Rule-based Control Strategy Research Articles

A Single-Stage SOC Reference Trajectory Prediction Method for Series PHEV Based on GRNN

In this paper, a single-stage SOC reference trajectory (SOC-RT) prediction method is proposed. Different from the two-stage prediction method mostly used in previous studies, the proposed strategy predicts the SOC-RT slope directly based on the operating condition, instead of predicting the vehicle speed first and then solving it based on a global optimization algorithm. Therefore, the proposed method can significantly reduce the computational load and has high application value. A generalized regression neural network (GRNN) is employed as the prediction model for its strong nonlinear fitting ability. Meanwhile, to solve the problem of error accumulation due to the slope prediction, the prediction trajectory is corrected by the linear trajectory, and the correction factor is determined based on particle swarm optimization algorithm (PSO). Finally, the trajectory is followed by equivalent consumption minimization strategy (ECMS) and the performance of the proposed strategy is verified. The results show that the prediction accuracy is improved by 52.10% and 3.73% compared to the linear trajectory under the two tested cycles, respectively. Meanwhile, the proposed strategy achieves the best fuel economy, with fuel consumption decreasing by 2.61% and 6.40%, respectively, compared to the rule-based control strategy. Finally, its real-time control performance in a real hardware environment is verified through the hardware-in-the-loop test.

A Computationally Efficient Rule-Based Scheduling Algorithm for Battery Energy Storage Systems

This paper presents a rule-based control strategy for the Battery Management System (BMS) of a prosumer connected to a low-voltage distribution network. The main objective of this work is to propose a computationally efficient algorithm capable of managing energy flows between the distribution network and a prosumer equipped with a photovoltaic (PV) energy production system. The goal of the BMS is to maximize the prosumer’s economic revenue by optimizing the use, storage, sale, and purchase of PV energy based on electricity market information and daily production/consumption curves. To achieve this goal, the method proposed in this paper consists of developing a rule-based algorithm that manages the prosumer’s Battery Energy Storage System (BESS). The rule-based approach in this type of problem allows for the reduction of computational costs, which is of fundamental importance in contexts where many users will be coordinated simultaneously. This means that the BMS presented in this work could play a vital role in emerging Renewable Energy Communities (RECs). From a general point of view, the method requires an algorithm to process the load and generation profiles of the prosumer for the following three days, together with the hourly price curve. The output is a battery scheduling plan for the timeframe, which is updated every hour. In this paper, the algorithm is validated in terms of economic performance achieved and computational times on two experimental datasets with different scenarios characterized by real productions and loads of prosumers for over a year. The annual economic results are presented in this work, and the proposed rule-based approach is compared with a linear programming optimization algorithm. The comparison highlights similar performance in terms of economic revenue, but the rule-based approach guarantees 30 times lower processing time.

Techno-economic assessment of a Carnot battery thermally integrated with a data center

Carnot batteries (CBs) are gaining interest as energy storage solutions, particularly when waste heat is available for thermal integration. Data centers (DCs) represent a relevant source of waste heat, as they convert most of the supplied electricity into low-grade heat, and their utilization is expected to continue increasing in the future. This study explores the feasibility of integrating an Organic Rankine Cycle (ORC)/Heat Pump (HP) CB with a DC powered by a photovoltaic power plant. First, from the thermodynamic analysis conducted in design conditions, the hydrofluoroolefin R1233zd(E) results in the most suitable working fluid, leading to 43 % roundtrip efficiency. Then, a detailed semi-empirical off-design model of a CB is employed in a rule-based control strategy to handle the ORC/HP operations. If the HP is thermally integrated with the DC waste heat, a higher coefficient of performance is achieved, and the consumption of the DC cooling system is reduced. A sensitivity analysis is conducted to explore the impact of the storage volume and the energy prices on the integrated system’s techno-economic performance. The CB implementation is economically feasible when the electricity price is high, reaching a yearly gain of 7744 €, and a simple payback period of 10 years when considering a DC electric consumption of 200 kWe and a 25 kWe-sized HP/ORC. The gain increases if the electric surplus cannot be sold, obtaining a 4-year payback period. Eventually, we perform a comparison with an ORC-only configuration to assess the CB’s technical and economic convenience.

Heating performance of PCM radiant floor coupled with horizontal ground source heat pump for single-family house in cold zones

As the energy demand for space heating increases, the necessity for innovative and energy-efficient heating technologies has become more urgent. This study proposes and evaluates a heating system that integrates a phase change material radiant floor with a horizontal ground source heat pump (PCM floor-HGSHP, Case 1), intending to maintain indoor comfort temperatures and enhance energy efficiency through the utilization of renewable energy sources. Despite the considerable potential of PCM floors, their combination with HGSHP requires further research to fully unlock their benefits. The study involved simulating the PCM floor using a validated TRNSYS component and developing an advanced rule-based control strategy with time-of-use tariffs as a key input to optimize system operation. To elucidate and quantify the advantages of the PCM floor-HGSHP system and verify its applicability in cold regions of China, a single-family house was used as a case study building, four representative cities were selected, and two comparative simulation cases were defined, along with three key performance indicators. The results indicate that, compared with the radiant floor heating system with HGSHP (RFHS-HGSHP, Case 2), the incorporation of PCM in the floor of Case 1 improves the indoor temperature stability by 8.6 %, enhances the load flexibility by 18.1 %, and reduces the total operating costs by 13.8 %. Furthermore, relative to the PCM floor with air-to-water heat pump system (PCM floor-AWHP, Case 3), the proposed system which substitutes the AWHP with the HGSHP, extends the duration of indoor comfort temperatures by 9.6 % and decreases the system electricity consumption by 33.7 %. The proposed system demonstrates excellent thermal and energy performance across all selected cities, proving to be both technically feasible and significantly beneficial for maintaining indoor comfort temperatures and reducing operational costs. This study offers valuable insights and technical support for designing and optimizing residential heating systems in cold regions, thereby promoting the development and application of renewable energy sources.

An improved rule-based control of battery energy storage for hourly power dispatching of photovoltaic sources

This paper presents an improved rule-based control scheme for battery energy storage (BES) system with the goal of minimising the fluctuation output from photovoltaic (PV) sources while ensuring the operational constraints of BES are regulated at the specified ranges for safety purposes. The control scheme is formulated in accordance with the intended operational limitations of the BES, including charge/discharge current limits and state-of-charge (SOC). The simulation studies were carried out using MATLAB/Simulink to evaluate the effectiveness of an improved rule-based control scheme on the 1.2 MW PV system data acquired from a location in Malaysia. Furthermore, a comparative study on the effectiveness of an improved rule-based control scheme compared with the conventional rule-based control scheme has been carried out. The simulation results show that an improved rule-based control scheme can effectively reduce the fluctuations in the output power of the PV sources and dispatch the output to the utility grid on an hourly basis with an efficiency of up to 94.47%. Finally, the comparison results on the effects of the BESS capacity also illustrate that an improved rule-based control scheme is more effective compared to the conventional rule-based control scheme in the previous study.

Performance evaluation of underground thermal storage integrated dual-source heat pump systems

The increasing demand for electricity stresses the existing electric grids. Buildings consume 73% of all U.S. electricity and are responsible for 30% of U.S. greenhouse gas emissions. Integrating thermal energy storage (TES) in building heating/cooling systems, which consume considerable electricity, can mitigate the challenges to electric grids. This study reports on a novel thermal energy storage device integrated heat pump system to reshape the building electricity demand profile while maintaining thermal comfort. The annual performance of the proposed system has been evaluated through a dynamic system simulation with high fidelity in the Modelica platform. The dynamic model of the novel hybrid component named ‘dual purpose underground thermal battery’ was developed and validated. It was then incorporated into the system model. Given a time-of-use tariff, a rule-based control strategy was designed to shift the electric demand and switch the heat pump source for a typical single-family house in different climate zones of the United States. The system performance of the new TES-integrated dual-source heat pump was compared with that of a conventional air-source heat pump system. The results indicate that the proposed system can reduce the annual HVAC electricity cost by up to 52% while saving 45.2% on electricity consumption. In the Northern areas, the annual peak load of the HVAC system can be reduced by 64.9%. However, this reduction is less in the Southern areas as the system’s higher efficiency in winter dominates the overall energy-saving potential.

Research on Power Optimization for Energy System of Hydrogen Fuel Cell Wheel-Driven Electric Tractor

Hydrogen fuel cell tractors are emerging as a new power source for tractors. Currently, there is no mature energy management control method available. Existing methods mostly rely on engineers’ experience to determine the output power of the fuel cell and the power battery, resulting in relatively low energy utilization efficiency of the energy system. To address the aforementioned problems, a power optimization method for the energy system of hydrogen fuel cell wheel-driven electric tractor was proposed. A dynamic model of tractor ploughing conditions was established based on the system dynamics theory. From this, based on the equivalent hydrogen consumption theory, the charging and discharging of the power battery were equivalent to the fuel consumption of the hydrogen fuel cell, forming an equivalent hydrogen consumption model for the tractor. Using the state of charge (SOC) of the power battery as a constraint, and with the minimum equivalent hydrogen consumption as the objective function, an instantaneously optimized power allocation method based on load demand in the energy system is proposed by using a traversal algorithm. The optimization method was simulated and tested based on the MATLAB simulation platform, and the results showed under ploughing conditions, compared with the rule-based control strategy, the proposed energy system power optimization method optimized the power output of hydrogen fuel cells and power batteries, allowing the energy system to work in a high-efficiency range, reducing the equivalent hydrogen consumption of the tractor by 7.79%, and solving the energy system power distribution problem.

Research on Automatic Optimization of a Vehicle Control Strategy for Electric Vehicles Based on Driver Style

In order to reduce energy consumption, improve driving mileage, and make vehicles adopt driver styles, research on automatic optimization of control strategy based on driver style is conducted in this paper. According to the structure of the powertrain, the vehicle control strategy is designed and a driver-style recognition model based on fuzzy recognition is added to the rule-based control strategy to improve the driver adaptation of the vehicle. In order to further improve the energy-saving effect of the strategy, the control strategy based on driver style is automatically optimized by the Isight optimization platform to make the strategy reach optimum. The test results show that the strategy based on driver style is able to adapt to different styles of drivers and the economy of the vehicle is improved by 2.06% compared with pre-optimization, which validates the effectiveness of the strategy.

Operational performance of PCM embedded radiant chilled ceiling using a rule-based control strategy

The increasing energy demand for space cooling, projected to triple by mid-century and surpassing a quarter of today's global electricity usage, underscores the urgent need for innovative, more energy-efficient air-conditioning technologies. The research introduces phase change material (PCM) embedded radiant cooling (PCM-RCC) as a promising solution, offering enhanced energy efficiency and indoor environmental quality. Despite its potential, PCM-RCC remains in the developmental stage, requiring further research to fully unlock its benefits. This work aims to analyse the operational performance of PCM-RCC and develop an advanced rule-based control strategy to enhance its efficiency further. The analysis was conducted using a validated PCM-RCC transient system simulation model. To quantify the advantages of PCM-RCC over typical radiant cooling and evaluate the effectiveness of the proposed control strategy, two reference cases are defined: (1) basic-controlled PCM-RCC, and (2) radiant cooling without PCM. Results reveal that PCM-RCC with advanced control demonstrates higher load flexibility (∼93% off-peak time operation), 12% less electricity usage, a 5% coefficient of performance improvement, and a 30% decrease in operational costs compared to radiant cooling. Furthermore, the proposed system shows 9% lower energy usage with a 20% improvement in thermal comfort compared to basic-controlled PCM-RCC.

A 4-Phase Combined Adhesion Threshold Algorithm for Wheel Slide Protection Systems in Rail Vehicles

The wheel slide protection control system for rail vehicles plays a crucial role in ensuring a consistent braking performance in all operating environments, making it a vital factor in the safety and efficiency of rail transportation. In this paper, a hybrid approach to wheel slide protection control is presented, which combines the rule-based control strategy and the model-based control methods using adhesion force estimation. Model-based control usually relies on mathematical models to characterize the vehicle dynamics, requiring online estimators to be designed or extra sensors to be added for practical application. Rule-based control operates on predefined rules and thresholds and the available data from vehicles in service. A comparative test was conducted between the traditional rule-based control strategy and the proposed combined control strategy using a semi-physical simulation test bench. The performance differences of the control strategies were analyzed from two perspectives: adhesion utilization and air consumption. It was observed that among the traditional 2-phase, 3-phase, 4-phase and the optimized 4-phase combined control method, the combined control strategy has the best adhesion utilization and the traditional 4-phase control strategy has the least air consumption.

A thermal management control using particle swarm optimization for hybrid electric energy system of electric vehicles

A metaheuristic algorithm, Particle Swarm Optimization (PSO), was employed for developing the optimal control strategies for an innovative hybrid thermal management system (IHTMS) in a proton exchange membrane fuel cell (PEMFC)/battery electric vehicles (EVs). The goals were to shorten the period of low-efficiency temperatures during the initial startup of EVs, and to maintain temperatures of PEMFCs and batteries at their optimal-efficiency zones, where significantly enhances the traveling range and power output of EVs. Prior to simulation for benefit analysis, eight IHTMS subsystems were mathematically constructed. For the multi-input-multi-output PSO control strategy, two inputs were the fuel cell and battery coolant temperatures; while two outputs were the coolant mass flow rate and the flow rate ratio between two energy sources. A rule-based (RB) control strategy for four actuators was designed as the baseline case. Another RB using the PSO to derive the initial conditions (PSOi) was developed as well. In this research, the IHTMS was tested under two driving patterns, WLTP and NEDC, where outstanding thermal management performance was exhibited. The results demonstrate that: in WLTP driving cycle, to compare PSO and PSOi-RB with the RB strategies, the rise time of optimal temperature decreased 13.655 % and 9.505 % for the PEMFC; while 8.77 % and 4.385 % for the battery. For the NEDC driving cycle, the rise time of optimal temperature decreased 8.908 % and 7.318 % for the PEMFC, while 5.226 % and 3.136 % for the battery. The improvements of average temperature errors of the PEMFC were 19.759 % and 11.023 %; the improvements of the average temperature errors of the battery were 57.027 % and 3.67 %. For NEDC driving cycle, the improvements of average temperature errors of the PEMFC were 18.879 % and 9.551 %; the improvements of the average temperature errors of the battery were 29.144 % and 20.221 %. In the future work, the IHTMS will be integrated to a hybrid-energy EV for experimental verification.

Optimal peak-shaving for dynamic demand response in smart Malaysian commercial buildings utilizing an efficient PV-BES system

Reducing peak demand on the utility grid benefits both grid operators and consumers. However, achieving this goal while maintaining human comfort presents a significant challenge. This study proposes the deployment of an energy-efficient grid-connected solar photovoltaic (PV) and battery energy storage (BES) system to perform peak shaving. We employ an optimal rule-based control strategy that considers day-ahead PV power and load demand predictions, along with a daily load profile limit, specifically designed for the C1 tariff in Malaysian Commercial Buildings. The peak shaving control strategy proactively determines optimal schedules for battery charging and discharging, aiming to effectively minimize peak demand. To regulate the daily demand accordingly, we employ a root-finding algorithm to determine the optimal demand limit in conjunction with the formulated rule-based peak-shaving control strategy. The required optimal inputs for the rule-based control strategy are determined using a genetic algorithm, which minimizes peak grid power. We verify the effectiveness of the rule-based peak-shaving method using MATLAB. The results demonstrate significant reductions in both energy consumption and peak demand for different load profiles. Specifically, we achieve a reduction of 19.02%, 20.9%, 4.72%, and 0% in energy consumption, and 42.46%, 49.82%, 40.44%, and 33.1% in peak demand for case studies (CS) 1, 2, 3, and 4, respectively. Furthermore, we observe that the application of the rule-based peak shaving method maintains a state-of-charge (SOC) of the battery at 50% by the end of the day, allowing for day-to-day management flexibility.

Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm

The deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm.

Technical and economic analyses of PV battery systems considering two different tariff policies

Installing batteries in solar photovoltaic (PV) houses is becoming commonplace and different tariff policies give residents more options to lower their energy bills. This paper develops two rule-based control strategies to operate solar PV battery systems under fixed flat or time-of-use tariff policies, aiming to increase PV self-consumption and self-sufficiency. The battery modelling considers the charging and discharging efficiencies and the battery energy efficiency. The payback period for solar PV battery systems under the two tariff policies is also analysed considering various economic factors such as the capital cost of solar PV systems, the capital and maintenance costs of the batteries, the annual discount rate and the increases or decreases in the retail prices of grid electricity and the feed-in tariff. The analyses are conducted using actual PV energy and smart meter data from a real case study house in Geelong, Australia. Results indicate that when battery capacity is increased, PV self-consumption and self-sufficiency grow under both tariff policies, but this trend is limited by constrained PV generation due to seasonal conditions. Additionally, increasing solar PV system size for fixed battery capacity increases PV self-sufficiency, but decreases PV self-consumption. Results of economic analysis demonstrate that the payback period for a standalone solar PV system increases as its capacity grows. Moreover, the payback period for PV batteries can be slightly shorter or even longer than using solar PV systems alone under both tariff policies, which is economically unattractive. Considering the benefits that batteries can bring to residents and electricity networks, local governments need to be more proactive in providing financial subsidies for residents to install batteries.

Energy Management Strategy for P1 + P3 Plug-In Hybrid Electric Vehicles

In order to simultaneously improve the fuel economy and overall performance of plug-in hybrid electric vehicles (PHEVs), this study selected the P1 + P3 configuration as its research object. Through a configuration analysis of hybrid vehicles, it confirmed the feasibility of P1 + P3 configuration-PHEV operating modes. Based on this, a rule-based control strategy was developed, and simulation models for the entire vehicle and control strategy were constructed in both Cruise and MATLAB/Simulink software. The study conducted simulation analysis by combining three sets of Worldwide Harmonized Light vehicles Test Cycle (WLTC) driving cycles to assess the fuel-saving potential of the dual-motor P1 + P3 configuration. The simulation results showed that the vehicle model was reasonably constructed and the proposed control strategy had good control effects on the entire vehicle. Compared to conventional gasoline vehicles, the P1 + P3 configuration PHEV achieved a 67.4% fuel economy improvement, demonstrating a significant enhancement in fuel efficiency with the introduction of electric motors.

Optimal control of a solar-driven seasonal sorption storage system through deep reinforcement learning

Deep reinforcement learning (DRL) has demonstrated its effectiveness in the control of energy systems, although it has not yet been applied to sorption thermal energy storage (TES) systems. The operation of sorption TES systems is notably more complicated compared to other TES variants. The discharge of a sorption TES occurs at a particular desorption and evaporation temperature. Achieving a continuous and efficient discharge of a sorption TES is a challenging control task if heat required at the evaporator is obtained from the sun or the environment. Its operation is especially complicated during winter, because of the limited availability of solar irradiation and low ambient temperatures. Thus, this study analyzes for first time in the literature the competitiveness of deep reinforcement learning to control a solar-driven seasonal sorption TES system and compares it against traditional optimized rule-based control strategy. The system, located in Central Europe, supplied domestic hot water and space heating to a single-family house. Two DRL models were developed and trained to operate the system under two different sets of data: 120 winter consecutive days and 60 winter non-consecutive days. The results showed that the DRL control strategy reduced the system operational costs by 28% in a 60 winter days scenario. For a 120 winter days scenario, the operational cost savings decreased to 13% because the smart control performed worst once the sorption TES was fully discharged. These results were derived from a four-year validation data set, bolstering their robustness. The study demonstrates the successful application of DRL in controlling a solar-driven seasonal sorption TES system, yielding considerable economic savings compared to an RBC strategy. Subsequent work will consist of implementing the smart control strategy at prototype level to assess its performance.

A new model predictive control approach integrating physical and data-driven modelling for improved energy performance of district heating substations

District heating (DH) substations play a crucial role in ensuring the efficient and effective distribution of thermal energy necessary to provide space heating for buildings. However, optimizing their operation for energy savings while still ensuring indoor comfort poses significant challenges due to the complex dynamics of building demand and the inertia of building envelopes. To address these challenges, this study introduces a novel model predictive control (MPC) approach that combines a reduced-order physical model with a machine learning-based data-driven model to jointly optimize the operation parameters of a DH substation. In this approach, a reduced-order physical model is first used to capture essential operational principles and energy behaviors of the DH substations and generate candidate solutions for the control of the DH substations. Then, a data-driven model is constructed by integrating a Long Short-Term Memory model and a Back-propagation Neural Network, leveraging historical operational data of the DH substation concerned. The data-driven model is further formulated into a data-driven MPC framework to identify optimal control solutions from all candidates provided by the physical model. To evaluate the proposed approach, a data-driven surrogate model is developed using real operational data. Comparative analysis against the original fuzzy rule-based control strategy and a pure data-driven strategy demonstrates a substantial reduction in heat consumption of 4.77% and 19.47%, respectively. Moreover, compared with using a reduced-order physical model alone, this approach achieves additional benefits in reducing the energy consumption of the DH substation and minimizing indoor temperature fluctuations within the end-users.

Optimal battery thermal management for electric vehicles with battery degradation minimization

The control of a battery thermal management system (BTMS) is essential for the thermal safety, energy efficiency, and durability of electric vehicles (EVs) in hot weather. To address the battery cooling optimization problem, this paper utilizes dynamic programming (DP) to develop an online rule-based control strategy. Firstly, an electrical–thermal-aging model of the LiFePO4 battery pack is established. A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures. Then in the DP framework, a cost function consisting of battery aging cost and cooling-induced electricity cost is minimized to obtain the optimal compressor power. By exacting three rules ”fast cooling, slow cooling, and temperature-maintaining” from the DP result, a near-optimal rule-based cooling strategy, which uses as much regenerative energy as possible to cool the battery pack, is proposed for online execution. Simulation results show that the proposed online strategy can dramatically improve the driving economy and reduce battery degradation under diverse operation conditions, achieving less than a 2.18% difference in battery loss compared to the offline DP. Recommendations regarding battery cooling under different real-world cases are finally provided.

Demand side flexibility for a Heat Booster Substation with ultra low temperature district heating

Ultra-low temperature district heating (ULTDH) systems improve the heat distribution efficiency by reducing thermal network losses and provide an easier access to Renewable Energy Sources (RES) by utilizing them as heat sources through Power-to-Heat solutions. In this study, Domestic Hot Water (DHW) preparation at a Heat Booster Substation (HBS) supplied with ULTDH is investigated for generating demand side flexibility. The main components of the HBS are a Hot Water Storage Tank (HWST) and an electric-driven heat pump. A genetic algorithm based control strategy is developed to optimally utilize the thermal energy storage capability of the HBS by intelligently scheduling the charging of the HWST based on time-varying electricity price signals. A multi-objective control strategy is designed to reduce energy costs while generating flexibility on the demand side. The components of the HBS are numerically modeled and experimentally validated to form a dynamic non-linear model of the HBS. Further, the data-driven seasonal ARIMA model, ARIMA(0,1,1)x(0,1,1)168, is developed to forecast the DHW consumption based on a statistical analysis of the historic consumption profiles over a two-year period. The forecast RMSE of the DHW prediction model is less than 0.03kgs−1 over the two-year test period, indicating that the prediction model is able to accurately forecast the DHW consumption of the residents. With the control-oriented HBS numerical model and the DHW prediction model serving as decision support tools, the developed control strategy is implemented at the HBS. Results show that in comparison to a rule-based control strategy, the developed control strategy demonstrates significant demand side flexibility while attaining energy cost savings of more than 91% and 84% at time resolutions of 15 and 30 min for the control interval respectively. With increased volatility in electrical prices due to higher penetration of RES, greater energy cost savings of more than 150% are observed.

Control and Optimization of Pre-Transmission Parallel Hybrid Vehicle with Fuzzy Logic Method and Comparison with Conventional Rule Based Control Strategy

In this study, a model of a pre-transmission parallel hybrid vehicle was created in MATLAB/Simulink environment. A torque control strategy has been developed for the hybrid vehicle on the created model. The hybrid vehicle's torque control is provided by fuzzy logic and conventional rule-based control strategies. The control strategies of the hybrid vehicle have been developed based on four input parameters: accelerator and brake pedal position, battery state of charge and the operating mode of the electric motor. The regenerative braking system of the pre-transmission parallel hybrid vehicle has been provided with a fuzzy logic control strategy in both vehicle structures. In the fuzzy logic torque split controller, a control algorithm has been created in which the safety, performance and fuel consumption values of the vehicle meet at the optimum point. Under different driving cycle conditions, the effects of control strategies on engine operating points have been examined and average fuel consumption values have been obtained. In the control of the parallel hybrid vehicle with fuzzy logic method, it has been obtained that it provides 12.87%, 6.62%, 2.27% and 6.23% fuel savings under FTP-75, NEDC, EUDC and ECE-15 driving cycle conditions, respectively. It has been observed that the vehicle cannot provide sufficient performance in US06 driving cycle conditions with the use of conventional rule-based control strategy.