Performance Of Hybrid Electric Vehicles Research Articles

PROCESS MONITORING IN HYBRID ELECTRIC VEHICLES BASED ON DYNAMIC NONLINEAR METHOD

Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose a multivariate statistical method based on dynamic nonlinear improvement, namely dynamic neural component analysis (DNCA). This method does not require the establishment of precise analytical models; instead, it only necessitates acquiring data from HEV powertrains. Through numerical simulation and real HEV experiments, we demonstrate the effectiveness of this approach in monitoring highway third-level faults. The testing outcomes demonstrate that DNCA outperforms traditional dynamic methods like dynamic principal component analysis (DPCA), conventional nonlinear methods such as kernel PCA (KPCA) and NCA, as well as traditional dynamic nonlinear methods like DKPCA.

Optimal plug‐in hybrid electric vehicle performance management using decentralized multichannel network design

AbstractIn addition to providing mobility, plug‐in hybrid electric vehicles (PHEVs) provide a two‐sided energy exchange opportunity which makes them highly flexible distributed energy storage systems for the future of energy systems. This paper analyzes PHEVs' performance from the perspective of urban traffic and energy using a decentralized multichannel blockchain network based on the hyperledger model. This network using a layered design and local management of energy sources can significantly contribute to urban management and optimal use of its infrastructures. Then, dynamic modelling of PHEVs in this network is performed, and their data is added to the network to evaluate the network performance compared with the current centralized networks. The results indicated that the proposed blockchain network could simultaneously optimize PHEVs' performance, urban traffic management, and energy systems. Furthermore, by utilizing smart contracts, it can consider and optimize multiple challenges, such as congestion in the electricity network, urban traffic, and limited fuel, simultaneously. Therefore, it gives a strong tool to study the impact of mass deployment of PHEVs and their value and role in the sustainable cities and communities of the future while helping to support the global efforts toward affordable and clean energy for all.

Assessment of the On-Road Performance of Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) in Urban Road Conditions in the Philippines

This current and pioneering work aimed to assess the on-road performance of selected hybrid electric vehicles (HEVs) and electric vehicles (EVs) in local urban road conditions following the World Harmonized Light Vehicles Test Procedure (WLTP) and the chase car protocol. An experimental research design was also implemented to investigate the effects of the different payload conditions on vehicle performance, and corresponding drive cycle patterns for the test vehicles were generated from each on-road test. From the series of these on-road tests, it was revealed that there was high variability in speed profiles, and vehicle speed was generally found to be inversely related to payload weight. The variations in the state of charge, fuel fill-up, and fuel and energy parameters exhibited no significant differences in terms of payload conditions. When compared to both the Canada fuel consumption guide and the US fuel consumption guide, the resulting fuel consumption and energy consumption indicated that the Mitsubishi Outlander PHEV and Mitsubishi iMiEV exceeded energy efficiency standards, unlike the Toyota Prius. Meanwhile, in terms of CO2 emissions, all vehicles demonstrated around 40–70% lower emissions compared to conventional vehicles according to the 2023 estimates of the US Environmental Protection Agency. Being the first of its kind in the Philippines, this study on the on-road performance assessments of HEVs and EVs is essential because it provides empirical data on these vehicles’ actual performance in everyday driving conditions. The data are important for evaluating the potential to address environmental concerns, promote sustainable transportation solutions, influence consumer adoption, and shape government policies. With ongoing improvements in technology and expanding charging infrastructure, HEVs and EVs are poised for significant adoption in the coming years.

Performance of hybrid electric vehicle air-conditioning using SiO2/POE nanolubricant

The automobile air-conditioning (AAC) system uses the most energy among all auxiliary components in hybrid electric vehicles (HEV) with an electrically-driven compressor (AAC-EDC). Incorporating nanolubricant improves AAC-EDC performance and reduces battery and AAC component size. This study aims to assess the performance of an AAC-EDC system by employing SiO2/POE nanolubricants. The performance of the AAC-EDC was examined using SiO2/POE with a volume concentration of up to 0.01 %, a refrigerant charge of 120–160 g, and a compressor speed of 1200–3840 rpm. The performance of the AAC system was measured by its heat absorption, compressor work, coefficient of performance (COP), and EDC power consumption. The SiO2/POE was stable for 30 days with a zeta potential of 102.5 mV. The SiO2/POE demonstrated superior performance over the pure POE lubricants. The compressor work and power consumption were reduced to 26.17 % and 11.97 %, respectively. The lowest expansion valve discharge temperature of 9.6 °C was achieved at 160 g refrigerant charge and compressor speed of 3840 rpm. The nanolubricant attained the highest COP of 3.36 at 1860 rpm speed and 160 g charge. The SiO2/POE nanolubricant with a volume concentration of 0.01 % is highly recommended for optimal performance in the AAC-EDC system.

Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components.

Accurate and Efficient Energy Management System of Fuel Cell/Battery/Supercapacitor/AC and DC Generators Hybrid Electric Vehicles

The development of hybrid electric vehicles (HEVs) is rapidly gaining traction as a viable solution for reducing carbon emissions and improving fuel efficiency. One type of HEV that is gaining significant interest is the fuel cell/battery/supercapacitor HEV (FC/Bat/SC HEV), which combines fuel cell, battery, supercapacitor, AC, and DC generators. These FC/B/SC HEVs are particularly appealing because they excel at efficiently managing energy and cater to a wide range of driving requirements. This study presents a novel approach for exploiting the kinetic energy of a sensorless HEV. The vehicle has a primary fuel cell resource, a supercapacitor, and lithium-ion battery energy storage banks, where each source is connected to a special converter. The obtained hybrid system allows the vehicle to enhance autonomy, support the fuel cell during low production moments, and improve transient and steady-state load requirements. The exploitation of kinetic energy is performed by the DC and AC generators that are linked to the electric vehicle front wheels to transfer the HEV’s wheel rotation into power, contributing to the overall power balance of the vehicle. The energy management system for electric vehicles determines the FC setpoint power through the classical state machine method. At the same time, a robust speed controller-based artificial intelligence algorithm reduces power losses and enhances the supply efficiency for the vehicle. Furthermore, we evaluate the performance of a robust controller with a speed estimator, specifically using the adaptive neuro-fuzzy inference system (ANFIS) and the model reference adaptive system (MRAS) estimator in conjunction with the direct torque control-support vector machine (DTC-SVM), to enhance the torque and speed performance of HEVs. The results demonstrate the feasibility and reliability of the vehicle while utilizing the additional DC and AC generators to extract free kinetic energy, both of which contributed to 28% and 24% of the total power for the vehicle, respectively. This approach leads to a vehicle supply efficiency exceeding 96%, reducing the burden on fuel cells and batteries and resulting in a significant reduction in fuel consumption, which is estimated to range from 25% to 35%.

Health-conscious energy management strategy for battery/fuel cell electric vehicles considering power sources dynamics

This paper presents a new multi-stage power management strategy aimed at enhancing the security and performance of hybrid electric vehicles (HEVs). The proposed strategy is designed to protect sensitive vehicle power sources, such as fuel cells (FCs), from potential damages caused by abrupt load variations. This is achieved through the implementation of a model-based coordinated switching strategy that utilizes transition functions that are tailored to the transient dynamics of power sources. The power flow in HEVs is managed using a fuzzy energy management approach that allows safe and predefined operation of FCs at multiple operating points. To ensure reliable and uninterrupted HEV operation, fault detection algorithms have been integrated into the second stage of the power management strategy to detect and correct any power source failures. Simulation results indicate that the proposed fuzzy energy management strategy provides efficient usage and precise control over FC power, while the coordinated switching strategy compensates for the sluggishness of FCs and reduces transient power ripples and fluctuations in the DC bus voltage during FC operating point variations. The efficiency of the proposed multi-stage power management strategy has been validated through simulations performed on the RT LAB platform and the obtained results were deemed satisfactory.

Configurations and Control Strategies of Hybrid Powertrain Systems

The configuration and control strategy of hybrid powertrain systems are significant for the development of hybrid electric vehicles (HEV) because they significantly affect their comprehensive performance. In this paper, the types, features, and applications of the mainstream hybrid powertrain configurations on the market in recent years are summarized and the effects of different configurations on the comprehensive performance of HEVs are compared. Moreover, the technical routes for each hybrid configuration are highlighted, as configuration optimization methods have become a technical difficulty. In addition, the technological advances in the steady-state energy management strategy and dynamic coordinated control strategy for hybrid powertrain systems are studied. The optimization of the steady-state energy management strategy mainly involves assigning the working point and working range of each power source reasonably. However, with the increase in the complexity of optimization algorithms, real-time control of HEVs still needs to be improved. The optimization of the dynamic coordinated control strategy mainly focuses on the stability and smoothness of the dynamic process involving switching and shifting the working mode. The optimization of the dynamic control process for the system remains to be further improved. It is pointed out that the configurations and strategies should be optimized jointly to obtain a comprehensive improvement in the system performance. This paper provides an informative basis and technical support for the design and optimization of a hybrid powertrain system.

Mathematical Modeling of Social Assessment for Alternative Fuel Vehicles

This research presents the first comprehensive model for social assessment of alternative fuel vehicles, covering hybrid-electric, battery-electric, and fuel-cell vehicles, based on their technological, financial, and infrastructure aspects. The model is adaptable to any country and does not require social engagement methods, thus producing more objective results. Indicators are assessed case-by-case and receive a score from zero to five. Lower scores show a greater need for prioritizing the indicator when adopting a specific alternative fuel vehicle. A low score doesn’t mean a low impact, but instead emphasizes the importance of improving the relevant indicator for the particular vehicle technology in the studied region. The case study conducted in Morocco tested the model, highlighting the superior performance of hybrid-electric vehicles based on their score. Although these vehicles offer financial profitability, their successful adoption relies on the government’s provision of more options, reduced purchase costs, and improved secondary road infrastructure. Battery-electric vehicles, while facing challenges in terms of maintenance and charging infrastructure, can be addressed in Morocco by implementing on-site generation in charging stations and enhancing secondary road infrastructure. On the other hand, fuel-cell vehicles have notable social requirements and necessitate more extensive efforts to penetrate the market compared to battery-electric vehicles. Finally, the authors suggest including more alternative fuel vehicles in the model and additional social variables, such as user experience, vehicle size, fuel efficiency, environmental awareness, purchasing behavior, and brand loyalty. The suggestion would allow gaining more insight into social preferences.

Hybrid Electric Vehicle Design and Simulation Performance Using Isolated DC/DC Converter Based MFO Algorithm

The accuracy and effectiveness of the vehicle’s motor, DC-DC converter, controller, and electrical system are crucial for Hybrid Electric Vehicles (HEV). The study is given with a DC-DC converter architecture and motor based on the MFO control algorithm for smooth driving and quick charging of hybrid electric vehicles. The front-end AC-DC converter’s output is translated by the suggested DC-DC converter architecture, which then provides a controlled supply to the EV propulsion battery. The suggested system consists of a drive train model created in MATLAB using the SIMULINK tool, a motor drive, a DC-DC converter, an inverter, and a drive train. A Simulink model with motor drive model was constructed and successfully simulated in order to operate and evaluate the performance of hybrid electric vehicles utilizing an MFO control method. The simulation findings are examined its performance of EV system and results clearly stated the hybrid car’s fuel efficiency advantages over the conventional vehicle model.

A Novel Braking Control Strategy for Hybrid Electric Buses Based on Vehicle Mass and Road Slope Estimation

Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles, which is prominently proved by many studies. To achieve better dynamic stable performance and higher energy recovery efficiency, an effective braking control strategy for hybrid electric buses (HEB) based on vehicle mass and road slope estimation is proposed in this paper. Firstly, the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter (EKF) and recursive least square (RLS). Secondly, the total braking torque of HEB is calculated by the sliding mode controller (SMC), which uses the information of brake intensity, whole vehicle mass, and road slope. Finally, comprehensively considering driver's braking intention and regulations of the Economic Commission for Europe (ECE), the optimal proportional relationship between regenerative braking and pneumatic braking is obtained. Furthermore, related simulations and experiments are carried out on the hardware-in-the-loop test bench. Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.

Performance comparison of series–parallel hybrid transmissions with multiple gears and modes based on efficiency model

Although multi-gear and multi-mode series–parallel hybrid transmissions (MGMM-SPHTs) can significantly improve the economy and power performance of hybrid electric vehicles (HEVs), few studies have elucidated their inherent mechanism. The present study quantitatively explores how different numbers of gears, the speed ratio range, and the working mode improve the economy and power performance of SPHTs. Moreover, the cost factor is considered in the comprehensive evaluation index. First, the speed ratio range of SPHT with different gears is defined, and the possible working modes of SPHT with different gear combinations are examined. Second, the efficiency model of the SPHT components is constructed on the basis of the energy flow, which provides a foundation for the subsequent construction of the power loss model. Moreover, by taking the minimum power loss in the power transmission process as the objective function, the dynamic programming method is used to evaluate the energy utilization performance of SPHT with different numbers of gears. Finally, on the basis of the aforementioned research, the comprehensive performance of MGMM-SPHTs is quantitatively compared, and the various influencing factors are analyzed in detail. The comparative analysis shows that increasing the number of engine direct drive gears has a significant effect on improving the economy. In particular, when the number of gears is increased from one to two, the fuel consumption is reduced by 4.007% on average; by contrast, when the number of gears is increased from two to three, the fuel consumption is reduced by only 1.173%. In addition, increasing the number of engine and motor direct drive gears has a significant effect on improving the power performance. Furthermore, this study elucidates the energy-saving mechanism of MGMM-SPHT, which can effectively guide the product design and development of SPHT.

Characterizing and assessing the fuel economy, particle number and gaseous emissions performance of hybrid electric and conventional vehicles under different driving modes

Hybrid electric vehicle (HEV) is considered to be more energy efficient and less emissions than conventional gasoline vehicle (CV). A growing number of studies suggested that real-road emissions may be much higher than laboratory measurements. To compare the fuel economy and emissions performance of HEV and CV on real roads, this study conducted real driving emission (RDE) tests on HEV and CV of the same configuration under real driving conditions with aggressive and normal driving styles. The results indicated that the HEV had lower fuel consumption (L/100 km), nitrogen oxides (NOX) and carbon dioxide (CO2) emissions compared to the CV. Fuel saving of HEV compared to CV was up to 41.14% under urban conditions. Compared to emissions of other trip conditions, The CO2 and NOX reductions of HEV under urban conditions were the largest, which were more than 33.65% and 61.98% respectively. However, the particle number (PN) and carbon monoxide (CO) emissions of HEV were larger than those of CV. This was due to the frequent stopping and restarting of the internal combustion engine of the HEV. In order to comprehensively compare the fuel economy and emissions performance of HEV and CV, the evaluation index of real conformity factor was proposed. The analysis showed that to reduce the high PN and CO emissions of HEV, a combination of fuel economy-focused and emission control-focused strategies should be used.

A Novel Control Strategy of Electric Vehicle with Hybrid Energy Storage System using Interval Type 2.0 Fuzzy Logic Controller

This research work proposes an electric power train (EPT) with hybrid energy storage system (HESS) using an interval type 2.0 fuzzy logic controller (T2.0-FLC). EPT’s will play a vital role in present and future transportation because they do not emit harmful gases and do not rely on fuel. In this proposed work, storage devices like battery, supercapacitor, and fuel cell will be considered for electric vehicle and a novel control strategy based on interval T2.0-FLC is used. There are various types of electric motors are generally used in Electric vehicles but In our proposed research work Permanent Magnet Synchronous Motor is used. This work implemented in different cases, starting from only solar powered electric vehicle to hybrid storage-Electric Vehicle having battery, solar, supercapacitor, and fuel cell. This research study gives a detail comparative analysis of the performance of hybrid electric vehicle between Type-1 FLC& IntervalType-2.0 FLC. Also shows the edge of Interval T2.0-FLC based electric vehicle over Type-1 FLC based electric vehicle as interval T2.0 approach having better response. The entire proposed scheme implemented with the help of MATLAB software

Energy management for hybrid electric vehicles based on imitation reinforcement learning

An effective energy management strategy (EMS) in hybrid electric vehicles (HEVs) is indispensable to promote consumption efficiency due to time-varying load conditions. Currently, learning based algorithms have been widely applied in energy controlling performance of HEVs. However, the enormous computation intensity, massive data training and rigid requirement of prediction of future operation state hinder their substantial exploitation. To mitigate these concerns, an imitation reinforcement learning-based algorithm with optimal guidance is proposed in this paper for energy control of hybrid vehicles to accelerate the solving process and meanwhile achieve preferable control performance. Firstly, offline global optimization is firstly conducted considering various driving conditions to search power allocation trajectories. Then, the battery depletion boundaries with respect to driving distance are introduced to generate a narrowed state space, in which the optimal trajectory is fused into the training process of reinforcement learning to guide the high-efficiency strategy production. The simulation validations reveal that the proposed method provides preferable energy reduction for HEVs in arbitrary driving scenarios, and suggests an efficient solution instruction for similar problems in mechanical and electrical systems with constraints and optimal information.

Comprehensive model for energetic analyses of a series hybrid-electric vehicle powered by a passive Turbulent Jet Ignition engine

In the last decades, the concern about vehicles’ environmental impact has progressively increased. Therefore, car manufacturers are moving towards vehicles with low emissions like Hybrid Electric Vehicles (HEV), that in the series architecture utilize an internal combustion engine (ICE) to power an electric generator for battery recharging thus extending the driving range of a Battery Electric Vehicle (BEV). For such applications, small four-stroke spark-ignition (SI) engines are a suitable solution, as they are proven low-cost and reliable systems and allow to increase vehicle driving range with reduced environmental impact. In series HEV the ICE is decoupled from the drive wheels and operates at a fixed working point regardless to the instantaneous power demand, thus promoting the lean mixture operation to achieve high efficiency and low CO2 emissions. In this work, the performance, in terms of driving range, energy consumption and CO2 emissions, of a series HEV are analyzed in experimental and simulation environment. Particularly, the Auxiliary Power Unit (APU) is a small ICE equipped with a passive Turbulent Jet Ignition (TJI) that allows to guarantee stable combustion also under lean mixture operation, with an increase of engine efficiency and a reduction of CO2 emissions. The effects of lean air–fuel ratio on combustion process and engine performance are investigated by a one-dimension (1-D) model and validated vs experimental measurements at the test bench on a prototype single cylinder engine equipped with passive prechamber. The benefits of the high efficiency ICE operation on the series HEV performance, energy consumption and CO2 emissions vs standard and arbitrary driving cycles are analyzed in simulation environment, making use of the simulation results gathered by the 1-D engine model. Simulation analyses are carried out to evaluate the best compromise between battery pack downsizing and energy consumption for a fixed target of vehicle drive range. Simulation results show that TJI concept allows to achieve an APU efficiency up to 32 %, by operating with λ equal to 1.26. Additionally, the analysis on battery pack downsizing evidences that switching from 150 % to 50 % of the reference battery pack capacity results in an increase of the overall energy consumption up to 22 %.

Impact of the number of planetary gears on the energy efficiency of electrified powertrains

Planetary gears (PGs) play a critical role in hybrid electric vehicles (HEVs) by combining the output torques of different powertrain components and delivering the resulting torque to the wheels. Whilst previous studies show that the number of planetary gears affects performance of HEVs, there is no prior study to systematically investigate such effects on energy consumption. This paper quantifies the energy efficiency improvement of HEVs due to increasing the number of PGs from one to two, and from two to three. This is done by comparing the minimum energy consumption for different topologies when the rest of the powertrain components – namely electric motors, batteries and engine – are the same. To calculate the minimum energy consumption, the paper proposes an optimal energy management strategy (EMS) for each topology to find the optimum sequence of clutch engagement and torque distribution. The minimum energy consumption of a vehicle with different number of PGs is then evaluated using the automotive simulation models (ASM) from dSPACE. Results show that, for the same electric motors and engine, increasing the number of PGs from one to two and from two to three reduces energy consumption by 5% and 1.5%, respectively.

Energy Management of Hybrid Electric Urban Bus by Off-Line Dynamic Programming Optimization and One-Step Look-Ahead Rollout

Due to the growing air quality concern in urban areas and rising fuel prices, urban bus fleets are progressively turning to hybrid electric vehicles (HEVs) which show higher efficiency and lower emissions in comparison with conventional vehicles. HEVs can reduce fuel consumption and emissions by combining different energy sources (i.e., fuel and batteries). In this sense, the performance of HEVs is strongly dependent on the energy management strategy (EMS) which coordinates the energy sources available to exploit their potential. While most EMSs are calibrated for general driving conditions, this paper proposes to adapt the EMS to the specific driving conditions on a particular bus route. The proposed algorithm relies on the fact that partial information on the driving cycle can be assumed since, in the case of a urban bus, the considered route is periodically covered. According to this hypothesis, the strategy presented in this paper is based on estimating the driving cycle from a previous trip of the bus in the considered route. This initial driving cycle is used to compute the theoretical optimal solution by dynamic programming. The obtained control policy (particularly the cost-to-go matrix) is stored and used in the subsequent driving cycles by applying one-step look-ahead roll out, then, adapting the EMS to the actual driving conditions but exploiting the similarities with previous cycles in the same route. To justify the proposed strategy, the paper discusses the common patterns in different driving cycles of the same bus route, pointing out several metrics that show how a single cycle captures most of the key parameters for EMS optimization. Then, the proposed algorithm (off-line dynamic programming optimization and one-step look-ahead rollout) is described. Results obtained by simulation show that the proposed method is able to keep the battery charge within the required range and achieve near-optimal performance, with only a 1.9% increase in fuel consumption with regards to the theoretical optimum. As a reference for comparison, the equivalent consumption minimization strategy (ECMS), which is the most widespread algorithm for HEV energy management, produces an increase in fuel consumption with respect to the optimal solution of 11%.

Uncertainty-Aware Energy Management Strategy for Hybrid Electric Vehicle Using Hybrid Deep Learning Method

Energy management strategy (EMS) is important to ensure energy-saving performance of hybrid electric vehicle (HEV). However, the power coupling property between different power sources, together with stochastic power demand fluctuation poses great challenges for EMS to achieve desirable performance in real-world scenario. This paper presents an uncertainty-aware energy management strategy for HEV. A speed predictor combining convolutional neural network and long short-term memory neural network is proposed to extract temporal features that could reveal speed change mechanism. Then an online self-adaptive transition probability matrix is constructed to estimate the speed prediction uncertainty. Tube model predictive control (tube-MPC) is finally used to solve the optimization control problem in a receding horizon manner. The robust set introduced in the tube-MPC greatly enhances the optimality and robust-ness of the control sequence under the scenario with speed prediction uncertainty. Simulations are conducted to verify the effectiveness of the proposed method. Results show that the speed prediction accuracy is 47.4% and 23.1% higher than exponential decay rate prediction model and autoregressive integrated moving average model respectively. Compared with traditional rule-based and MPC method, the proposed tube-MPC method could achieve 10.7% and 3.0% energy-saving performance improvement in average.

Development of a Hybrid Electric Vehicle Simulation Tool with a Rule-Based Topology

The performance of hybrid electric vehicles (HEVs) greatly depends on the various sub-system components and their architecture, and designers need comprehensive reviews of HEVs before vehicle investigation and manufacturing. Simulations facilitate development of virtual prototypes that make it possible to rapidly see the effects of design modifications, avoiding the need to manufacture multiple expensive physical prototypes. To achieve the required levels of emissions and hardware costs, designers must use control strategies and tools such as computational modeling and optimization. However, most hybrid simulation tools do not share their principles and control logic algorithms in the open literature. With this motivation, the author developed a hybrid simulation tool with a rule-based topology. The major advantage of this tool is enhanced flexibility to choose different control and energy management strategies, enabling the user to explore a wide range of hybrid topologies. The tool provides the user with the ability to modify any sub-system according to one’s own requirements. In addition, the author introduces a simple logic control for a rule-base strategy as an example to show the flexibility of the tool in allowing the adaptation of any logic algorithm by the user. The results match the experimental data quite well. Details regarding modeling principle and control logic are provided for the user’s benefit.