Study on Driver-Oriented Energy Management Strategy for Hybrid Heavy-Duty Off-Road Vehicles under Aggressive Transient Operating Condition
Abstract
:1. Introduction
- A nonlinear autoregressive with exogenous input (NARX) neural network is trained to recognize the driver’s driving intention and translate it into the expected subsequent vehicle speed trajectory, which will be used as the tracking reference of the MPC controller.
- A nonlinear MPC controller is designed to track the reference vehicle speed and realize energy management. By using MPC, the driving force and engine power are co-optimized, which allows engine power to follow demand power well so that battery current overload is avoided. The penalty weights are used to coordinate the variations in driving force and engine power, which leads to improvement of engine power smoothing and further decrease in transient battery current. The weights in the objective function are adaptively selected according to the vehicle working condition classification to ensure that the demand power variation can match the engine dynamic characteristic under different transient conditions.
- The effectiveness of the proposed strategy is verified through simulation and the hardware-in-the-loop (HiL) test in an actual off-road driving cycle with drastic power fluctuations.
2. Simulation-Oriented Vehicle Model
2.1. Engine Model
2.1.1. Turbocharger Model
2.1.2. Intercooler Model
2.1.3. Intake and Exhaust Manifold Model
2.1.4. Cylinder Model
2.1.5. Engine Model Validation
2.2. Generator and Driving Motor Model
2.3. Battery Model
2.4. Vehicle Longitudinal Dynamics Model
2.5. Control Problem Analysis
2.5.1. Driving Cycle Used for Simulation
2.5.2. Results of Filter-Based Energy Management
3. Co-Optimization-Based Driver-Oriented Energy Management Strategy
3.1. Driving Intention Recognition Using a NARX Neural Network
3.2. Road Slope Estimation Based on Luenberger Observer
3.3. Working Condition Classification
3.4. MPC Design
4. Results and Analysis
4.1. Simulation Results
4.2. Hardware-in-the-Loop Test
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Description |
---|---|
Curb weight | 30,000 kg |
Rolling resistance coefficient | 0.1 |
Frontal area | 4 m2 |
Drag coefficient | 1 |
Rotating mass conversion factor | 1.2 |
Length × width × height | 7.5 m × 3 m × 2 m |
Engine | 8 V diesel engine; normal power: 550 kW |
Gear box | Ratio: 1.9 |
Generator | Permanent magnet synchronous motor; normal power: 500 kW; peak power: 600 kW |
Motor | Permanent magnet synchronous motor; normal power: 200 kW; peak power: 313 kW |
Battery | Lithium iron phosphate; layout: 2P246S; capacity: 96 Ah; voltage: 900 V |
Transmission | Dual-speed mechanical transmission; ratios: 28.78, 12.02 |
Number | Feature Parameter |
---|---|
1 | Average speed |
2 | Maximum speed |
3 | Speed span |
4 | Average acceleration |
5 | Maximum acceleration |
6 | Accelerated proportion |
7 | Average deceleration |
8 | Maximum deceleration |
9 | Deceleration proportion |
10 | Road slope |
Working Condition Feature | Example |
---|---|
1 Low power demand (0 < P < 143 kW) | |
2 Parking (P = 0) | |
3 High power demand (435 kW < P < 550 kW) | |
4 Medium power demand (143 kW <P < 435 kW) |
Classification | |||||
---|---|---|---|---|---|
1 | 14 | 10 | 2 | 9 | 4 |
2 | 14 | 10 | 2 | 9 | 4 |
3 | 13 | 17 | 2 | 11 | 4 |
4 | 14 | 15 | 2 | 10 | 4 |
Engine Speed Standard Deviation | Lowest Fuel Consumption Zone Probability (%) | Fuel Consumption (L) | Final SoC (%) | Equivalent Fuel Consumption (L) | |
---|---|---|---|---|---|
Filter-based control | 223.3509 | 42 | 21.06 | 75.80 | 21.058 |
Sequential optimization | 163.3545 | 53 | 19.96 | 75.05 | 19.959 |
Co-optimization | 136.9377 | 55 | 19.33 | 75.12 | 19.327 |
Component | Configuration |
---|---|
Real-time target machine | DSPACE PX10 simulation platform |
VCU | DSPACE MicroAutoBox Ⅱ |
Target machine host PC | Intel(R) Core(TM) i7-10700 CPU @ 2.90 GHz 32 GB RAM |
VCU host PC | Intel(R) Core(TM) i5-5200U CPU @ 2.20 GHz 8 GB RAM |
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Share and Cite
Wang, X.; Huang, Y.; Wang, J. Study on Driver-Oriented Energy Management Strategy for Hybrid Heavy-Duty Off-Road Vehicles under Aggressive Transient Operating Condition. Sustainability 2023, 15, 7539. https://doi.org/10.3390/su15097539
Wang X, Huang Y, Wang J. Study on Driver-Oriented Energy Management Strategy for Hybrid Heavy-Duty Off-Road Vehicles under Aggressive Transient Operating Condition. Sustainability. 2023; 15(9):7539. https://doi.org/10.3390/su15097539
Chicago/Turabian StyleWang, Xu, Ying Huang, and Jian Wang. 2023. "Study on Driver-Oriented Energy Management Strategy for Hybrid Heavy-Duty Off-Road Vehicles under Aggressive Transient Operating Condition" Sustainability 15, no. 9: 7539. https://doi.org/10.3390/su15097539