Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.

Section: Techniques Applied To Apps In Phevsmentioning

confidence: 99%

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Recalde,

Cajo,

Velasquez

et al. 2024

“…Hua et al proposed an estimation model for electric vehicle energy consumption based on both vehicle parameters, such as speed and environmental data (e.g., GPS position and temperature), with a machine learning algorithm [34]. In addition, ML has been widely used to control the fuel economy of hybrid electric vehicles [35][36][37][38]. Harold et al developed a framework that would allow supervised machine learning to automatically retrain the supervisory powertrain control approach for hybrid electric vehicles [35].…”

Section: Introductionmentioning

confidence: 99%

“…This framework consists of a combination of dynamic programming and supervised machine learning based on fuel consumption. Son et al proposed an advanced rule-based control strategy with ML taking into account the driving cycle characteristics and present battery state of charge (SOC) for plug-in hybrid electric vehicles [36]. Madziel et al suggest a method for developing CO 2 emission models that require little computational effort and produce workable outcomes for fully hybrid vehicles [37].…”

Section: Introductionmentioning

confidence: 99%

Determination of Energy Savings via Fuel Consumption Estimation with Machine Learning Methods and Rule-Based Control Methods Developed for Experimental Data of Hybrid Electric Vehicles

Arıkuşu,

Bayhan,

Tiryaki

2023

In this study, a parallel hybrid electric vehicle produced within the scope of our project titled “Development of Fuel Efficiency Enhancing and Innovative Technologies for Internal Combustion Engine Vehicles” has been modeled. Firstly, a new rule-based control method is proposed to minimize fuel consumption and carbon emission values in driving cycles in the experimental model of the parallel hybrid electric vehicle produced within the scope of this project. The proposed control method ensures that the internal combustion engine (ICE) operates at the optimum point. In addition, the electric motor (EM) is activated more frequently at low speeds, and the electric motor can also work as a generator. Then, a new dataset was also created on a traffic-free racetrack with the proposed control method for fuel consumption estimation of a parallel hybrid electric vehicle using ECE-15 (Urban Driving Cycle), EUDC (Extra Urban Driving Cycle), and NEDC (New European Driving Cycle) driving cycles. The data set is dependent on 11 different input variables, which complicates the system. Afterward, the fuel estimation process is made with seven different machine learning methods (ML), and these methods are compared using the obtained data set. To avoid overfitting machine learning, two different test data sets were created. The Random Forest algorithm is the most suitable technique in terms of training and testing the fuel consumption model using correlation coefficient (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) simulation appropriateness for both test datasets. Moreover, the random forest algorithm achieved an impressive accuracy of 97% and 90% for both test datasets, outperforming the other algorithms. Furthermore, the proposed method consumes 4.72 L of fuel per 100 km, while the gasoline-powered vehicle consumes 7 L of fuel per 100 km. The results show that the proposed method emits 4.69 kg less CO2 emissions. The effectiveness of the Random Forest Algorithm has been verified by both simulation results and real-world data.

“…The rule-based EMS is practical because of its simple structure and reliable control performance. The most typical one is the Charge Depletion-Charge Sustaining(CD-CS) strategy [9]. In the CD phase, the power required by the vehicle is provided by the battery and ICE is started only when the required drive power exceeds the peak output of the motor.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Energy Management Strategy Based on Driving Conditions Using a Feature Fusion Extreme Learning Machine

Qiang

Pan

et al. 2022

To address the problem that a single energy management strategy cannot adapt to complex driving conditions, in this paper, a real-time energy management strategy for different driving conditions is proposed to improve fuel economy. First, in order to improve the accuracy and stability of the driving condition identifier, a feature fusion extreme learning machine (FFELM) is used for identification. Secondly, equivalent consumption minimization strategy (ECMS) offline optimization is conducted for different types of driving cycles, and the effect of driving cycle type and driving distance on the energy management strategy under the optimization result is analyzed. A real-time energy management strategy combining driving cycle type, driving distance, and optimal power allocation factor is proposed. To demonstrate the effectiveness of the proposed strategy, combined driving cycles were used for testing. The simulation results show that the proposed strategy can improve the equivalent fuel consumption by 10.21% compared to the conventional strategy CD-CS. The equivalent fuel economy can be improved by 2.5% compared to the single ECMS strategy with the less computational burden. Thus, it is demonstrated that the proposed strategy can be effectively adapted to different driving conditions and shows better real-time and economic performance.