# Machine-Learning-Based Optimization of Energy Management in a Novel Hybrid Powertrain of Concrete Truck Mixers

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## Abstract

**:**

## 1. Introduction

## 2. Powertrain Configuration and Model

#### 2.1. Powertrain Specification and System Modeling

^{3}agitator capacity carrying the proposed novel hybrid powertrain is taken as the research object, and the major parameters of the E-RE-CTM are listed in Table 1. To realize the E-RE-CTM control, the powertrain models, mainly including the engine, motor, generator, battery pack, and upper mixing system, are established, and previous studies are available for details [23].

#### 2.2. Energy Optimal Problem Formulation

## 3. Global Optimal Energy Management Strategy

#### 3.1. Driving Data Obtain

#### 3.2. Dynamic Programming to Solve the Optimization Problem

## 4. Approximate Optimal Energy Management Design Based on Machine Learning

#### 4.1. Machine Learning of the Trip Information

#### 4.1.1. Typical Driving Conditions Construction Based on Unsupervised Learning

#### 4.1.2. Development of Driving Condition Identifier Based on Supervised Learning

#### 4.2. Machine Learning of the Optimal Energy Management

#### 4.2.1. Neural Network Structure Determination

#### 4.2.2. Neural Network Training and Verification

## 5. Conclusions

- For the CTMs equipped with a proposed novel hybrid powertrain, a global optimization algorithm based on DP was proposed to solve the two-point boundary value problem in the finite time domain, which has the characteristics of constrained time-varying and double control variables. By designing an optimal efficiency curve of the generator in driving mode and establish the generator efficiency model, the complexity of solving the energy optimization problem can be reduced;
- An optimal control database can be obtained based on the ML and data-driven method; different ML-based driving condition identifiers were constructed and compared. Simulation results showed that the total performance of ELM is superior to the RF and LVQ through the comparison of kappa coefficient, identification time, and identification accuracy. An optimized ELM identifier based on genetic algorithm was presented, which can further promote online identification performance;
- For the E-RE-CTM, a vehicle mass and power demand of an upper-part system based novel neural network energy management strategy was designed based on a constructed optimal control database. Simulation results showed that the designed neural network is reasonable and feasible.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**The flow chart of global energy management strategy for an E-RE-CTM. (

**a**) Five route segments of a CTM; (

**b**) driving data of a CTM; (

**c**) optimal efficiency curve of the generator in driving mode; (

**d**) the schematic diagram of DP process.

**Figure 3.**The ML-based structure for an E-RE-CTM. (

**a**) Description of micro-trips; (

**b**) description of clustering results; (

**c**) the three types of typical driving condition; (

**d**) description of LVQ neural network; (

**e**) the calculation process of RF; (

**f**) the structure of ELM neural network; (

**g**) the description of the neural network modules; (

**h**) the occurring micro-trips in the real world.

**Figure 4.**Driving data obtain and analysis. (

**a**) The variance contribution rate of principal components; (

**b**) features load coefficients in the principal components; (

**c**) silhouette values based on the k-means + + algorithm; (

**d**) some micro-trips of three types; (

**e**) classification results of a standard driving cycle.

**Figure 5.**The optimal efficiency curve and optimal control results. (

**a**) Optimal control results under type 1; (

**b**) optimal control results under type 2; (

**c**) optimal control results under type 3.

**Figure 8.**Training results of designed NN modules under different types. (

**a**) NN module training results under type 1; (

**b**) NN module training results under type 2; (

**c**) NN module training results under type 3.

**Table 1.**The parameters of the main components for the powertrain [23].

Item | Value |
---|---|

E-RE-CTM non-load mass (kg) | 15,000 |

E-RE-CTM full-load mass (kg) | 40,500 |

Li-ion battery pack capacity (Ah) | 300 |

Driving motor maximum power (kW) | 350 |

Hydraulic pump maximum displacement (cm^{3}/r) | 90 |

Hydraulic pump/motor maximum pressure (bar) | 420 |

Generator maximum power (kW) | 130 |

Engine maximum power (kW) | 125 |

Symbol | Description | Unit |
---|---|---|

${v}_{max}$ | Maximum vehicle speed | km/h |

${a}_{max}^{n}$ | Maximum negative acceleration | m/s^{2} |

$\sigma \left({a}^{p}\xb7v\right)$ | The standard deviation of the product of positive acceleration and speed | m^{2}/s^{3} |

$\overline{\left({a}^{n}\xb7v\right)}$ | Mean value of the product of negative acceleration and speed | m^{2}/s^{3} |

${r}_{v}^{0,15}$ | The ratio between 0 and 15 of vehicle speed | - |

${r}_{v}^{30,50}$ | The ratio between 30 and 50 of vehicle speed | - |

Evaluation Index | $\left[{\mathit{W}}_{\mathit{t}},{\mathit{I}}_{\mathit{t}}\right]=\left[120,1\right]{}^{1}$ | $\left[{\mathit{W}}_{\mathit{t}},{\mathit{I}}_{\mathit{t}}\right]=\left[80,1\right]{}^{2}$ | |||||
---|---|---|---|---|---|---|---|

LVQ | RF | ELM | Optimized ELM | LVQ | RF | ELM | |

κ | 0.52 | 0.59 | 0.63 | 0.65 | 0.61 | 0.53 | 0.56 |

${A}_{m}$ | 0.73 | 0.76 | 0.78 | 0.79 | 0.78 | 0.71 | 0.73 |

${t}_{a}$ | 15.30 | 11.94 | 0.52 | 0.45 | 17.91 | 12.32 | 0.47 |

^{1}The optimal window size and identification interval of RF and ELM;

^{2}the optimal window size and identification interval of LVQ.

Type | MSE |
---|---|

1 | 0.00032 |

2 | 0.0020 |

3 | 0.00021 |

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**MDPI and ACS Style**

Huang, Y.; Jiang, F.; Xie, H.
Machine-Learning-Based Optimization of Energy Management in a Novel Hybrid Powertrain of Concrete Truck Mixers. *World Electr. Veh. J.* **2021**, *12*, 175.
https://doi.org/10.3390/wevj12040175

**AMA Style**

Huang Y, Jiang F, Xie H.
Machine-Learning-Based Optimization of Energy Management in a Novel Hybrid Powertrain of Concrete Truck Mixers. *World Electric Vehicle Journal*. 2021; 12(4):175.
https://doi.org/10.3390/wevj12040175

**Chicago/Turabian Style**

Huang, Ying, Fachao Jiang, and Haiming Xie.
2021. "Machine-Learning-Based Optimization of Energy Management in a Novel Hybrid Powertrain of Concrete Truck Mixers" *World Electric Vehicle Journal* 12, no. 4: 175.
https://doi.org/10.3390/wevj12040175