Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain
Abstract
:1. Introduction
2. Hybrid Powertrain System Configuration and Modeling
2.1. Hybrid Powertrain Configuration
2.2. System Modeling
2.2.1. Engine Model
2.2.2. Motor Model
2.2.3. Battery Model
2.2.4. Longitudinal Dynamics Model
2.2.5. Driver Model
3. Energy Management Control Strategies
3.1. Energy Management-Based Ruled
3.2. Approximate-ECMS Strategy
3.2.1. Power Allocation Factor
3.2.2. Basic of ECMS
3.2.3. Approximate-ECMS
3.2.4. Flow Diagram of the Approximate-ECMS
4. Equivalent Factor Optimization Based on DPSO-GA
4.1. Hybrid DPSO-GA-Based Optimization Algorithm
4.1.1. Basic Principle of Dynamic Particle Swarm Optimization
4.1.2. Procedures of DPSO-GA
- Initialize particle swarm including swarm number N, particle position , and velocity . Initialize and of the particles, which are the initial value of the equivalent factor and its variation, respectively.
- The objective function: The goal of optimization is to achieve equivalent fuel consumption under typical driving cycles and to keep the difference between the final value of and the target value of SOC to a minimum, as shown in Equation (44).
- Select particles based on ranked fitness and update: After initializing the particle swarm, the particle fitness is ranked according to the objective function. The function with high fitness is selected for particle swarm algorithm update based on Equation (43) and for low fitness values a genetic algorithm update is performed.
- End: Set the termination condition. If the termination condition is not satisfied, execute steps 2–3 until the particle search satisfies the termination condition. Then obtain the best solution.
5. Simulation Results and Discussion
5.1. Model and Settings of the Vehicle
5.2. Comparison of Rule-EMS, Basic-ECMS, PSO-ECMS, and Approximate-ECMS
5.3. Optimization of Based on DPSO-GA
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Operation Mode | Conditions | Torque Distribution |
---|---|---|
1. Stop mode | ||
2. Motor-only mode | ||
3. Engine-only mode | ||
4. Hybrid mode | ||
5. Recharging mode | ||
6. Regenerative braking mode | ||
Component | Parameters | Value |
---|---|---|
Engine | Engine type | 1.9L.SI |
Maximum Power | 63 kW @ 5500 rpm. | |
Peak Torque | 145 Nm @ 2000 rpm. | |
Motor | Motor type | Permanent magnet motor |
Maximum power | 25 kW | |
Battery | Battery type | Lithium–ion |
Capacity | 25 Ah | |
Vehicle | Vehicle mass | 1350 kg |
Radius of tire | 0.282 m | |
Vehicle front area | 2 m | |
Rolling resistance coefficient | 0.014 | |
Aerodynamic drag coefficient | 0.335 |
Strategy | (L/km) | (L/km) | Improved(%) |
---|---|---|---|
Rule-EMS | 8.387 | 6.037 | 0 |
Basic-ECMS | 7.144 | 4.793 | 17.40 |
PSO-ECMS | 7.088 | 4.734 | 18.32 |
Approximate-ECMS | 7.135 | 4.787 | 17.55 |
(L/km) | (L/km) | Improved | |||
---|---|---|---|---|---|
Before optimization | 2.75 | 7.135 | 4.787 | 0.0541 | 0 |
After optimization | 3.10 | 6.499 | 4.354 | 0.1205 | 9.04% |
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Qiang, P.; Wu, P.; Pan, T.; Zang, H. Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain. Energies 2021, 14, 7919. https://doi.org/10.3390/en14237919
Qiang P, Wu P, Pan T, Zang H. Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain. Energies. 2021; 14(23):7919. https://doi.org/10.3390/en14237919
Chicago/Turabian StyleQiang, Penghui, Peng Wu, Tao Pan, and Huaiquan Zang. 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain" Energies 14, no. 23: 7919. https://doi.org/10.3390/en14237919
APA StyleQiang, P., Wu, P., Pan, T., & Zang, H. (2021). Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain. Energies, 14(23), 7919. https://doi.org/10.3390/en14237919