Optimizing mechanical energy transfer in hybrid vehicle engines is a crucial aspect of improving fuel efficiency and overall vehicle performance. This comprehensive guide delves into the various strategies and techniques that can be employed to achieve this optimization, backed by measurable and quantifiable data from recent studies.
Power-Split Hybrid Electric Vehicle (PS-HEV) Optimization
Power-split hybrid electric vehicles (PS-HEVs), such as the Toyota Prius, utilize a planetary gear set to distribute the engine power between the drive wheels and the generator/motor. By optimizing the gear ratios and controlling the engine and motor speeds, significant improvements in fuel efficiency can be achieved.
One study by Hyeonjun Lee et al. proposed an optimization method for PS-HEVs using dynamic programming (DP) to consider the moment of inertia of the power-split device (PSD). The results showed a 3.5% reduction in WLTC (Worldwide Harmonized Light Vehicles Test Cycle) rated fuel consumption compared to a conventional power-split architecture, and a 2.0% reduction compared to a P2 architecture.
The optimization process can be represented by the following mathematical model:
Minimize: Fuel Consumption
Subject to:
Engine Speed Constraint: ω_e_min ≤ ω_e ≤ ω_e_max
Motor Speed Constraint: ω_m_min ≤ ω_m ≤ ω_m_max
Torque Constraint: T_e_min ≤ T_e ≤ T_e_max
T_m_min ≤ T_m ≤ T_m_max
Power-Split Ratio Constraint: 0 ≤ r_ps ≤ 1
where ω_e, ω_m, T_e, T_m, and r_ps represent the engine speed, motor speed, engine torque, motor torque, and power-split ratio, respectively. The optimization aims to minimize the fuel consumption while satisfying the various constraints on the system parameters.
Transmission System Parameters Optimization
The optimization of the transmission system parameters in hybrid electric vehicles can also contribute to improved mechanical energy transfer. This approach includes the optimization of transmission ratios as decision variables.
A study by Zhang et al. focused on the power transmission optimization of a dual-motor coupling drive for pure electric cars based on a genetic algorithm. The same principles can be applied to hybrid vehicle transmission systems.
The optimization problem can be formulated as follows:
Minimize: Power Loss
Subject to:
Gear Ratio Constraint: r_1_min ≤ r_1 ≤ r_1_max
r_2_min ≤ r_2 ≤ r_2_max
Torque Constraint: T_1_min ≤ T_1 ≤ T_1_max
T_2_min ≤ T_2 ≤ T_2_max
Speed Constraint: ω_1_min ≤ ω_1 ≤ ω_1_max
ω_2_min ≤ ω_2 ≤ ω_2_max
where r_1, r_2, T_1, T_2, ω_1, and ω_2 represent the gear ratios, torques, and speeds of the two motors, respectively. The optimization aims to minimize the power loss while satisfying the constraints on the transmission system parameters.
Multi-Objective Design Optimization of Hybrid Electric Vehicle Transmission System
This approach utilizes genetic algorithms to optimize the transmission system parameters of hybrid electric vehicles for multiple objectives, such as fuel efficiency and performance.
A study by Fatemi-Anaraki et al. used a hybrid of K-means and genetic algorithm to solve a bi-objective green delivery and pick-up problem, which can be adapted for hybrid vehicle energy consumption optimization. The optimization problem can be formulated as follows:
Minimize: Fuel Consumption, Vehicle Mass
Subject to:
Gear Ratio Constraint: r_1_min ≤ r_1 ≤ r_1_max
r_2_min ≤ r_2 ≤ r_2_max
Motor Power Constraint: P_m_1_min ≤ P_m_1 ≤ P_m_1_max
P_m_2_min ≤ P_m_2 ≤ P_m_2_max
Battery Capacity Constraint: E_bat_min ≤ E_bat ≤ E_bat_max
where r_1, r_2, P_m_1, P_m_2, and E_bat represent the gear ratios, motor powers, and battery capacity, respectively. The optimization aims to minimize both the fuel consumption and the vehicle mass while satisfying the various constraints on the transmission system parameters and powertrain components.
Energy Consumption Optimization Control Strategy Based on NSGA-II Genetic Algorithm
This method utilizes a genetic algorithm to optimize the energy consumption of hybrid vehicles based on the NSGA-II (Non-dominated Sorting Genetic Algorithm II) multi-objective optimization algorithm.
A study by Rabbani et al. used this approach for a bioenergy supply chain optimization problem, which can be adapted for hybrid vehicle energy consumption optimization. The optimization problem can be formulated as follows:
Minimize: Energy Consumption, Emissions
Subject to:
State of Charge (SOC) Constraint: SOC_min ≤ SOC ≤ SOC_max
Power Demand Constraint: P_dem_min ≤ P_dem ≤ P_dem_max
Engine Power Constraint: P_e_min ≤ P_e ≤ P_e_max
Motor Power Constraint: P_m_min ≤ P_m ≤ P_m_max
where SOC, P_dem, P_e, and P_m represent the state of charge, power demand, engine power, and motor power, respectively. The optimization aims to minimize both the energy consumption and emissions while satisfying the various constraints on the powertrain components and operating conditions.
Genetic Algorithm-Based Fuzzy Optimization of Energy Management Strategy for Fuel Cell Vehicles Considering Driving Cycles
This approach uses a genetic algorithm to optimize the energy management strategy of fuel cell hybrid vehicles based on driving cycles. While the study by Zhang et al. focused on fuel cell hybrid vehicles, the same principles can be applied to hybrid vehicles in general.
The optimization problem can be formulated as follows:
Minimize: Fuel Consumption, Emissions
Subject to:
Power Balance Constraint: P_fc + P_bat = P_dem
Battery State of Charge Constraint: SOC_min ≤ SOC ≤ SOC_max
Fuel Cell Power Constraint: P_fc_min ≤ P_fc ≤ P_fc_max
Battery Power Constraint: P_bat_min ≤ P_bat ≤ P_bat_max
where P_fc, P_bat, P_dem, SOC, P_fc_min, P_fc_max, P_bat_min, and P_bat_max represent the fuel cell power, battery power, power demand, state of charge, fuel cell power limits, and battery power limits, respectively. The optimization aims to minimize both the fuel consumption and emissions while satisfying the various constraints on the powertrain components and operating conditions.
By implementing these strategies and techniques, hybrid vehicle manufacturers and researchers can significantly optimize the mechanical energy transfer in hybrid vehicle engines, leading to improved fuel efficiency, reduced emissions, and enhanced overall vehicle performance.
References
- Jui Julakha Jahan Ahmad Mohd Ashraf Molla M.M. Imran Rashid Muhammad Ikram Mohd, “Optimal energy management strategies for hybrid electric vehicles: A recent survey of machine learning approaches”, ScienceDirect, 2024.
- Hyeonjun Lee, Sung-ho Hwang, “Optimization Method of Power-Split Hybrid Electric Vehicle Considering Moment of Inertia of PSD”, 33rd Electric Vehicle Symposium (EVS33), Portland, Oregon, June 14 – 17, 2020.
- Q. Zhang, X.J. Wu, Y. Yuan, S. Xu, Z. Lu, “Research on power transmission optimisation of dual motor coupling drive for pure electric car based on genetic algorithm”, International Journal of Vehicle System Modelling and Testing, 2022.
- Rabbani, M., Akbarian-Saravi, N., Ansari, M., Musavi, M., “A bi-objective vehicle-routing problem for optimization of a bioenergy supply chain by using NSGA-II algorithm”, Journal of Quality Engineering, Production and Operations Management, 2020.
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