Abstract
In order to further improve the energy utilization of the range extender electric vehicle (REEV), the energy management strategy of the REEV under different heat demands and operating temperatures is studied. Firstly, for reducing the extra fuel consumption caused by engine operating temperature, the influence of engine lubricating oil temperature on the equivalence factor is analyzed, and a novel method is proposed, which can determine the equivalence factor by three parameters, including engine temperature, operating condition type, and battery state of charge (SOC). Secondly, to reduce the influence of frequent engine start/stop on fuel consumption, a method to establish the penalty factor based on battery SOC is proposed. Finally, the engine temperature rise law under low-temperature environment and the compressor drive mode determination method under high-temperature environment are studied according to the heat demand under different ambient temperatures. An adaptive equivalent consumption minimization strategy (ECMS) considering the effect of temperature under different heat demands is proposed on the basis of the above research. The simulation under the worldwide harmonized light vehicle test cycle (WLTC) drive cycle reveals that by using the adaptive ECMS, the vehicle operating cost is reduced by 5.69% in normal temperature environment, 0.53%-2.39% in a low-temperature environment, and 0.2%-2.01% in a high-temperature environment.
1. Introduction
The engine provides the main energy required for the operation of the REEV during the charge sustaining (CS) stage, and the engine will start and stop frequently, which will affect the operating temperature of the engine and the whole vehicle’s energy consumption. Especially, when there is a heat demand, the operating energy consumption of the whole vehicle can change significantly. In order to improve the economy of the whole vehicle, it is necessary to conduct research on the energy management strategy of the range extender electric vehicle under different heat demands in the CS stage.
When the ambient temperature is low or high, the vehicle often has a heat demand for heating or cooling, and the heat demand can significantly reduce the pure electric driving range [1, 2]. For the REEV, when the ambient temperature is low, the waste heat from the engine can be used to meet the heat demand of the vehicle. In order to utilize the engine waste heat as quickly as possible, Shams-Zahraei et al. [3] applied a dynamic programming algorithm for global optimization, which shows that the engine would reach the warm-up state at a faster rate. Wang et al. [4] established the optimal control of the auxiliary power unit (APU) cold start and warm-up process with a dynamic planning optimization algorithm to reduce the fuel consumption and warm-up time of the warm-up process. Engbroks et al. [5] applied Pontryagin’s minimum principle (PMP) global optimization strategy to investigate the engine temperature variation under different operating conditions and found that the engine reaches the warming state at a fast rate under various operating conditions. All the above studies applied the global optimization strategy, which can make the engine reach the warm-up state at a fast rate only under specific operating conditions and heat demand, lacking a comprehensive study of the effects of operating conditions, heat demand, and battery SOC on the engine temperature rise rate.
Pure electric vehicles use electric compressors for cooling when the ambient temperature is high [1, 6]. For the REEV, the conversion efficiency of energy is reduced when the electric energy generated by the APU is used to drive the compressor. The operating cost of the vehicle will be reduced if the mechanical energy of the engine is directly used to drive the compressor when the engine is running and if the electric energy is used to drive the compressor when the engine is off.
During engine operation, the initial phase requires extra power to overcome the friction caused by the high oil viscosity due to the low temperature of the engine lubricating oil, which generates extra fuel consumption [7]. The engine’s fuel consumption needs to be corrected [4, 8]. When this temperature reaches above 80°C, the effect of temperature on fuel consumption is negligible [9]. Van Berkel et al. [10] considered the effect of engine lubricant temperature on fuel consumption when applying a dynamic planning energy management strategy. Merz et al. [11] corrected the fuel consumption during cold start of the engine in a PMP energy management strategy and obtained a more accurate fuel consumption in use. Pozzato et al. [12] considered the effect of engine operating temperature on starting cost but did not consider the effect of real-time temperature on fuel consumption.
During the CS stage, the engine starts and stops more frequently in order to maintain battery power [13]. However, the engine generates additional fuel consumption each time it is turned on [14]. To reduce the fuel consumption caused by frequent engine starts and stops, the literature [15] introduced a start-stop penalty factor in the dynamic programming process, which effectively reduced the fuel consumption caused by frequent starts and stops. Pozzato et al. [12] added the engine start-up cost to the objective function of the global optimization strategy and limited the engine running time to reduce the number of engine starts and stops. Song et al. [16] used a penalty factor to reduce the number of starts and stops in their study of fuel cells. The above literature only added a penalty factor or start-up cost under current operating conditions and did not investigate the influence of operating conditions, battery SOC, and engine operating temperature on penalty factor.
Currently, energy management strategies are categorized into two types, rule-based and optimization-based [17]. The rule-based energy management strategy is easy to implement, but its poor adaptability to working conditions makes it hardly improve the economy further. Zhang et al. [18] developed a multipoint switching control strategy with a genetic algorithm for optimization that planned the path of engine switching and showed a significant reduction in fuel consumption and emissions compared with the conventional switching method. Padmarajan et al. [19] proposed a rule-control strategy that considered the working condition information and vehicle driving energy, which improved the fuel economy. Kamal et al. [20] combined a neural network with a deterministic rule control energy management strategy to calculate overall vehicle efficiency by considering drivetrain components and applied an adaptive fuzzy neural algorithm to jointly control energy distribution based on the mode switching strategy of fuzzy control to achieve real-time control. Dawei et al. [21] applied a genetic algorithm to optimize the affiliation function of fuzzy control and established a system based on the ratio of target torque and demand torque of the motor and SOC as inputs and the torque distribution between the engine and the motor as the output of the intelligent fuzzy logic control strategy. The global optimization-based energy management strategy needs to anticipate global operating conditions and cannot be applied in real time, while the real-time optimization-based ECMS can not only be applied in real time but also bring higher economic benefits than the rule-based energy management strategy, which has better practical value [22, 23]. Zhang et al. [24] proposed an ECMS strategy based on speed prediction through a neural network approach to improve fuel economy. Han et al. [25] proposed an energy management strategy combining rule control and ECMS and verified the effectiveness of the strategy through simulation. In the ECMS strategy, the equivalence factor varies with the type of operating conditions [26, 27] or SOC [28, 29], but these studies ignored the effect of engine operating temperature on the equivalence factor.
In view of the above problems, the REEV is taken as the research object in this study, and ECMS real-time optimization is adopted for the CS stage. The main contributions are summarized as follows: (i)The relationship between the engine operating temperature and the ECMS strategy equivalence factor is analyzed, and the map of the equivalence factor under different cycles is obtained(ii)The relationship between the penalty factor and the number of engine starts and stops is studied, and it is concluded that the penalty factor is only related to SOC(iii)The engine temperature rise law and the operation mode of the compressor are investigated to establish an adaptive ECMS considering the influence of temperature under different heat demands, and the effectiveness of the proposed strategy is proved by simulation analysis of three different environments
The remainder of this paper is organized as follows: in Section 2, the engine model, the battery model, and the operating cost model are developed. The adaptive ECMS strategy is presented in Section 3, while the strategy is simulated and analyzed in Section 4. Finally, in Section 5, the whole work is summarized.
2. Vehicle Drivetrain Modelling
The power system structure of the REEV studied is shown in Figure 1, which consists of an engine, integrated starter generator (ISG) motor, battery, drive motor, gearbox, and several control units.
2.1. Engine Models
2.1.1. Engine Fuel Consumption Model
The fuel consumption of the engine is related to speed and torque, and the data obtained from the test are interpolated to obtain a numerical model of the fuel consumption rate related to torque and speed, as shown in Figure 2. The engine has a maximum power of 41 kW, and its economic zone is around 20 kW.
2.1.2. Engine Fuel Consumption Correction Model
The fuel consumption rate in the engine’s universal characteristic diagram refers to the fuel consumption rate in the warm-up state of the engine. When the engine is not warmed up, the fuel consumption rate is affected by the temperature. where denotes the fuel consumption rate after considering the effect of temperature (g/kWh) and denotes the temperature influence factor, calculated as follows: where indicates the lubricating oil temperature when the engine reaches the warm state (take 80°C), indicates the actual cooling water temperature of the engine (°C), and and are the influence coefficients (1/kK).
2.1.3. Engine Temperature Rise Model
The energy produced by the combustion of gasoline is classified as mechanical energy and heat energy. The mechanical energy is output through the mechanical structure of the engine. Part of the heat energy is discharged through the exhaust gas and the engine body, and the rest is used to warm up the engine. The rate of change the engine lubricating oil temperature is where indicates the total energy produced by the combustion of gasoline (kJ), indicates the heat taken away by the exhaust gas (kJ), indicates the heat taken away by the environment around the engine (kJ), and indicates the effective heat capacity of the engine (J/k).
2.2. Power Battery Model
The lithium-ion battery selected for this paper has a capacity of 60 Ah. The basic parameters of the power battery are shown in Table 1.
Based on the Rint equivalent circuit model, calculate the power cell current by where is the open circuit voltage of power battery (V), is the output power of battery (kW), is the internal resistance of power battery (Ω), indicates the charging efficiency of battery, and indicates the discharging efficiency of battery. Battery SOC is calculated based on current and power cell capacity. where is the initial value and is the rated capacity.
2.3. Operating Cost Model
The operating cost model includes fuel consumption and electricity consumption, as follows: where denotes the cost of fuel consumption (yuan) and denotes the cost of electricity consumption (yuan).
3. Adaptive ECMS considering Temperature Effects under Different Heat Demands
In the CS stage, the REEV will start and stop frequently under different operating conditions and different heat demands in order to keep the battery SOC around 0.3, and its operating temperature will change greatly. In this study, the effect of engine operating temperature on the equivalent factor is analyzed to reduce the additional fuel consumption caused by low engine operating temperature, and a penalty factor is established to control the engine start/stop, thus reducing the impact of frequent engine start/stop on the overall vehicle energy consumption. To further reduce the energy consumption of the whole vehicle under different heat demands, an adaptive ECMS considering temperature effects under different heat demands is proposed, based on the analysis of the temperature rise pattern of the engine in low-temperature environment and the operation mode of the compressor in high-temperature environment.
3.1. Equivalence Factor considering the Effect of Temperature
ECMS is a real-time optimized control strategy, the essence of which is to equate the power consumed by the battery to the fuel consumption and integrate the actual fuel consumption of the engine to obtain the instantaneous whole vehicle fuel consumption, which is calculated as follows: where is the engine fuel consumption of the car at time , denotes the equivalence factor, and denotes the low heating value of the fuel, respectively.
As can be seen from the above equation, the key to the ECMS strategy is the reasonable selection of the equivalence factor, whereas previous literature only determined the equivalence factor according to the operating conditions or battery SOC, ignoring the effect of engine operating temperature on the equivalence factor. To better characterize the relationship between the equivalence factor and engine temperature, the effect of temperature on the equivalence factor is investigated, and the equivalence factor under different operating conditions is calculated in this study.
3.1.1. Analysis of the Relationship between Equivalent Factor and Engine Temperature
To ensure battery life [30], the vehicle enters the CS stage when the battery SOC is 0.3. Only the effect of the equivalent factor on the SOC change at different engine lubricating oil temperatures in this stage is analyzed here. The SOC change is expressed as the difference between the SOC value at the beginning of the drive cycle and the SOC value at the end of the drive cycle. where indicates the change value of SOC. If the value is positive, battery SOC decreases after the drive cycle and the battery is in a discharge state; if the value is negative, battery SOC increases after the drive cycle and the battery is in a charge state. indicates the SOC value at the beginning of the drive cycle, and indicates the SOC value at the end of the drive cycle. Here, six selected standard drive cycles are studied, and the results are shown in Figure 3.
(a) BUSRTE
(b) INDIA_URBAN
(c) IM240
(d) EUDC
(e) HWFET
(f) US06_HWY
From Figure 3, it can be seen that under each operating condition, the smaller the equivalence factor, the larger the value of , and the larger the battery discharge; the larger the equivalence factor, the smaller the value of , and the larger the charge. Additionally, the engine temperature has an effect on the equivalence factor. When the value is the same, the lower the temperature, the larger the equivalence factor under the same operating condition. This is because the lower the temperature, the higher the corrected fuel consumption of the engine, which requires a larger equivalence factor; similarly, when the equivalence factor is the same, the lower the engine temperature, the larger the value. Therefore, when formulating the ECMS, in addition to the influence of operating conditions and battery SOC on the equivalence factor, the influence of engine lubricant temperature on the equivalence factor also needs to be considered.
3.1.2. Equivalent Factor Calculation
Figure 3 shows that is monotonic with the equivalence factor for the same operating conditions and the same engine lubricating oil temperature, in which case the battery SOC can be adjusted by adjusting the equivalence factor to keep the SOC near the target value. It is assumed that the same equivalence factor will result in the same for the same operating conditions and the same engine lubricating oil temperature [15]. For example, in Figure 3, in the BUSRTE drive cycle, the engine temperature is 20°C at , which means that the SOC increases by 0.02 and the equivalence factor is 3.8. In order to maintain the SOC at 0.3, the equivalence factor value is set to 3.8 at that operating condition and temperature when the battery SOC value is 0.28, which can maintain the SOC near 0.3. According to this method, the coordinates in Figure 3 are transformed and interpolated to obtain the map diagram of the equivalent factor for each operating condition when SOC is between 0.2 and 0.4, as shown in Figure 4.
(a) BUSRTE
(b) INDIA_URBAN
(c) IM240
(d) EUDC
(e) HWFET
(f) US06_HWY
3.2. Engine Start-Stop Factor considering the Effect of SOC
Frequent engine start-stops lead to large start-up fuel consumption. The equivalence factor will determine whether the engine is on or off but cannot reduce the number of engine start-stops. Based on the equivalence factor research, the engine start-stop penalty factor is introduced to control the engine start-stop and then control the engine temperature and reduce the start-stop fuel consumption. When the power provided by the battery cannot meet the current power demand of the whole vehicle, the engine is turned on, and the penalty factor controls the starting running time to get the instantaneous equivalent fuel consumption calculated as follows: where is the start-stop penalty factor. The penalty factor cannot be selected arbitrarily; if it is selected too small, the engine will start and stop frequently; if it is selected too large, when the engine starts, the control strategy is difficult to reach the engine shutdown condition, which will lead to the engine running for a long time but enhance the whole vehicle operation cost. The relationship of penalty factor with operating condition, SOC, and engine lubricating oil temperature is studied, respectively, for selecting the appropriate penalty factor. The results demonstrated that the penalty factor is only related to SOC. In order to analyze the relationship between penalty factor and SOC, the IM240 drive cycle is used as the research object, the initial engine temperature is set to 20°C, and the initial SOC is set to 0.25, 0.3, and 0.35, respectively, for the study. The simulation results are shown in Figure 5.
(a)
(b)
(c)
From Figure 5, it can be seen that the penalty factors are different at different SOCs. When and the penalty factor is between 0.04 and 0.08, the whole vehicle has the lowest operating cost; when and the penalty factor is between 0.12 and 0.14, the whole vehicle has the lowest operating cost; when and the penalty factor is between 0.15 and 0.17, the whole vehicle has the lowest operating cost. Therefore, the penalty factor is related to the SOC value and will increase with SOC. When the simulation analysis was performed for SOC values of 0.2 and 0.4, the penalty factor was between 0.04 and 0.06 and 0.16 and 0.18, respectively, and the lowest overall vehicle operating cost was achieved. The mean value of the penalty factor corresponding to the lowest operating cost for each SOC was taken, and the mean value of the lowest operating cost penalty factor obtained for different SOCs was fitted, as shown in Figure 6.
3.3. Impact of Different Heat Demands on Vehicle Energy Consumption
When the vehicle has a heating demand at a low ambient temperature, the engine needs to reach the warm-up state at a faster speed, while the engine’s own temperature varies greatly due to the heating demand. Therefore, a reasonable control must be applied to the temperature change of the engine’s warm-up process so that the waste heat can be utilized and, meanwhile, energy consumption can be reduced. When the vehicle has cooling demand in a high-temperature environment, the engine mechanical energy, APU, and battery electric energy can be used to achieve a hybrid drive air conditioning compressor to achieve further energy savings. In order to analyze the impact of different heat demands on the energy consumption of the whole vehicle, this study investigates the temperature rise rate of the engine when there is a demand for heating in a low-temperature environment and the driving mode of the dual power source compressor in a high-temperature environment.
3.3.1. Study of Engine Temperature Rise Rate in Low Ambient Temperature
In low-temperature environment, the heat management system meets the heating demand by using positive temperature coefficient (PTC) heating or the engine waste heat, but the engine coolant temperature needs to reach 60°C when using the waste heat [1], and the coolant temperature is replaced by the lubricant temperature in this study [31]. The engine of the REEV will work for a long time in the CS stage, and once the warm-up is complete, the coolant temperature will be maintained above 60°C, which can meet the heat demand of the whole vehicle. PTC heat production will only exist in the engine warm-up process, and faster or slower engine warm-up may generate more energy consumption. Thus, the engine temperature rise rate will be investigated in this section.
In this section, the PMP global optimization strategy is applied to investigate the temperature rise law under different SOCs, different operating conditions, and different ambient temperatures, and the temperature rise rate of the engine is found to be only related to the ambient temperature. In the analysis of the effect of ambient temperature on the temperature rise rate, the medium-speed IM240 drive cycle is selected, the operating time is set to 2000 s, the ambient temperature is -20°C, -10°C, 0°C, and 10°C, and the initial SOC of the battery is set to 0.3. After simulation, the temperature rise curve of the engine is shown in Figure 7(a), from which it can be seen that the higher the ambient temperature, the smaller the temperature rise rate of the engine warming process. This is because the higher the ambient temperature, the smaller the heating power required, and the smaller the economy brought by using the engine waste heat. The engine temperature rise rate curves at different ambient temperatures are extracted to obtain the temperature rise reference graph, as shown in Figure 7(b), and the engine temperature is determined by the heating power and operating time.
(a) Temperature rise curve under different ambient temperatures
(b) Temperature rise reference diagram
3.3.2. Study of Dual Power Source Compressor Model in High Ambient Temperature
When the ambient temperature is high and the passenger compartment has cooling demand , the driving mode of the dual power source air conditioning compressor is investigated in this study. Firstly, the energy saving potential of the dual power source air conditioning compressor is analyzed. When the dual power source compressor is used for hybrid drive, the demand power is allocated according to the operating condition, and the corresponding engine power and battery power are obtained. Then, the operating state of the engine is judged according to the result of power allocation. If the engine is on, the mechanical drive mode is adopted, and the engine provides the compressor power. If the engine is off, the electric drive mode is adopted, and the battery provides the compressor power. To verify that the use of a dual power compressor has energy-saving potential, the electric compressor and the dual power source compressor are simulated and compared under the cases of 30°C, 35°C, and 40°C refrigeration power, respectively, and the results are shown in Table 2.
As can be seen from Table 2, the hybrid drive has lower operating costs compared with the electric drive, and the higher the refrigeration power, the more significant the energy savings. This is because the higher the refrigeration power, the greater the losses due to low energy conversion efficiency, proving that the dual power source compressor has energy-saving potential.
At present, when determining the driving mode of a multipower source air conditioning compressor, the engine is first turned on or off according to the demand power of the operating condition. If the engine is turned on, the air conditioning compressor is driven mechanically; if the engine is turned off, the air conditioning compressor is driven electrically. The required power of the air conditioner and the required power of the operating condition are not considered jointly to determine the engine on and off, so as to determine the driving mode of the compressor. In this study, the way of jointly considering the power demand is used to determine the start or shutdown of the engine. The schematic diagram of not considering and considering the required power jointly is shown in Figure 8.
(a) No joint consideration of power
(b) Joint consideration of power
3.3.3. Establishment of an Energy Management Strategy
According to the results of the previous analysis, an adaptive ECMS considering the effect of temperature under different heat demands is established, as shown in Figure 9. The control process is as follows: (a)In order to improve the operating condition adaptability, a probabilistic neural network-based operating condition recognition model is established to determine the current operating condition type, and the equivalent factor value of the current vehicle operating condition is obtained by interpolation based on the current engine lubricant temperature and the current battery SOC(b)Interpolation of the penalty factor from the SOC values during vehicle operation(c)Selection of the appropriate equivalent fuel consumption calculation based on current engine operation condition(d)Determination of the ambient temperature. When the ambient temperature is medium, the power is allocated only according to the equivalence factor and penalty factor. If the ambient temperature is high and there is cooling demand, it is necessary to jointly consider the power demand of the whole vehicle to determine the operation mode of the compressor and allocate the power. If the ambient temperature is low, it is necessary to consider the temperature rise law of the engine to allocate the power(e)Calculation of the temperature change of the engine lubricating oil and the battery SOC change based on the power provided(f)Feed it back to step (a), and repeat steps (a)–(f) in a cycle
4. Simulation Analysis
4.1. Simulation under Normal Ambient Temperature
When there is no heat demand in a normal temperature environment, this study only considers the effect of engine lubricating oil temperature on the equivalent factor and uses the penalty factor to control the engine start/stop. To verify the effectiveness of the proposed energy management strategy in a normal temperature environment, two WLTC drive cycles were selected for simulation at an ambient temperature of 20°C and the initial battery SOC is set to 0.3. The results are shown in Figure 10 and Table 3.
(a) WLTC cycle condition identification results
(b) Engine start/stop status
(c) SOC
(d) Operating cost
From Figure 10, it can be seen that the recognition results of the WLTC drive cycle are urban conditions, suburban conditions, and high-speed conditions, which basically match with the actual WLTC condition composition, indicating that the probabilistic neural network-based condition recognition model meets the requirements. Compared with the traditional ECMS, the adaptive ECMS effectively reduces the number of engine starts and stops. The engine works longer, and more power is used to charge the battery. Therefore, the overall battery SOC will be high. Traditional ECMS takes a small value of the equivalent factor, where the battery will provide more power and the SOC is in a downward trend. Thus, its initial operating cost is low. The adaptive ECMS has a higher initial operating cost; nonetheless, with the growth of simulation time, the strategy reduces the number of engine starts and stops with less electricity consumption and reduces the overall vehicle operating cost by 5.69%, which shows that in this study, the effectiveness of the proposed adaptive ECMS is demonstrated.
4.2. Simulation under Low Ambient Temperature
When the ambient temperature is low and there is a heating demand, in order to verify the effectiveness of the adaptive ECMS, the WLTC drive cycle was selected for simulation at an ambient temperature of -20°C and an initial SOC of 0.3. The simulation results of the proposed strategy are compared with that of the PMP global optimization strategy and the traditional ECMS without using the engine temperature rise law, as shown in Figure 11 and Table 4.
(a) SOC
(b) Engine temperature
(c) Operating cost
From Figure 11 and Table 4, it can be seen that compared with the PMP global optimization strategy, the proposed adaptive ECMS increases the whole vehicle operating cost by 1.92% but has great real-time performance. Compared with the traditional ECMS, the strategy proposed in this study results in a 2.39% reduction in the overall vehicle operating cost. In the early stage of operation, the adaptive ECMS can utilize the engine waste heat faster, and the engine will work for a long time in the early stage, which leads to a higher operating cost compared with traditional ECMS. Nonetheless, since the traditional ECMS utilizes the engine waste heat slower, its operating cost will be higher than the proposed adaptive ECMS in the later stage of vehicle operation.
When the ambient temperature is -10°C and 0°C, the simulation results are shown in Table 5. Compared with the traditional ECMS, the adaptive ECMS under two different ambient temperatures has reduced the operating cost of the whole vehicle, but the reduction effect is not obvious. This is because the higher the ambient temperature, the smaller the heating power required, and the lower the benefit of using the engine waste heat faster.
4.3. Simulation under High Ambient Temperature
When the ambient temperature is high and there is cooling demand, in order to verify the effectiveness of the proposed adaptive ECMS, the proposed adaptive ECMS is simulated under the WLTC drive cycle, where the ambient temperature is 40°C and the initial SOC is 0.3; the simulation results are compared with those of the traditional ECMS, which does not consider the power demand of the whole vehicle, as shown in Figure 12 and Table 6.
(a) SOC
(b) Operating cost
(c) Compressor drive mode
From Figure 12 and Table 6, it can be seen that the adaptive energy management strategy in high-temperature environment reduces the operating cost by 2.01% compared with the traditional ECMS with comparable power consumption, and the compressor drive mode switches between mechanical drive and electric drive mode mutually, which proves the effectiveness of this energy management strategy. In order to verify the effectiveness of this strategy in high-temperature environment, simulation analysis was conducted at ambient temperature of 30°C and 35°C, and the results are shown in Table 7.
5. Conclusion
In this study, with REEV as the research object, an adaptive ECMS is proposed to improve the overall energy economy of the vehicle based on analyzing the effect of temperature on energy consumption in the CS operation stage. The effects of engine operating temperature and frequent starts and stops on fuel consumption, the effect of lubricant temperature on the equivalence factor, and the relationship between the start/stop penalty factor and SOC are investigated. Furthermore, the engine temperature rise law under the PMP global optimization strategy is extracted based on the influence of the heating demand on energy consumption under the low-temperature environment, and the energy saving potential of the dual power source compressor is studied based on the influence of the cooling demand on energy consumption under the high-temperature environment to reasonably determine the operation mode of the compressor under different working conditions. The simulation results under the WLTC drive cycle show that under normal temperature environment, the number of engine starts is reduced, and the running cost of the whole vehicle is reduced by 5.69% compared with strategies that ignore the influence of engine temperature and start-stop. In a low-temperature environment, the temperature of the engine rises at a reasonable rate during the warm-up process. Under different heating requirements, the operation cost of the whole vehicle is reduced by 0.53%-2.39%. In high ambient temperature, the compressor drive mode can switch between mechanical drive and electric drive, reducing the whole vehicle’s operating cost by 0.2%-2.01% under different cooling demands.
Data Availability
The authors elect to not share data.
Conflicts of Interest
The authors have no conflict of interest to declare.
Acknowledgments
This research was funded by the Fundamental Research Funds for the Central Universities under Grant No. 2022CDJDX-004.