# Adaptive Fuzzy Power Management Strategy for Extended-Range Electric Logistics Vehicles Based on Driving Pattern Recognition

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## Abstract

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## 1. Introduction

_{2}emissions, and effectively adapting to uncertainties and complex driving conditions [6,7]. To accomplish these objectives, the EMSs in ERELVs require access to real-time information about the driving conditions. This information can be obtained from various sources such as internet maps, global positioning systems (GPS), geographical information systems (GIS), or intelligent transportation systems (ITS) [8]. By integrating these data sources into the EMSs, ERELVs can make informed decisions regarding power allocation, optimizing the utilization of available energy sources, and adapting to the specific driving conditions in real time. This intelligent coordination and management of energy sources contribute to maximizing the overall efficiency and performance of ERELVs in terms of fuel economy and environmental sustainability.

#### 1.1. Literature Review

#### 1.2. Motivation and Innovation

#### 1.3. Article Overview

## 2. ERELV Powertrain Modelling

#### 2.1. Vehicle Structure and Longitudinal Dynamics Model

#### 2.2. Engine Model

#### 2.3. Electric Machine Model

_{f}, speed, and torque is described by the following equation:

#### 2.4. Battery Model

## 3. Adaptive Fuzzy Power Management Strategy

#### 3.1. Driving Pattern Recognition Based on Multi-Dimensional Features

#### 3.1.1. Multi-Domain Feature Extraction Based on the Variational Mode Decomposition Algorithm

#### 3.1.2. Deep Learning Driving Pattern Identification Method

_{t}

_{−1}is the information of the memory unit at the previous moment.

#### 3.2. Adaptive Fuzzy Energy Management Strategy

## 4. Results and Discussion

#### 4.1. Driving Pattern Recognition Performance Verification

^{2}. Contrastingly, in the second type of training data, the same kinematic segment showcases an average speed of 11 km/h, a maximum speed of 41.60 km/h, and an average acceleration of 0.4988 m/s

^{2}. These observations underscore the distinct kinematic characteristics between different types. In the third category, the sixth kinematic segment stands out with an average speed of 47.40 km/h, a maximum speed of 76.36 km/h, and an average acceleration of 0.2669 m/s

^{2}. However, it is crucial to note that when using fixed historical time periods as input for the recognition model in practical applications, the differences in time-domain statistical characteristics may not be readily apparent, leading to lower recognition accuracy. Thus, it becomes imperative to incorporate multi-dimensional and multi-domain features such as time-frequency characteristics, accelerator pedal signals, and brake pedal signals.

#### 4.1.1. The Impact of Time-Frequency Domain Feature Estimates on Recognition Performance

#### 4.1.2. Comparison of the Two Identification Methods

#### 4.1.3. The Impact of Different Types of Features on Recognition Performance

#### 4.2. Performance Comparison of Energy Management Strategies

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

EMS | Energy Management Strategy | GIS | Geographical Information Systems |

ERELV | Extended-Range Electric Logistics Vehicle | ELM | Extreme Learning Machine |

GPS | Global Positioning Systems | ||

DP | Dynamic Programming | RB-EMS | Rule-basedEnergy Management Strategy |

DPR | Driving Pattern Recognition | SOC | State of Charge |

LSTM | Long Short-Term Memory Neural Network | PHEV | Plug-in Hybrid Electric Vehicle |

ITS | Intelligent Transportation Systems | VMD | Variational Mode Decomposition Algorithm |

Nomenclature | |||

${\eta}_{T}$ | Transmission Efficiency | ${r}_{roll}$ | Rolling Radius |

${i}_{T}$ | Transmission Ratio | ${Q}_{e}$ | Fuel Consumption |

${T}_{P}$ | Driving Torque | ${T}_{E}(t)$, ${\omega}_{E}(t)$ | Engine Torque and Speed |

${T}_{b}$ | Brake Torque | ${b}_{E}(t)$ | Engine Fuel Consumption Rate |

m | Overall Mass of Vehicle | ${\rho}_{f}$ | Fuel Specific Gravity |

$\delta $ | Rotational Mass Coefficient | ${\eta}_{M}$ | Electrical Circuitry Efficiency |

$v$ | Vehicle Speed | ${U}_{VOC}$ | Open Circuit Voltage |

${\rho}_{\mathrm{a}}$ | Air Density | ${\omega}_{k}$ | Mode Center Frequency |

$\alpha $ | Road Slope | ${\widehat{u}}_{k}^{n+1}(\omega )$ | Fourier Transform Component |

$f$ | Coefficient of Rolling Resistance | ${W}_{i}$, ${W}_{f}$, ${W}_{a}$ | Input Weight Vector |

${C}_{D}$ | Air Resistance Coefficient | ${u}_{f}$ | Fuzzy Controller Output |

S | Wind Area | ${r}_{roll}$ | Rolling Radius |

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**Figure 1.**Schematic diagram of (

**a**) hybrid powertrain structure; (

**b**) forces on a vehicle in motion; and (

**c**) vehicle.

**Figure 7.**Fuzzy energy management strategy framework for working condition recognition using a neural network.

**Figure 12.**Comparison of results at different recording times: (

**a**) recognition rate; (

**b**) overfitting.

**Figure 14.**Engine operating point distribution: (

**a**) fuzzy strategy; (

**b**) fuzzy strategy under T + F + A + B features; and (

**c**) fuzzy strategy under T + F features.

Components | Description | |
---|---|---|

Final reduction drive | Final ratio | 6.143 |

Engine | Maximum power (kW) | 108 |

Electric machine | Maximum power (kW) | 100 |

Battery | Rate capacity (Ah) | 37 |

Vehicle | Vehicle mass (kg) | 6000 |

${C}_{D}$ | 0.335 | |

S (m^{2}) | 2 | |

Roll coefficient | 0.0125 | |

Wheel radius (m) | 0.376 | |

Rotational mass coefficient | 1.06 |

Required Power | SOC | ||
---|---|---|---|

L | M | H | |

L | VL | L | M |

M | L | M | H |

H | M | H | VH |

Recognition Step Size | LSTM Recognition Rate | ELM Recognition Rate | LSTM Overfitting Degree | ELM Overfitting Degree |
---|---|---|---|---|

5 s | 0.980 | 0.960 | 0.020 | 0.040 |

10 s | 0.973 | 0.953 | 0.027 | 0.047 |

15 s | 0.964 | 0.940 | 0.032 | 0.051 |

20 s | 0.955 | 0.920 | 0.031 | 0.068 |

Feature Type | Recognition Step | Recognition Rate | Overfitting Degree |
---|---|---|---|

T | 5 s | 0.9171 | 0.0828 |

10 s | 0.8941 | 0.1014 | |

15 s | 0.8872 | 0.1057 | |

20 s | 0.8783 | 0.1129 | |

T + F | 5 s | 0.9675 | 0.0323 |

10 s | 0.9566 | 0.0386 | |

15 s | 0.9300 | 0.0624 | |

20 s | 0.9235 | 0.0671 | |

T + A | 5 s | 0.9226 | 0.0772 |

10 s | 0.9203 | 0.0751 | |

15 s | 0.9002 | 0.0925 | |

20 s | 0.8914 | 0.0996 | |

T + B | 5 s | 0.9232 | 0.0766 |

10 s | 0.9047 | 0.0907 | |

15 s | 0.8958 | 0.0969 | |

20 s | 0.8894 | 0.1016 | |

T + F + A + B | 5 s | 0.9803 | 0.0195 |

10 s | 0.9728 | 0.0223 | |

15 s | 0.9644 | 0.0278 | |

20 s | 0.9554 | 0.0348 |

Abscissa of Vertex | Urban | Suburban | Expressways |
---|---|---|---|

x_{1} | 0.7523 | 0.7336 | 0.4690 |

x_{2} | 0.8949 | 0.3011 | 0.0873 |

x_{3} | 0.8418 | 0.4956 | 0.8287 |

x_{4} | 0.1309 | 0.2582 | 0.6859 |

x_{5} | 0.1892 | 0.7329 | 0.2673 |

x_{6} | 0.1536 | 0.1168 | 0.9695 |

x_{7} | 0.0289 | 0.7460 | 0.1838 |

x_{8} | 0.0091 | 0.8098 | 0.2999 |

x_{9} | 0.5965 | 0.7452 | 0.4112 |

x_{10} | 0.6090 | 0.3371 | 0.2365 |

x_{11} | 0.9189 | 0.5843 | 0.1951 |

Different Strategies | Fuel Consumption (g) | SOC_{end} | Number of Engine Starts | Engine on Time | Average Rate of Change of Battery Power (kw/s) |
---|---|---|---|---|---|

Fuzzy | 2408 | 0.3013 | 177 | 1057 | 6.67 |

T + F + A + B | 2268 | 0.3010 | 165 | 973 | 6.48 |

T + F | 2294 | 0.3014 | 165 | 974 | 6.53 |

T | 2327 | 0.3007 | 166 | 971 | 6.62 |

T + B | 2307 | 0.3006 | 170 | 1011 | 6.53 |

T + A | 2316 | 0.3013 | 165 | 983 | 6.60 |

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**MDPI and ACS Style**

Wei, C.; Wang, X.; Chen, Y.; Wu, H.; Chen, Y.
Adaptive Fuzzy Power Management Strategy for Extended-Range Electric Logistics Vehicles Based on Driving Pattern Recognition. *Actuators* **2023**, *12*, 410.
https://doi.org/10.3390/act12110410

**AMA Style**

Wei C, Wang X, Chen Y, Wu H, Chen Y.
Adaptive Fuzzy Power Management Strategy for Extended-Range Electric Logistics Vehicles Based on Driving Pattern Recognition. *Actuators*. 2023; 12(11):410.
https://doi.org/10.3390/act12110410

**Chicago/Turabian Style**

Wei, Changyin, Xiaodong Wang, Yunxing Chen, Huawei Wu, and Yong Chen.
2023. "Adaptive Fuzzy Power Management Strategy for Extended-Range Electric Logistics Vehicles Based on Driving Pattern Recognition" *Actuators* 12, no. 11: 410.
https://doi.org/10.3390/act12110410