The growing need for renewable energy and zero carbon dioxide emissions has fueled the development of thermoelectric generators with improved power generating capability. Along with the endeavor to develop thermoelectric materials with greater figures of merit, the geometrical and structural optimization of thermoelectric generators is equally critical for maximum power output and efficiency. Green energy strategies that are constantly updated are a viable option for addressing the global energy issue while also protecting the environment. There have been significant focuses on the development of thermoelectric modules for a range of solar, automotive, military, and aerospace applications in recent years due to various advantages including as low vibration, great reliability and durability, and the absence of moving components. In order to enhance the system performance of the thermoelectric generator, an artificial neural network (ANN) based algorithm is proposed. Furthermore, to achieve high efficiency and system stability, a buck converter is designed and deployed. Simulation and experimental findings demonstrate that the suggested method is viable and available, and that it is almost similar to the real value in the steady state with the least power losses, making it ideal for vehicle exhaust thermoelectric generator applications. Furthermore, the proposed hybrid algorithm has a high reference value for the development of a dependable and efficient car exhaust thermoelectric generating system.

The engine is the core component of traditional fuel vehicles and gasoline-electric hybrid electric vehicles, while only about 30% of the energy of the commonly used gasoline engine is used to drive the vehicle and to be used for on-board electrical appliances during the work process, and about 40% of the energy is in the form of waste heat. The exhaust gas is emitted, and the average temperature of the exhaust pipe exceeds 250°C [

The output power of the automotive exhaust thermoelectric generator (AETEG) is mainly affected by the engine operating conditions and external loads. When the engine is working at a stable speed, the power value of the thermoelectric generator increases with the voltage. The growth first increases and then decreases, so there is a maximum power point [

Literature [

The structure diagram of AETEG system is shown in

According to the power transfer theorem, when

In the AETEG test bench, the I-U-P characteristic curve of the thermoelectric generator is shown in

Generally, the DC/DC converter supplies power to the battery with low voltage and high current. However, high current will cause a series of problems (high current stress, more conduction loss of the switch), and at the same time, the current ripple on the input and output sides will adversely affect the thermoelectric generator and battery. In order to reduce the current ripple on the input side, the inductor should work in the current continuous mode, but the inductor value will be very large, and its weight and volume will increase sharply, so a two-phase interleaved Buck converter is used [_{L1} and I_{L2} are half of the load current I_{out}, and the inductance values of L_{1} and L_{2} are becomes smaller, and the total current ripple is smaller than the single-phase ripple [

The DC/DC converter system is a nonlinear time-varying system, and the general linear theory cannot be directly applied. In order to carry out dynamic characteristic analysis and related design, the state space averaging method is used [

In the formula, the state and input matrix are _{1}00)^{T}, (100), (0) respectively. A disturbance is added at the steady state point, and the nonlinear AC small-signal state equation of the two-phase interleaved Buck circuit is:

Since the equivalent resistance

A PI controller is designed using the converter element parameters shown in

Parameter | Numerical value |
---|---|

Rated power |
1 kW |

Input voltage |
50~350 |

Output voltage |
45~55 |

Input side capacitance |
4700 |

Output side capacitor |
80 |

Output side inductance |
400 |

Transfer function of DC/DC input voltage to duty cycle

The transfer function of DC/DC input voltage to duty cycle before and after correction is shown in ^{3} rad/s, the phase angle margin _{s} = 4.4/

Usually, the incremental conductance method adopts a fixed-step strategy, and the selection of the step change

In order to solve the problem of the proportional coefficient

In the formula,

The advantage of a neural network is that it does not require an accurate mathematical model, but it can establish complex nonlinear relationships between input and output. In this paper, the feedforward BP-ANN method is adopted. There are three-layer networks. The transfer functions of the hidden layer and the output layer are tansig and purelin. The neural structure is trained by the gradient descent method. The input layer has two neurons, the hidden layer has 5 neurons, and the output layer consists of only one neuron. As shown in

In order to adjust the weights, according to the gradient descent method, the formula is as follows

The neural network established in MATLAB is shown in ^{3}.

The adopted BP neural network method operates in two modes: Offline and online. Firstly, in offline mode, collect 200 sets of experimental data of the conductance incremental method with a fixed step size

When the on-board thermoelectric generator is running in a steady state, the temperature of the exhaust pipe and the output power of the thermoelectric generator do not change or change slowly. When the operating conditions of the engine suddenly change, the temperature and flow rate of the exhaust gas will change drastically, resulting in a sudden change in the output power of the thermoelectric generator. If the traditional fixed-step BP neural network method is used to find the new maximum power point, the dilemma of slow convergence speed and steady-state oscillation will occur. Because choosing a larger step size can ensure that the dynamic time is shortened, but steady-state oscillation is inevitable. Choosing a smaller step size can reduce steady-state oscillation, but it will be accompanied by the problem of poor dynamic performance. In order to account for the on-board thermoelectric generator's tracking speed and steady-state accuracy, this paper proposes an adaptive variable step size adjustment strategy according to the power variation characteristics of the on-board thermoelectric generator, as shown below.

In the formula,

In the adaptive variable step size adjustment strategy, the dimensioning factor

To sum up, a new hybrid MPPT algorithm is proposed as shown in

The equivalent circuit model of a single thermoelectric module is a voltage source connected in series with a resistor, and its output is related to the temperature difference between the hot and cold ends. As shown in

Set the initial temperature difference of the thermoelectric generator ^{o}C, change to ^{o}C at 3 s, then jump to ^{o}C at 6 s, and finally change to ^{o}C at t = 10 s. In order to compare the performance of each method more precisely, this paper uses three criteria that is, MPPT tracking accuracy, response time, and overshoot.

The simulation results of the proposed algorithm, the separate improved conductance incremental method (SIINC) and the separate BP artificial neural network method (SBP-ANN) are shown in

It shows that the HM and SBP-ANN search for the maximum power is very close to the theoretical value, and the steady-state accuracy is very high. However, when SIINC tracks the maximum power, the duty cycle of the DC/DC converter changes drastically, resulting in a lot of power loss, so its steady-state accuracy is not high.

As shown in

As shown in

In order to verify the effectiveness of the proposed HM algorithm, a test bench was created for AETEG which is depicted in

When the automobile engine runs in two stable conditions (3000 r/min@65NM, 2600 r/min@ 58NM), the results are shown in

Factor | Actual value | SIINC algorithm | SBP-ANN algorithm | Proposed |
---|---|---|---|---|

Hot end temperature _{H} (^{o}C) |
226.9 | 226.9 | 226.9 | 226.9 |

Cold end temperature _{c} (^{o}C) |
62.4 | 62.4 | 62.9 | 62.4 |

Voltage |
100.4 | 100.4 | 100.4 | 100.4 |

Current |
1.16 | 1.08 | 1.07 | 0.94 |

Power |
116.66 | 110.49 | 112.45 | 114.87 |

Deviation rate |
0 | 5.28 | 3.61 | 1.53 |

Factor | Actual value | SIINC algorithm | SBP-ANN algorithm | Proposed |
---|---|---|---|---|

Hot end temperature _{H} (^{o}C) |
105.1 | 105.1 | 105.1 | 105.1 |

Cold end temperature _{c} (^{o}C) |
59.5 | 59.5 | 59.5 | 59.5 |

Voltage |
50.7 | 63.8 | 82.5 | 57.2 |

Current |
0.79 | 0.56 | 0.46 | 0.68 |

Power |
40.1 | 35.9 | 37.9 | 39.2 |

Deviation rate |
0 | 10.4 | 5.50 | 2.24 |

As shown in

Among them, the response times of proposed, SIINC and SBP-ANN algorithms are 2.4, 2 and 3.2 s, indicating that SIINC has the shortest dynamic response time, but its overshoot of 5.9 W is greater than 2.1 W of proposed algorithm and 3.8 W of SBP-ANN algorithm, causing great damage to the circuit components of the DC/DC converter. The experimental results show that the proposed algorithm outperforms the traditional SIINC and SBP-ANN algorithms in terms of stability and dynamic performance.

This paper proposed a novel algorithm for thermoelectric generator based on neural networks.

(1) The AETEG system based on the 48 V electrical architecture is proposed, which can improve the fuel economy of the vehicle and achieve the effect of energy saving and emission reduction.

(2) The design and implementation of the double closed-loop interleaved Buck converter ensures the high efficiency and stability of the AETEG system.

(3) Based on the maximum power tracking strategy, the improved conductance increment method can be used when far from the maximum power point, which can improve the dynamic performance of the system. When approaching the maximum power point, the adaptive variable step size BP neural network method can reduce the steady-state oscillation.

(4) Simulation and experimental results show that the hybrid method proposed in this paper can not only improve the output power of the on-board thermoelectric generator, but also reduce the response time of the system.

The authors would like to thanks the editors and reviewers for their review and recommendations.