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This paper proposes an adaptive real-time energy management strategy for a parallel plug-in hybrid electric vehicle (PHEV). Three efforts have been made. First, a novel driving pattern recognition method based on statistical analysis has been proposed. This method classified driving cycles into three driving patterns: low speed cycle, middle speed cycle, and high speed cycle, and then carried statistical analysis on these three driving patterns to obtain rules; the types of real-time driving cycles can be identified according to these rules. Second, particle swarm optimization (PSO) algorithm is applied to optimize equivalent factor (EF) and then the EF MAPs, indexed vertically by battery’s State of Charge (SOC) and horizontally by driving distance, under the above three driving cycles, are obtained. Finally, an adaptive real-time energy management strategy based on Simplified-ECMS and the novel driving pattern recognition method has been proposed. Simulation on a test driving cycle is performed. The simulation results show that the adaptive energy management strategy can decrease fuel consumption of PHEV by 17.63% under the testing driving cycle, compared to CD-CS-based strategy. The calculation time of the proposed adaptive strategy is obviously shorter than the time of ECMS-based strategy and close to the time of CD-CS-based strategy, which is a real-time control strategy.

The energy crisis and environmental pollution problem have become a serious concern in the recent years [

The optimization-based EMSs are classified into global optimization-based EMSs and instantaneous optimization-based EMSs [

The instantaneous optimization-based-EMSs mainly encompass convex programming- (CP-) based-EMS [

To reduce the calculation time of the ECMS-based-EMS, we proposed a Simplified-ECMS-based EMS for a parallel plug-in hybrid electric vehicle in our previous article [

To solve the above problem and obtain an adaptive real-time energy management strategy for plug-in hybrid electric vehicle, a novel driving pattern recognition method with low computational cost was first proposed. This method classified driving cycles into three driving patterns: low speed cycle, middle speed cycle, and high speed cycle, carried statistical analysis on these three driving patterns to obtain rules, and identified the types of real-time driving cycles according to these obtained rules. Second, particle swarm optimization (PSO) algorithm is applied to optimize EF, and the EF MAPs under the above three driving cycles are obtained. Finally, an adaptive real-time energy management strategy based on Simplified-ECMS and the novel driving pattern recognition method was proposed. Simulation on a test driving cycle is performed to verify the proposed adaptive strategy.

This paper is outlined as follows. The structure and parameters of parallel CVT-based PHEV powertrain are described in Section

This study focused on a single-shaft parallel CVT-based PHEV. Figure

Basic parameters of the PHEV.

| | |
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Basic parameters of the vehicle | Curb weight/kg | 1395 |

Frontal area/m^{2} | 2.265 | |

Air drag coefficient | 0.301 | |

Wheel radius/m | 0.307 | |

Wheel rolling resistance coefficient | 0.0135 | |

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Engine | Peak power/kW | 90 |

Maximum torque/(Nm) | 155 | |

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ISG motor | Peak power/kW | 30 |

Maximum torque/(Nm) | 113 | |

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Battery | Capacity/ Ah | 30 |

Rated voltage/V | 316 | |

Initial SOC | 0.95 | |

Minimum SOC | 0.25 | |

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CVT | The range of speed ratio | 0.422-2.432 |

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FD | Speed ratio | 5.24 |

Parallel CVT-based PHEV powertrain system.

Simplified-ECMS-based EMS is derived from ECMS-based EMS. The detailed derivation process can be found in our previous article [

Firstly, the engine’s fuel rate and battery’s equivalent fuel consumption rate are approximately fitted by the piecewise function. And the fitted result of the engine’s fuel rate is expressed as follows [

The fitted result of the battery’s equivalent fuel consumption rate is expressed as follows [

Then, the total equivalent fuel rate can be expressed by a convex piecewise function [

(1) If

(2) If

According to Eqs. (

Consequently, the concept of the Simplified-ECMS-based EMS is achieving the optimal solution by calculating and comparing the total equivalent fuel rate of the above five points for each time instant, instead of calculating and comparing the total equivalent fuel rate of huge candidates, who cover all over the control domain [

The procedure flow chart of the Simplified-ECMS-based EMS.

(1) Calculate the required torque

(2) If

(3) Obtain the equivalent factor (EF) by a certain algorithm and then calculate the total equivalent fuel rate of above five points according to Eqs. (

(4) Obtain the optimal control variable after comparing the total equivalent fuel rates of above five points, and output the optimal torque distribution to power system.

The detailed development process of the adaptive real-time energy management for the plug-in HEV based on simplified-ECMS and the novel driving pattern recognition method is illustrated in Figure

Control scheme of the adaptive energy management strategy for the plug-in HEV.

The logic flow is also shown in Figure

Detailed procedure of the proposed energy management strategy.

Offline | (1) Three typical driving cycles are constructed after driving data gathering, driving features selection, and driving segments clustering. |

(2) Two driving features for driving pattern recognition are determined, and the probability density functions of these two driving features under the above typical driving cycles are obtained by statistical analysis. | |

(3) Rules of driving cycle recognition are extracted according to the above two probability density functions. | |

(4) Particle swarm optimization (PSO) algorithm is applied to optimize equivalent factor (EF), and the MAPs of this factor under different typical driving cycles, driving distance, and SOC are obtained. | |

| |

Online | (5) The real-time driving pattern is identified according to the driving cycle recognition rules, and output the type of drive cycle. |

(6) The driving distance is got by navigation system and vehicle’s velocity. | |

(7) Based on the aforementioned work, the EF can be obtained by looking up EF Maps through the type of driving cycle, driving distance, and SOC. | |

(8) Simplified-ECMS-based strategy is employed to solve the energy distribution optimization problem, and the optimal torque distribution between engine and motor can be obtained. |

In step 1, three typical driving cycles (low speed cycle, middle speed cycle, and high speed cycle) are constructed after driving data gathering, driving features selection, and driving segments clustering. The profiles of three typical driving cycles are shown in Figure

Profiles of three typical driving cycles.

Low speed cycle

Middle speed cycle

High speed cycle

In step 2 and step 3, mean velocity (

If

If

If

If

If

If

The probability density functions of two driving features under different driving cycles.

Probability density function of mean velocity under different types of driving cycles

Probability density function of cruise percentage under different types of driving cycles

Based on the above deduction, the rules for driving cycle recognition are determined, and these rules are shown in Table

Rules for the driving pattern recognition.

| | | |
---|---|---|---|

| Low | Low | Middle |

| Low | Middle | High |

| Middle | High | High |

In Step 4, particle swarm optimization (PSO) algorithm is applied to optimize equivalent factor (EF) of the simplified-ECMS-based EMS under each typical driving cycle, driving distance, and SOC. The fitness function is expressed as

The flow chart of EF optimization is shown in Figure

The flow chart of EF optimization.

The optimization result is shown in Figure

The optimization result.

EF is mainly affected by driving distance and SOC in certain driving cycle, the above EF optimization is based on given driving distance and SOC; next, under high speed cycle, the EF optimization is implemented one by one in different driving distances and SOC through offline optimization, and the MAP figure of EF under high speed cycle, as shown in Figure

Optimal EF of high speed cycle.

Optimal EF of middle speed cycle.

Optimal EF of low speed cycle.

In Step 5, the real-time driving pattern is identified according to the driving cycle recognition rules. In this step, the time window of data sampling is set to 100 s. Once the vehicle is running, the vehicle controller will monitor the vehicle speed. When the operating time exceeds 100s, the driving cycle recognition module will calculate mean velocity and cruise percentage for the previous driving block, and then these data will be used to identify the driving pattern according to the driving cycle recognition rules. It is noted that in the above recognition process, the initial driving pattern is set to the middle speed cycle.

In Step 6, the driving distance is got by navigation system and vehicle’s velocity. The total distance can be obtained by navigation system. The completed distance can be calculated by the vehicle’s velocity. Then driving distance is obtained by subtracting the completed distance from the total distance.

In Step 7, based on the aforementioned work, the EF can be obtained by looking up EF Maps through the type of driving cycle, driving distance, and SOC.

In Step 8, simplified-ECMS-based EMS is employed to solve the energy distribution optimization problem, and the optimal torque distribution between engine and motor can be obtained.

In this section, the proposed adaptive energy management strategy is verified in simulation. To evaluate the control performance of the proposed adaptive energy management strategy, the CD-CS-based strategy and the proposed strategy were simulated in MATLAB/Simulink, and the CD-CS-based strategy was taken as the benchmark.

The simulation works are carried out under a test driving cycle. The driving cycle is shown in Figure

The calculation time of different control strategies under the test driving cycle.

calculation time | |
---|---|

CD-CS-based strategy | 72.23 |

The proposed adaptive strategy | 164.39 |

Simplified-ECMS-based strategy | 149.26 |

ECMS-based strategy | 875.83 |

The test driving cycle.

The driving pattern recognition result.

The basic results of the proposed strategy on the test driving cycle.

SOC variation trajectories.

Engine working points on the test driving cycle.

Fuel consumption curves on the test driving cycle.

The driving pattern recognition result is shown in Figure

The basic results, including the required torque of plug-in hybrid powertrain (

SOC curves of these two strategies on the test driving cycle were shown in Figure

Engine working points of these two strategies on the test driving cycle are shown in Figure

Fuel consumption curves of these two strategies on the test driving cycle are shown in Figure

The calculation time of different control strategies under the test driving cycle is shown in Table

In this paper, an adaptive real-time energy management strategy based on simplified-ECMS and a novel driving pattern recognition method for PHEV is proposed. The findings of this paper can be shown as follows:

(1) A novel driving pattern recognition method based on statistical analysis has been proposed. This method classifies driving cycles into three driving patterns: low speed cycle, middle speed cycle, and high speed cycle, and then statistical analysis on these three driving patterns is carried to obtain rules. Simulation results show that the types of real-time driving cycles can be effectively identified according to these rules.

(2) Particle swarm optimization (PSO) algorithm is applied to optimize equivalent factor (EF) and then the EF MAPs, indexed vertically by battery’s SOC and horizontally by driving distance, under the above three driving cycles are obtained.

(4) An adaptive real-time energy management strategy based on Simplified-ECMS and a novel driving pattern recognition has been proposed. Simulation on a test driving cycle is performed. The simulation results show that the adaptive energy management strategy can decrease fuel consumption of PHEV by 17.63% under the testing driving cycle, compared to CD-CS-based strategy. The calculation time of the proposed adaptive strategies is obviously shorter than the time of ECMS-based strategy and close to the time of CD-CS-based strategy, which is a real-time control strategy.

Currently, the proposed adaptive strategy is only verified through simulations. The next step is to perform hardware-in-the-loop test or experimental validations.

The data used to support the findings of this study are available from the corresponding author upon request.

The authors declare that they have no competing interests.

The work presented in this paper is supported by the National Natural Science Foundation of China (Grant no. 51665020) and the State Key Laboratory of Mechanical Transmission’s open fund (Grant no. SKLMT-KFKT-201617).