an overall result, it was estimated that, compared to a reference traditional turbocharged engine, the turbo-compound system could gain vehicle efficiency improvement between 3.1% and 17.9%, according to the output power delivered, with an average efficiency increment of 10.9% evaluated on the whole operating range.

Hybrid Propulsion Efficiency Increment through Exhaust Energy Recovery—Part 1: Radial Turbine Modelling and Design

The efficiency of Hybrid Electric Vehicles (HEVs) may be substantially increased if the energy of the exhaust gases, which do not complete the expansion inside the cylinder of the internal combustion engine, is efficiently recovered by means of a properly designed turbogenerator and employed for vehicle propulsion; previous studies, carried out by the same authors of this work, showed a potential hybrid vehicle fuel efficiency increment up to 15% by employing a 20 kW turbine on a 100 HP rated power thermal unit. The innovative thermal unit here proposed is composed of a supercharged engine endowed with a properly designed turbogenerator, which comprises two fundamental elements: an exhaust gas turbine expressly designed and optimized for the application, and a suitable electric generator necessary to convert the recovered energy into electric energy, which can be stored in the on-board energy storage system of the vehicle. In these two parts, the realistic efficiency of the innovative thermal unit for hybrid vehicle is evaluated and compared to a traditional turbocharged engine. In Part 1, the authors present a model for the prediction of the efficiency of a dedicated radial turbine, based on a simple but effective mean-line approach; the same paper also reports a design algorithm, which, owing to some assumptions and approximations, allows a fast determination of the proper turbine geometry for a given design operating condition. It is worth pointing out that, being optimized for quasi-steady power production, the exhaust gas turbine considered is quite different from the ones commonly employed for turbocharging application; for this reason, and in consideration of the required power size, such a turbine is not available on the market, nor has its development been previously carried out in the scientific literature. In the Part 2 paper, a radial turbine geometry is defined for the thermal unit previously calculated, employing the design algorithm described in Part 1; the realistic energetic advantage that could be achieved by the implementation of the turbogenerator on a hybrid propulsion system is evaluated through the performance prediction model under the different operating conditions of the thermal unit. As an overall result, it was estimated that, compared to a reference traditional turbocharged engine, the turbocompound system could gain vehicle efficiency improvement between 3.1% and 17.9%, depending on the output power level, while an average efficiency increment of 10.9% was determined for the whole operating range.

A comparative study of 13 deep reinforcement learning based energy management methods for a hybrid electric vehicle

Energy management strategy (EMS) has a huge impact on the energy efficiency of hybrid electric vehicles (HEVs). Recently, fast-growing number of studies have applied different deep reinforcement learning (DRL) based EMS for HEVs. However, a unified performance review benchmark is lacking for most popular DRL algorithms. In this study, 13 popular DRL algorithms are applied as HEV EMSs. The reward performance, computation cost, and learning convergence of different DRL algorithms are discussed. In addition, HEV environments are modified to fit both discrete and continuous action spaces. The results show that the stability of agent during the learning process of continuous action space is more stable than discrete action space. In the continuous action space, SAC has the highest reward, and PPO has the lowest time cost. In discrete action space, DQN has the lowest time cost, and FQF has the highest reward. The comparison among SAC, FQF, rule-based, and equivalent consumption minimization strategies (ECMS) shows that DRL EMSs run the engine more efficiently, thus saving fuel consumption. The fuel consumption of FQF is 10.26% and 5.34% less than Rule-based and ECMS, respectively. The contribution of this paper will speed up the application of DRL algorithms in the HEV EMS application.

A New HEV Power Distribution Algorithm Using Nonlinear Programming

An equivalent consumption minimization strategy (ECMS) is one of the most powerful and practical ways to improve the fuel efficiency of hybrid electric vehicles (HEVs). In an ECMS, it is important to determine the optimal equivalent factor to reach a global optimal solution. The optimal equivalent factor is determined by driving conditions. Previous studies have used an adaptive ECMS (A-ECMS) to determine the appropriate equivalent factor according to changing driving conditions. An A-ECMS adjusts the equivalent factor by controlling the battery’s state of charge (SOC) to follow a reference SOC trajectory. It is therefore critical to identify a reference SOC trajectory that reflects real-world driving conditions. These conditions, which are composed of the HEV’s nonlinear dynamics and complex constraints, can be formulated into a nonlinear optimal control problem (NOCP). Here, we propose applying nonlinear programming (NLP) to an A-ECMS. The NLP-based ECMS algorithm can be divided into two parts: the use of an NLP to solve an NOCP to obtain the reference SOC trajectory and the application of an NLP solution (the result of the first part) to an A-ECMS. Simulation results demonstrate that the proposed NLP-based ECMS closely resembles a global optimal solution for dynamic programming in a relatively brief calculation time.

A Rule-Based Control Strategy of Driver Demand to Enhance Energy Efficiency of Hybrid Electric Vehicles

In recent years, hybrid electric vehicles (HEVs) have increased significantly due to climate change and the demand for high-efficiency power sources. HEVs that combine an internal combustion engine (ICE) and an electric motor (EM) can improve the power output of the ICE and overcome the challenges of the insufficient battery life of electric vehicles. In this study, a parallel HEV with a power distribution mechanism is developed for energy saving and carbon reduction. The driver′s power demands are used as input sources, and a rule-based control strategy is used to determine the power distribution of the generator, EM, and ICE. The NEDC2000 driving cycle is used as the test benchmark to demonstrate the performance of the HEV. In comparison to ICE vehicles, the fuel efficiency of HEVs significantly improved. In addition, other parameters, including the average brake-specific fuel consumption (BSFC), brake-specific carbon monoxide emission (BSCO), and brake-specific hydrocarbons (BSHCs), were lower, which can effectively save fossil fuel and reduce air pollution.

The application of machine learning based energy management strategy in multi-mode plug-in hybrid electric vehicle, part I: Twin Delayed Deep Deterministic Policy Gradient algorithm design for hybrid mode

As the performance of Energy Management Strategy (EMS) is crucial for the energy efficiency of Hybrid Electric Vehicles (HEVs), a Deep Reinforcement Learning (DRL)-based algorithm, namely Twin Delayed Deep Deterministic Policy Gradient (TD3), is adopted to design EMS for the power Charge-Sustained (CS) stage of a multi-mode plug-in Hybrid Electric Vehicle (HEV). In addition, EMS is improved by combining the actor-network of TD3 with Gumbel-Softmax to realize mode selection and torque distribution simultaneously, which is a discrete (mode)-continuous (engine speed) hybrid action space and not applicable in original TD3. To reduce the unreasonable exploration of agents in discrete action, a rule-based mode control mechanism (RBMCM) is designed and involved in EMS. The improved algorithm speeds up the learning process and achieves better fuel economy. Simulation results show that the gap between the proposed strategy and the benchmark dynamic programming (DP) is reduced to 2.55% in the selected training cycle. Regarding the unknown testing cycles, the fuel economy of agents trained by the improved method overperforms traditional DRL-based EMS when it reaches more than 90% of the DP-based benchmarking. In conclusion, the proposed method provides a theoretical foundation for the solution of the hybrid space optimization problem for hybrid systems.

Road Test of Selected Electrical Parameters of the Hybrid Vehicle Accumulation System

Increasing energy efficiency in hybrid electric vehicles is associated with the possibility of recovering some of the kinetic energy in the delayed movement phase and converting it to electricity, which can be used to charge batteries. It is structurally possible to implement by using an electric machine operating as a generator and an energy accumulation system. In addition to the widely used lithium-ion energy storage systems, nickel-metal hydride energy accumulation solutions are also an alternative. Battery systems are characterized by specific electrical parameters which include, among others: electrical capacity, open circuit voltage (cells), discharge/charge current, battery state of charge or power of the energy storage system. The article was aimed at determining these parameters of the nickel-metal hydride energy storage system. The actual characteristics of the above-mentioned electric battery indicators were obtained through the measurements carried out during road and bench tests with the use of research apparatus and research object (Toyota Prius the third generation). The tests showed that the battery voltage varied slightly from 203.42 … 203.98 V (cell voltage −14.49 … 14.57 V, link voltage −1.211 … 1.214 V). On the other hand, the values of the charge/discharge current were in a wide range (−87.57 … 132.74 A) and depended on road conditions. This parameter was largely dependent on the torque balance of electric machines (generator, electric motor), but it also had an impact on the battery state of charge and power. To determine the parameters, an electric model of the Three RC Network Model, powered from an open-circuit voltage source and consisted of three resistors and three capacitors, was used. This electric model was characterized by high accuracy (up to 2.93%) and described the behavior of the nickel-metal hydride system of energy accumulation in real conditions in a good way.

Real-Time Energy Management Strategy Based on Driver-Action-Impact MPC for Series Hybrid Electric Vehicles

Precise prediction of future vehicle information can improve the control efficiency of hybrid electric vehicles. Nowadays, most prediction models use previous information of vehicles to predict future driving velocity, which cannot reflect the impact of the driver and the environment. In this paper, a real-time energy management strategy (EMS) based on driver-action-impact MPC is proposed for series hybrid electric vehicles. The proposed EMS consists of two modules: the velocity prediction module and the real-time MPC module. In the velocity prediction module, a long short-term memory (LSTM) neural network model, which is trained by the traffic data derived from a VR-based driving simulator, is adopted to predict the future driving information by using driver action information and current vehicle’s velocity. The obtained future driving velocity is treated as the inputs of the real-time MPC module, which outputs the control variables to act on the underlying controllers of power components by solving a standard quadratic programming (QP) problem. Compared with the rule-based strategy, a 5.6% average reduction of fuel consumption is obtained. The effectiveness of real-time computation of the EMS is validated and verified through a hardware-in-the-loop test platform.

Improving Efficiency of Hybrid Electric Vehicle Using Matrix Converters

The paper deals with a novel enhanced connection of AC/AC powertrain for HEV hybrid vehicles. The substantial contribution of such a connection is the absence of 4QC auxiliary converter needed for autonomous and hybrid operational modes and its compensation by power-lesser 0×5 matrix converter. The main advantages of a simplified connection are beside smaller auxiliary converter sizing and possible better efficiency of the HEV powertrain. So, powertrain operation in autonomous traction accu-battery modes uses direct 0×5 configuration of traction 3×5 MxC matrix converter and in hybrid modes of ICE engine and accu-battery, besides traction 3×5 MxC matrix converter, uses the auxiliary 0×5 matrix converter. Modeling and simulation using Matlab-Simulink environment of traction powertrain configuration in autonomous modes are presented in the paper, as well as all simulation experiment results.

Adaptive Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Pontryagin’s Minimum Principle Based on Daily Driving Patterns

Optimal control ideas based on Pontryagin’s Minimum Principle (PMP) have become mature techniques for maximizing the fuel efficiency of Hybrid Electric Vehicles (HEVs) and Plug-in Hybrid Electric Vehicles (PHEVs). The outstanding performance of this control concept has already been verified in many studies, in which the PMP-based control produces optimal solutions that are very close to the global optimal solution obtained by Dynamic Programming (DP). However, the drawback of the control concept is that the PMP-based control will not guarantee optimality if no information about the future driving condition is given. This is not just a drawback of the PMP-based control, but it is an unavoidable limitation in most optimal control concepts. Therefore, previous studies have been focused on finding an optimal costate when the future driving conditions are given or predicted prior to driving. In this study, a methodology that analyzes the past driving pattern and updates the control parameters is proposed by assuming that vehicles are operated under repeated driving conditions. A control parameter, or a costate in the PMP-based control, can be estimated from two parameters that characterize the driving conditions, and the correlation between the costates and the energy consumption patterns is used to update the control parameter. Based on this control concept, the final State of Charge (SOC) at the end of each drive gets gradually closer to the desired value as the driving cycle is repeated. The methodology can be used for vehicles operated under repeated driving patterns, such as commuting buses, parcel delivery vehicles, or refuse collection trucks.

Energy Management Strategy for Atkinson Cycle Engine Based Parallel Hybrid Electric Vehicle

Energy management strategies are widely used for the fuel efficiency of hybrid electric vehicle (HEV). Most of the on road hybrid cars use Atkinson cycle engine instead of Otto cycle engine, on account of better efficiency. The authors have already proposed a control oriented extended mean value engine model (EMVEM) of the Atkinson cycle engine and also developed and evaluated its control framework. The prior research was limited to evaluate the fuel efficiency of the engine only, while HEV employs both the engine and the motor. The presented work is an attempt to devise and demonstrate energy management strategy (EMS) by giving full consideration to the powertrain using Atkinson cycle engine. A novel energy management strategy based on the vehicle speed for Atkinson cycle engine for HEV is proposed. A parallel hybrid electric vehicle is modeled in Matlab/Simulink through a forward facing simulator. The proposed EMS with Atkinson cycle engine control framework exhibits the significant improvement in the fuel economy around 12.30% for standard Manhattan driving cycle and 7.22% for the modified federal urban driving schedule (FUDS) driving cycle in comparison with the Otto cycle engine.

Heat management methodology for enhanced global efficiency in hybrid electric vehicles

The transportation impact on pollution and global climate change, has forced the automotive sector to search for more ecological solutions. Owing to the different properties of Fuel Cell (FC), real potential for reducing vehicles’ emissions has been witnessed. The optimization of FC integration within Electric Vehicles (EVs) is one of the original solutions. This paper presents an innovating solution of multi-stack Fuel Cell Electrical Vehicle (FCEV) in terms of efficiency, durability and ecological impact on environment. The main objective is to illustrate the interest of using the multi-stack FC system on the global autonomy, cycling, and efficiency enhancement, besides optimizing its operation performance.

Robust proportional ECMS control of a parallel hybrid electric vehicle

Improved fuel efficiency in hybrid electric vehicles requires a delicate balance between the internal combustion engine usage and battery energy, using a carefully designed energy management control algorithm. Numerous energy management strategies for hybrid electric vehicles have been proposed in literature, with many of these centered on the equivalent consumption minimisation strategy (ECMS) owing to its potential for online implementation. The key challenge with the equivalent consumption minimisation strategy lies in estimating or adapting the equivalence factor in real-time so that reasonable fuel savings are achieved without over-depleting the battery state of charge at the end of the defined driving cycle. To address the challenge, this paper proposes a novel state of charge feedback ECMS controller which simultaneously optimises and selects the adaption factors (proportional controller gain and initial equivalence factor) as single parameters which can be applied in real time, over any driving cycle. Unlike other existing state of charge feedback methods, this approach solves a conflicting multiple-objective optimisation control problem, thus ensuring that the obtained adaptation factors are optimised for robustness, charge sustenance and fuel reduction. The potential of the proposed approach was thoroughly explored over a number of legislative and real-world driving cycles with varying vehicle power requirements. The results showed that, whilst achieving fuel savings in the range of 8.40 −19.68% depending on the cycle, final battery state of charge can be optimally controlled to within ±5% of the target battery state of charge.

Energy management strategy for hybrid electric vehicles using genetic algorithm

Energy management strategies significantly influence the fuel efficiency of hybrid electric vehicles. They play a crucial role in splitting the power between two sources, namely, engine and the battery. Power split between these two intelligently will enhance the fuel economy and regulates the power flow. Power split between engine and motor depends on state of charge (SOC) of battery, power required at the wheels, and engine's operating range. Various parameters of power train are considered to control the toggling between engine and battery. To achieve parameter optimization, genetic algorithm is practised to realize the optimal performance. A modified SOC estimation algorithm is employed with different battery models to analyze the vehicle performance. The battery models with internal resistance only and combinations of 1RC and 2RC are used. Parameter optimization over different battery models with modified SOC estimation algorithm is performed in different situations and a comparative study is elaborated.

HEV Optimal Battery State of Charge Prediction: A Time Series Inspired Approach

Fuel efficiency in hybrid electric vehicles requires a fine balance between combustion engine usage and battery energy, using a carefully designed control algorithm. Owing to the transient nature of HEV dynamics, driving conditions prediction, have unavoidably become a vital part of HEV energy management. The use of vehicle onboard telematics for driving conditions prediction have been widely researched and documented in literature, with most of these studies identifying high equipment cost and lack of route information (for routes unfamiliar to the GPS) as factors currently impeding the commercialization of predictive HEV control using telematics. In view of this challenge, this study inspires a look-ahead HEV energy management approach, which uses time series predictors (neural networks or Markov chains), to forecast future battery state of charge, for a given horizon, along the optimal front (optimal battery state of charge trajectory). The primary contribution of this paper is a detailed theoretical appraisal and comparison of the neural network and Markov chain time series predictors over different driving scenarios (FTP72, SC03, ARTEMIS U130 and WLTC 3 driving cycles). Based on the analysis performed in this study, the following useful inferences are drawn: 1. Prediction accuracy decreases massively and disproportionately on average with increased prediction horizon for multi-input neural networks, 2. In a single-input/single-horizon prediction network, the performance of both the neural network and Markov chain predictors are similar and near optimal, with a mean absolute percentage error of less than 0.7% and a root mean square error of less than 0.6 for all driving cycles analysed, 3. Markov chains appeal as a promising time series predictor for online vehicular applications, as it impacts the relative advantage of high precision and moderate computation time.

Life-cycle analysis of energy and greenhouse gas emissions of automotive fuels in India: Part 1 – Tank-to-Wheel analysis

As part of a two-part life cycle efficiency and greenhouse gas emission analysis for various automotive fuels in the Indian context, this paper presents the first part, i.e., Tank-to-Wheel analysis of various fuel/powertrain configurations for a subcompact passenger car. The Tank-to-Wheel analysis was applied to 28 fuel/powertrain configurations using fuels such as gasoline, diesel, compressed natural gas, liquefied petroleum gas and hydrogen with various conventional and hybrid electric powertrains. The gasoline-equivalent fuel economy and carbon dioxide emission results for individual fuel/powertrain configuration are evaluated and compared. It is found that the split hybrid configuration is best among hybrids as it leads to fuel economy improvement and carbon dioxide emissions reduction by 20–40% over the Indian drive cycle. Further, the engine efficiency, engine on-off time and regenerative braking energy assessment is done to evaluate the causes for higher energy efficiency of hybrid electric vehicles. The hybridization increases average engine efficiency by 10–60% which includes 19–23% of energy recovered at wheel through regenerative braking over the drive cycle. Overall, the Tank-to-Wheel energy use and efficiency results are evaluated for all fuel/powertrain configurations which show Battery Electric Vehicle, fuel cell vehicles and diesel hybrids are near and long term energy efficient vehicle configurations.