Patent ID: 12258000

DESCRIPTION OF THE EMBODIMENTS

FIG.1is a structural schematic diagram of a hybrid power system in an embodiment of the disclosure.FIG.2is a block schematic diagram of the hybrid power system inFIG.1.FIG.3is a flowchart of an optimization method of the hybrid power system inFIG.2.

Referring toFIGS.1to3, a hybrid power system100of the disclosure includes a computing core110, a power converter120, a driving motor130, an engine generator140, a charging stand150, and a battery pack160.

The computing core110is, for example, a processor unit of a vehicle carrier and is used to receive signals from various sensors of the vehicle carrier to determine the operation status of the vehicle carrier. The computing core110is also used to switch to different modes corresponding with various operation statuses according to a built-in program, thereby fulfilling the objective of the hybrid power system100automatically switching between different modes.

The power converter120is coupled to the computing core110. The engine generator140is coupled to the power converter120. The driving motor130is coupled to the power converter120. The charging stand150is coupled to the power converter120. For example, when the vehicle carrier is parked at a charging station, an external electric energy source200is coupled to the charging stand150for charging. The battery pack160is coupled to the power converter120, wherein the battery pack160includes rechargeable batteries and adopts lead-acid batteries, nickel-metal hydride batteries, lithium-ion batteries, aluminum batteries, or fuel cells.

In addition, the battery pack160includes multiple battery units. When a charge capacity of one of the battery units is lower than a preset value, the discharging of the battery unit is stopped, and the battery unit is succeeded by another one of the battery units that has a charge capacity greater than the preset value for discharging so as to maintain power output.

Referring toFIGS.1to3, when inputting a required torque to the computing core110and switching to a charging mode, an electric energy source200is coupled to the charging stand150and provides power to the battery pack160through the power converter120to achieve the charging of the battery pack160. The computing core110executes an optimal power allocation algorithm.

Specifically, the power converter120of the disclosure has a multi-input single-output structure. “Multi-input” refers to multiple power input ends, and “single-output” refers to one power output end. The power converter120, in response to the charging actions of the charging stand150and the electric energy source200, realizes the optimal charging control of the battery pack160and the charging stand150through the optimal power allocation algorithm so as to reduce charging time and achieve the objective of energy saving.

Referring toFIGS.1to3, in an embodiment of the disclosure, the computing core110executes an optimal power allocation algorithm, and the engine generator140and/or the battery pack160provide power to the driving motor130to generate a dynamic force in case of inputting the required torque to the computing core110and switching to a driving mode.

With reference toFIGS.1and2, a deceleration mechanism170and a dynamometer180are further included. The deceleration mechanism170is connected to the driving motor130, and the dynamometer180is connected to the deceleration mechanism170. The deceleration mechanism170and the dynamometer180serve as load simulators, and the load sizes of the deceleration mechanism170and the dynamometer180are adjusted by controlling the supplied current so as to simulate the weight of the vehicle carrier and the friction resistance generated when driving or the gradient resistance when climbing a slope. This facilitates the improvement of the simulation accuracy of the hybrid power system100in executing the optimal power allocation algorithm.

The deceleration mechanism170further includes an encoder171and a torque meter172. The encoder171is connected to the driving motor130and used to measure the rotational speed of the driving motor130and feedback a signal to the computing core110. The torque meter172is connected between the encoder171and the dynamometer180, and feedbacks a torque value of the driving motor130to the computing core110.

The computing core110is adaptable to receive signal values from the encoder171and the torque meter172, thereby dynamically adjusting the energy output ratio of the engine generator140and the battery pack160so as to achieve the objective of minimizing energy consumption.

Referring toFIGS.1to3, an energy management optimization method of the hybrid power system100of the disclosure includes the following steps. In Step S1, the computing core110detects the required torque being 0, i.e., the hybrid power system100is not activated. In Step S2, the computing core110switches the hybrid power system100to a standby mode. In Step S3, when the hybrid power system100is switched to the charging mode, inputting a required torque to the computing core110first. In Step S4, the computing core110determines whether the required torque is greater than 0. In Step S5, the hybrid power system100is switched to the standby mode in response to a negative result. In Step S6, the computing core110of the hybrid power system100executes an optimal power allocation algorithm in response to a positive result.

FIG.4is a schematic diagram of a four-loop calculation of the optimal power allocation algorithm of the hybrid power system inFIG.3in a charging mode.

With reference toFIGS.1to4, the optimal power allocation algorithm establishes a four-loop formula and conducts a global search for a first loop E1(a total required power Pd), a second loop E2(a total required current Id), a third loop E3(a power ratio α), and a fourth loop E4(a minimum power consumption J1), for example, the search range of the total demand power Pd is 100 kW to 500 kW, the search range of the required current Id is 1 A to 48A, and the search range of the power ratio α is 0 to 1, and further uses a global grid search method to calculate multiple minimum power consumption J1of all conditions and outputs a multi-dimensional table.

Specifically, an efficiency optimization method of the hybrid power system100applies the global grid search (GGS) theory to obtain an optimal power ratio (PR). Thus, the power consumed by the hybrid power system100is used for comparison to obtain the optimal power allocation algorithm of the hybrid power system100. By using a target function program and a computing result from an optimal global search, a power ratio (α) of the battery pack160and a power ratio (1−α) of the charging stand150are thereby derived.

Referring toFIG.4, a function of the minimum power consumption is defined as J1=min[Vb*Ib/ηb+Vobc*Iobc/ηobc]+ω, Vbis an output voltage of the battery pack160. Vobcis an output voltage of the charging stand150. It is an output current of the battery pack160. Iobcis an output current of the charging stand150. ηbis a charging efficiency of the battery pack160. ηobcis a charging efficiency of the charging stand150, and ω is a penalty value for the state of charge SOC of the battery pack160. When the conditions of the grid search exceed a physical limit, i.e., the total required power of the battery pack160and the charging stand150, a penalty value ω is given so that the calculated minimum power consumption J1generates a maximum. This best value result is not adopted by the computing core110.

For example, a search range of the output voltage Vbof the battery pack160is 1V to 48V. A search range of the output voltage Vobcof the charging stand150is 1V to 48V. In terms of the charging efficiency ηobcof the charging stand150and the charging efficiency ηbof the battery pack160, a search range of the charging efficiency ηbis 1% to 50%. Multiple minimum power consumption J1of all conditions are calculated using the global grid search, and multi-dimensional table is output. The established multi-dimensional table is embedded into an energy management system. Parameters of the required power Pd, the required current Id, and the power ratio α of all conditions are input in order to find out an array of all minimum power consumption J1at the moment. Then, the relationship, to which a minimum power consumption J1in the array corresponds, between the output power of the battery pack160and the output power of the engine generator140and the charging efficiencies ηobcand ηbis to be found.

Referring toFIGS.1to4, an array of values of the corresponding minimum power consumption J1is obtained by inputting particular values of the total required power Pd, the total required current Id, and the power ratio a to the multi-dimensional table so as to find an optimal power ratio α in the array of values. In addition, when computing under different operating conditions (i.e., different total required power Pd, total required current Id, and power ratio α), a function of the minimum power consumption J1may be obtained through the integration of all minimum power consumption of the hybrid power system100. The function is a two-dimensional-table formula for the optimal power ratio α: J1*(a,b)=min J1(a,b,c). Through the two-dimensional-table formula for the optimal power ratio α, a multi-dimensional table may be produced, which may be directly imported into a controller of the vehicle carrier for charging optimization and energy management, thereby achieving the effect of minimizing the charging power consumption and reducing charging time.

Referring toFIGS.1to3, the energy management optimization method of the hybrid power system100of the disclosure includes the following steps. In Step S1, the computing core110detects the required torque being 0, i.e., the hybrid power system100is not activated. In Step S2, the computing core110switches the hybrid power system100to the standby mode. In Step S7, when the hybrid power system100is switched to the driving mode, a required torque is first input to the computing core110. In Step S8, the computing core110determines whether the required torque is greater than 0. In Step S9, the hybrid power system100is switched to the standby mode in response to a negative result, and the computing core110of the hybrid power system100executes the optimal power allocation algorithm in response to a positive result.

In short, when a charge capacity of the battery pack160is greater than a preset value, the engine generator140is not activated and the battery pack160is continuously discharged to the driving motor130. When the charge capacity of the battery pack160is less than a preset value, the engine generator140is activated, and the battery pack160is charged through the power converter120. Further, the battery pack160is continuously discharged to the driving motor130, thereby performing an optimal deployment of electric energy supply to improve the endurance of the vehicle for driving.

FIG.5is a schematic diagram of a four-loop calculation of the optimal power allocation algorithm of the hybrid power system inFIG.3in a driving mode.

Referring toFIGS.1,2,3, and5, when the computing core110executes the optimal power allocation algorithm, a four-loop formula is established, with a first loop F1being the required power Pd, a second loop F2being the total required current Id, a third loop F3being a dynamic force ratio B, and a fourth loop F4being a function of the minimum equivalent consumption, wherein the function of the minimum equivalent consumption is defined as J2=min[me/ηg+f(SOC)*mb]+γ.

Specifically, me is an actual fuel consumption of the engine generator140. ηgis a generator efficiency of the engine generator140. mbis an equivalent fuel consumption of the battery pack160, and f(SOC) is a weight of a battery charging state. The optimal power allocation algorithm utilizes an equation, i.e. the target function, to present a fuel consumption of the whole vehicle as the equivalent fuel consumption. The steps of an algorithm concerning an equivalent consumption minimization strategy (ECMS) are provided below.

As shown inFIG.5, a global search is conducted for the discretized total required power Pd, the total require current Id, and the dynamic force ratio β. For example, the search range of the total required power Pd is 100 kW to 500 kW, the search range of the total require current Id is 1 A to 48A, and the search range of the dynamic force ratio β is 0 to 1. Finally, the equivalent fuel consumption of all conditions is calculated with the global grid search method in an off-line environment, and a multi-dimensional table of the equivalent fuel consumption is established. The established multi-dimensional table is embedded into the energy management system. An array of all equivalent fuel consumption at the moment is found out according to conditions concerning parameters input at the moment, wherein the parameters include the required power Pd, the required current Id, and the dynamic force ratio β. Then, the relationship, to which a minimum equivalent fuel consumption in the array corresponds, between the output power of the battery pack160and the output power of the engine generator140and the current is to be found.

In summary, the hybrid power system of the disclosure is applicable to a vehicle carrier, and the hybrid power system has an engine generator, a driving motor, and a battery pack. The engine generator is only used to provide power to the battery pack, and then the power from the battery pack is output to the driving motor through a power converter to generate a dynamic force. Since the only source of dynamic force generation is the driving motor, a dynamic force level may be controlled by adjusting a current value input to the driving motor. In comparison with existing hybrid electric vehicles, equipping the engine generator with a transmission and a dynamic force transmission system is not required. Thus, an installation position of the engine generator in the vehicle carrier is relatively flexible. In addition, the engine generator of the disclosure operates only for electricity generation. Thus, controlling a sewage discharge level of the engine generator is less challenging.

The hybrid power system of the disclosure, when in the charging mode, is charged through the battery pack and the charging stand via the power converter. The hybrid power system also obtains the optimal power ratio in the charging mode through the global grid search theory, thereby achieving the objective of minimizing the charging power consumption and reducing charging time.

Further, the hybrid power system of the disclosure adopts the optimal power allocation algorithm in the driving mode to achieve an optimal energy consumption allocation in a dual power structure consisting of the engine generator and the battery pack, thereby improving an operational endurance of the hybrid power system. The operational endurance of the hybrid power system is also improved by enabling the hybrid power system to automatically deploy the dual power output ratio of the engine generator and the battery pack through the optimal power allocation algorithm, further avoiding damage and safety problems resulted from overcharge and overdischarge of the battery pack.