The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle
<p>Structure of the fuel cell hybrid electric vehicle.</p> "> Figure 2
<p>Motor efficiency map.</p> "> Figure 3
<p>PEMFC system efficiency.</p> "> Figure 4
<p>Battery Rint model.</p> "> Figure 5
<p>(<b>a</b>) Fitness value searching for balanced optimization. (<b>b</b>) Fitness value searching for battery performance degradation preferred optimization. (<b>c</b>) Fitness value searching for system cost preferred optimization. (<b>d</b>) Fitness value searching for hydrogen consumption preferred optimization.</p> "> Figure 5 Cont.
<p>(<b>a</b>) Fitness value searching for balanced optimization. (<b>b</b>) Fitness value searching for battery performance degradation preferred optimization. (<b>c</b>) Fitness value searching for system cost preferred optimization. (<b>d</b>) Fitness value searching for hydrogen consumption preferred optimization.</p> "> Figure 6
<p>(<b>a</b>) Fitness value searching for balanced optimization. (<b>b</b>) Fitness value searching for battery performance degradation preferred optimization. (<b>c</b>) Fitness value searching for system cost preferred optimization (<b>d</b>) Fitness value searching for hydrogen consumption preferred optimization.</p> "> Figure 7
<p>DDPG algorithm for EMS optimization.</p> "> Figure 8
<p>Training process of EMS agent with weighting factors a<sub>1</sub> = 0.34, a<sub>2</sub> = 0.33, a<sub>3</sub> = 0.33.</p> "> Figure 9
<p>Power distribution between FC and battery in WLTC in selected training episode with weighting factors a<sub>1</sub> = 0.34, a<sub>2</sub> = 0.33, a<sub>3</sub> = 0.33 (<b>a</b>) Complete cycle; (<b>b</b>) Part of the cycle.</p> "> Figure 10
<p>Training process of EMS agent with weighting factors a<sub>1</sub> = 0.2, a<sub>2</sub> = 0.2, a<sub>3</sub> = 0.6.</p> "> Figure 11
<p>Power distribution between FC and battery in WLTC in selected training episode with weighting factors a<sub>1</sub> = 0.2, a<sub>2</sub> = 0.2, a<sub>3</sub> = 0.6, (<b>a</b>) Complete cycle; (<b>b</b>) Part of the cycle.</p> "> Figure 11 Cont.
<p>Power distribution between FC and battery in WLTC in selected training episode with weighting factors a<sub>1</sub> = 0.2, a<sub>2</sub> = 0.2, a<sub>3</sub> = 0.6, (<b>a</b>) Complete cycle; (<b>b</b>) Part of the cycle.</p> "> Figure 12
<p>Power distribution between FC and battery in UDDS in selected training episode with weighting factors a<sub>1</sub> = 0.34, a<sub>2</sub> = 0.33, a<sub>3</sub> = 0.33.</p> "> Figure 13
<p>Power distribution between FC and battery in UDDS in selected training episode with weighting factors a<sub>1</sub> = 0.2, a<sub>2</sub> = 0.2, a<sub>3</sub> = 0.6.</p> ">
Abstract
:1. Introduction
1.1. The Need for a Hybrid System
1.2. Review of EMS Development for FC EV
2. Optimization of FC–Battery Powertrain Configurations
2.1. Structure and Specifications
2.2. Electric Machine
2.3. Fuel Cell Model
2.4. Battery Model
2.5. System Configurations
3. Optimum Design of Hybrid Powertrain
3.1. Method
Algorithm 1 PSO-based multi-objective DoH optimization |
1: for each particle i |
2: for each dimension j |
3: Initialize velocity Vij and position Xij for particle i |
4: Calculate the fitness value fit(Xij) and set p_bestij = Xij, |
5: end for |
6: end for |
7: Choose the particle having the best fitness value as the g_bestj |
8: for iteration N = 2, M do |
9: for each particle i |
10: for each dimension j |
11: Updata the velocity of particle i: |
12: |
13: Updata the position of particle i: |
14: |
15: end for |
16: if ) |
17: |
18: end if |
19: if ) |
20: |
21: end if |
22: end for |
23: end for |
24: print the last g_best value |
3.2. Concept Design Optimization Results
4. RL EMS
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Value | Unit |
---|---|---|
Front area | 2.18 | m2 |
Aerodynamic drag coefficient | 0.32 | N/A |
Coefficient of rolling resistance | 0.0105 | N/A |
Equipped mass | 1750 | kg |
Correction coefficient of rotating mass | 1.05 | N/A |
Acc performance (0–100 km/h) | 10 | s |
Hill-climbing capability | 30@30 km/h | ° (degree) |
Top speed | 120 | km/h |
Parameter | Value | Unit |
---|---|---|
Rated power | 50 | kW |
Peak power | 120 | kW |
Rated speed | 5500 | rpm |
Max speed | 14,000 | rpm |
Rated torque | 90 | Nm |
Max torque | 215 | Nm |
Parameter | Variable | Value | Unit |
---|---|---|---|
Cell numbers | N | 80 | |
Peak power | Pfc,Max | 45 | kW |
Peak current | Ist,Max | 300 | A |
Stack mass | FCMass | 13.1 | kg |
Battery Parameters | DoH < Threshold | DoH > Threshold |
---|---|---|
Energy density | 0.156 kWh/kg | 0.156 kWh/kg |
Power density | 2.4 kW/kg | 0.96 kW/kg |
Weight | 31.25 kg | 125 kg |
Voltage | 375 V | 375 V |
DoH Range | Weight Coefficients | Optimal DoH | Fitness Value |
---|---|---|---|
[0, 0.375] | a1 = 0.34, a2 = 0.33, a3 = 0.33 | 0.2016 | 1.1088 |
a1 = 0.2, a2 = 0.2, a3 = 0.6 | 0.2323 | 0.6703 | |
a1 = 0.2, a2 = 0.6, a3 = 0.2 | 0.0347 | 0.7399 | |
a1 = 0.6, a2 = 0.2, a3 = 0.2 | 0.3493 | 1.711 | |
[0.375, 1] | a1 = 0.34, a2 = 0.33, a3 = 0.33 | 0.3755 | 1.1337 |
a1 = 0.2, a2 = 0.2, a3 = 0.6 | 0.3821 | 0.6794 | |
a1 = 0.2, a2 = 0.6, a3 = 0.2 | 0.3750 | 0.8507 | |
a1 = 0.6, a2 = 0.2, a3 = 0.2 | 0.3750 | 1.7060 |
Weighting Factors (a1,a2,a3) | Equivalent Hydrogen Consumption | Battery Capacity Degradation | FC Performance Degradation |
---|---|---|---|
0.34–0.3–0.33 | 0.558 kg | 0.0126 | 0.0073 |
0.2–0.2–0.6 | 0.629 kg | 0.0142 | 0.0068 |
Weighting Factors (a1,a2,a3) | Equivalent Hydrogen Consumption | Battery Capacity Degradation | FC Performance Degradation |
---|---|---|---|
0.34–0.3–0.33 | 0.4284 kg | 0.0036 | 0.0618 |
0.2–0.2–0.6 | 0.6447 kg | 0.0061 | 0.0535 |
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Zhou, J.; Feng, C.; Su, Q.; Jiang, S.; Fan, Z.; Ruan, J.; Sun, S.; Hu, L. The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle. Sustainability 2022, 14, 6320. https://doi.org/10.3390/su14106320
Zhou J, Feng C, Su Q, Jiang S, Fan Z, Ruan J, Sun S, Hu L. The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle. Sustainability. 2022; 14(10):6320. https://doi.org/10.3390/su14106320
Chicago/Turabian StyleZhou, Jiaming, Chunxiao Feng, Qingqing Su, Shangfeng Jiang, Zhixian Fan, Jiageng Ruan, Shikai Sun, and Leli Hu. 2022. "The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle" Sustainability 14, no. 10: 6320. https://doi.org/10.3390/su14106320