LU504997B1 - An online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization - Google Patents

Abstract

The invention discloses an online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization, including the following steps: Step 1: Propose a strategy: a strategy to limit the operation of PEMFCS to a specific power level; Step 2: Adjust output power: PEMFCS will adjust its output power level according to the SOC of the battery; Step 3: Determine the shifting strategy: Adopt a shifting strategy of the gearbox, the battery SOC needs to be kept in a intermediate interval, the present invention combines rules and optimization, incorporating rules as constraints into the cost optimization model, short-distance driving cycles, long-distance driving cycles, and commuting driving cycles were studied in order to take into account the actual driving conditions throughout the vehicle life cycle. Energy management strategy combining rules and optimization outperformed the others in all driving conditions, benefiting from fewer fuel cell system start-stop cycles and increased opportunities to operate in high-efficiency areas.

Description

AN ONLINE REAL-TIME ENERGY MANAGEMENT METHOD FOR FUEL CELL HYBRID ELECTRIC

VEHICLES COMBINING RULES AND OPTIMIZATION 7504997

Technical Field

The present invention relates to the technical field of vehicle energy management, in particular to an online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization.

Background Technique

Fuel cells can be divided into polymer electrolyte membrane fuel cell (PEMFC), solid oxide fuel cell (SOFC), alkaline fuel cell (AFC), molten carbonate fuel cell (MCFC) and phosphoric acid fuel cell (PAFC), among which, PEMFC has received the most research and development attention due to their operation at relatively low temperatures (-40°C to 120°C), making them suitable for portable, transportation, and fixed electric power applications. Fuel cell hybrid electric vehicles (FCHEVs) have advantages of zero pollution and gaseous emissions such as electric vehicle (EV), while also offering longer driving mileage and shorter refueling times, however, FCHEV has the disadvantage of high costs, mainly due to operational costs related to vehicle fuel cells and battery systems, which account for a large part of the total cost of ownership, operating costs are not only hydrogen consumption, but also the degradation cost of the fuel cell and the battery, due to the fragility and high cost of fuel cell and battery systems, excessive load will shorten their service life, thereby increasing the maintenance cost of the vehicle, that is, the degradation cost of the fuel cell and the battery. The degradation cost of the fuel cell accounts for a considerable proportion of the total operating cost. The degradation of the fuel cell system is mainly affected by various operating conditions, such as start-stop cycles, high and low loads, and load shifts, energy management strategy (EMS) can effectively reduce hydrogen consumption costs and mitigate the degradation of power system by adjusting power distribution between fuel cell systems and batteries.

EMS allocates output power among different power sources to reduce cost and meet power demand, power follower strategy and thermostat strategy are two typical deterministic rule- based control methods, for power sources after EMS (PFEMS), which required the power supply is mainly provided by the fuel cell system, and any shortfall is covered by the battery [10], PFEMS usually leads to high hydrogen consumption, because the fuel cell system usually operates outside the high-efficiency area. In addition, PFEMS causes significant load changes, which accelerate the degradation of fuel cell stack. The thermostat EMS (THEMS) controls the fuel cell to operate at a constant power point unless the state of charge of the battery is close to its allowable boundary, otherwise, the power of the fuel cell remains constant. It is very important to determine the constant power, because the optimal the constant power depends on the driving conditions, but the control rules will not change with the driving conditions, resulting in a poor energy-saving rate of the vehicle.

In summary, traditional energy management methods have the following disadvantages:

Hybrid electric vehicles conduct short-distance driving cycles (typically under 30 km) to develop and test EMS strategies, however, this narrow focus on short distances leads to underutilization of battery energy, increased hydrogen consumption, and the degradation of fuel cell, and during short-distance driving, frequent start-stop cycles lead to the degradation of fuel cell, reducing the management efficiency of energy management methods.

Contents of the Invention

The purpose of the present invention is to provide an online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization to solve the problem that hybrid electric vehicles conduct short-distance driving cycles (typically under 30 km) to develop and test EMS strategies proposed in the above-mentioned background technology.

However, this narrow focus on short distances leads to an underutilization of these resources, battery capacity, increased hydrogen consumption, and the degradation of fuel cell, moreover, during short driving distances, frequent start-stop cycles can lead to degradation of fuel cells, thus reducing the management efficiency of energy management methods.

In order to achieve the above purpose, the present invention provides the following technical solution: an online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization, including the following steps:

Step 1: Propose a strategy: A strategy to limit the operation of PEMFCS to a specific power level;

Step 2: Adjust output power: PEMFCS will adjust its output power level according to the SOC of the battery;

Step 3: Determine the shifting strategy: Adopt a shifting strategy similar to that of a traditional gearbox. The battery SOC needs to be kept in an intermediate area or range, otherwise, the performance of the battery will decline rapidly. Therefore, the battery SOC is used as the shifting state of PEMFCS, and the downshift point is SOCH1, SOCH2, SOCH3, and SOCH4, while the upshift points are represented by SOC L1, SOCL2, SOCL3, and SOCL4;

Step 4: Determine the adjustment coefficient: Determine two coefficients related to SOC,

BH, and BL;

LU504997

Step 5: Establish an optimization model: Assuming a time step of 1 second, the cost of operating increments at each time step is represented as

AC, = AC ju, "AC, +AC,, -Pe 4 Tals | Velde | VolioYon!| Aol

NelHVy ng JH AUgy pe 2Npo Oca

By minimizing the equation, at each time step, the total operating cost is expected to decrease, but this optimization may result in the battery's SOC exceeding its allowable range of changes, BH and BL are introduced to adjust the objective function. The corrected objective function is represented as

AC, = Yule, ¥uly |, Publ PAU | Vols le

Bun LHV Bin LHV ar AU po. je 2N 501 Qcen

When the battery SOC approaches the allowable lower limit, the battery output power should be minimized, therefore the coefficient BL increases the cost of using batteries;

Step 6: Revise the optimization model for the first time: simplify the expression to,

By =a(SOC - SOC, ), Bu = (soc, —SOC) where a is the slope of the linear function, and at the same time, these two coefficients are limited to changes between 0.01 and 1. Considering that the lower and upper boundaries of SOC are set to SOCL=0.2 and SOCH=0.8;

Step 7: Revise the optimization model for the second time: In order to maintain the SOC of the battery within the allowable range, it is necessary to adjust the power output of PEMFCS. It only takes effect when the SOC of the battery approaches the allowable boundary. Finally, REMS is represented by the OEMS optimization model as follows minAC, = rule wh BaPiY gs SUg | Biel » P, Ban eLEV Bing LHVy AUtone 2NupOu

SIP, =0kW,or6kW <P <P, ..

B, = a(SOC — SOC, ), B,, =a(SOC,, —SOC)

Step 8: Adjust the power: Modify the fixed power of PEMFCS under REMS conditions to the power range;

Step 9: Select detailed power: Use the optimization model in OEMS to select the detailed power of PEMFCS from the power range. The specific rules are represented as follows,

I = PR, SOC > SOC #1 pe =[P,P,} SOC; < SOC < SOC p\ 1 =[P,P|SOC,, < SOC < SOC, 1, =|P,, RB] SOC < SOC, ,

Step 10: Revise the optimization model for the third time: ROCEMS is represented by the following optimization model, which integrates the rules shown in equation (23) under the 94997 constraints of the optimization model, minAC, = rule wh BaPiY gs SUg | Biel

Ph Ban, LHVy Bin LHV 7 AU por. 2N por Qeen stif SOC =0.8, thenP, = 0kW if 0.7< SOC <0.8, thenP, =0kWor6kW if 02<SOC<0.7, thenP,, =0kWor6kW <P, <12kW if SOC<0.2, then12kW < Pr <30kW

B, = a(SOC — SOC, ), Br = a(SOC, — SOC)

Compared with the OEMS optimization model, the ROCEMS optimization model has added more constraints, which are based on the rules of battery SOC and the same enumeration method is used to solve the optimization model.

The specific power level in Step 1 is specifically that PEMFCS is limited to operate at five power levels: P1=0 kW off state, P2=6 kW, P3=12 kW, P4=24 kW, and P5=30 kW maximum power.

The P2 and P4 are critical points in the high-efficiency area, with 20% and 80% of their maximum power. The purpose is to maintain PEMFCS to operate as much as possible in the high-efficiency area, which corresponds to power levels P2 to P4, compared to the low-efficiency area, the degradation rate of these power levels is lower, and the degradation rate of PEMFCS in the off state P1 is zero. However, it may lead to an increase in degradation in the future, and PEMFCS will only be turned off when the battery SOC reaches the upper limit to avoid unnecessary degradation.

The output power in Step 2 is specifically to operate in the high-efficiency area P2 to P4, reducing hydrogen consumption and degradation, only ascending to the maximum power P5.

Whenthe battery SOC reaches the lower limit, it indicates a high power demand from the vehicle, and PEMFCS operates at its highest efficiency point while avoiding low-efficiency areas.

The optimization in Step 5 uses an enumeration method to find an approximate global optimum, and the design variable Pfc is discretized using a regular grid with a grid width of 1 kW.

The objective function value is calculated at each discrete point, and the point with the smallest objective function value is selected as the optimal solution.

Adjusting the power in Step 8 is specifically to utilize the two adjustment coefficients BH and

BL to control the SOC of the battery, and they only take effect when the SOC of the battery approaches the allowable boundary.

Compared with the existing technology, the beneficial effects of the present invention are:

Combining rules with optimization and incorporating rules as constraints into the cost optimization model, to consider the actual driving conditions throughout the entire vehicle life cycle, short-distance driving cycles, long-distance driving cycles, and commuting driving cycles 5 were studied. The energy management strategy combining rules and optimization outperformed other strategies under all driving conditions, thanks to fewer start-stop cycles of fuel cell systems and increased opportunities for operation inefficient areas.

Brief Description of the Drawings

Fig. 1: The structural schematic diagram of the present invention;

Fig. 2: The displacement diagram of PEMFCS of the present invention;

Fig. 3: The flowchart of REMS of the present invention;

Fig. 4: The shift point diagram of the battery SOC in REMS of the present invention;

Fig. 5: The coefficient diagram of the adjustment of the present invention changing with the change of battery SOC;

Fig. 6: The simulation result diagram of the present invention for two repeated LA92 driving cycles;

Fig. 7: The detailed operating cost diagram for each EMS of the present invention;

Fig. 8: The speed and power curve diagram of the comprehensive long driving cycle of the present invention;

Fig. 9: The resulting diagram of the long-distance driving cycle of the present invention;

Fig. 10: The detailed operating cost diagram of the long-distance driving cycle of the present invention;

Fig. 11: The resulting diagram of the driving cycle of the commuter HWFET of the present invention;

Fig. 12: The speed curve and PEMFCS power diagram during a single HWFET trip of the present invention;

Fig. 13: The detailed operating cost diagram of the commuter driving cycle of the present invention.

Specific Embodiments

The following will provide a clear and complete description of the technical solutions in the examples of the present invention, in conjunction with the examples of the present invention.

Obviously, the described examples are only a part of the examples of the present invention, not all of them. Based on the examples in the present invention, all other examples obtained by ordinary technicians in the field without creative labor fall within the scope of protection of the a LU504997 present invention.

Please refer to Figures 1-13. The present invention provides an online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization, including the following steps:

Step 1: Propose a strategy: a strategy to limit the operation of PEMFCS to a specific power level;

Step 2: Adjust output power: PEMFCS will adjust its output power level according to the SOC of the battery;

Step 3: Determine the shifting strategy: Adopt a shifting strategy similar to that of a traditional gearbox. The battery SOC needs to be kept in an intermediate area or range, otherwise, the performance of the battery will decline rapidly. Therefore, the battery SOC is used as the shifting state of PEMFCS, and the downshift point is SOCH1, SOCH2, SOCH3, and SOCH4, while the upshift points are represented by SOC L1, SOCL2, SOCL3, and SOCL4;

Step 4: Determine the adjustment coefficient: Determine two coefficients related to SOC,

BH, and BL;

Step 5: Establish an optimization model: Assuming a time step of 1 second, the cost of operating increments at each time step is represented as

AC, = AC ju, +AC, , +AC,, = TB, YuPs | YeProc NU e | VolioYon!| Aol

NelHVy ng JH AUgy pe 2Npo Oca

By minimizing the equation, at each time step, the total operating cost is expected to decrease, but this optimization may result in the battery's SOC exceeding its allowable range of changes, BH and BL are introduced to adjust the objective function. The corrected objective function is represented as ac, —— tube yup Pair Fan AU | Vl ica]

Bun LHV Bi LHV ar AU po. je 2N 501 Oa

When the battery SOC approaches the allowable lower limit, the battery output power should be minimized, therefore the coefficient BL increases the cost of using batteries;

Step 6: Revise the optimization model for the first time: simplify the expression to, where a is the slope of the linear function, and at the same time, these two coefficients are limited to changes between 0.01 and 1. Considering that the lower and upper limits of SOC are set to

SOCL=0.2 and SOCH=0.8;

Step 7: Revise the optimization model for the second time: To maintain the battery's SOC within the allowable range, it is necessary to adjust the power output of PEMFCS. It only takes

LU504997 effect when the SOC of the battery approaches the allowable boundary. Finally, REMS is represented by the OEMS optimization model as follows

P P AU Ei minAC., _ Yauf ge + Yalı + PnB1Y imax Le, Vs | ven]

Pr Bun LH Va Bing LHV, AU por, fe 2N xorQecen

SIP, =0kW,or6kW <P <P, ..

B, =a(SOC — SOC, ), B,, =a(SOC, — SOC)

Step 8: Adjust the power: Modify the fixed power of PEMFCS under REMS conditions to the power range;

Step 9: Select detailed power: Use the optimization model in OEMS to select the detailed power of PEMFCS from the power range. The specific rules are represented as follows,

I = PR, SOC > SOC #1 p.17 [P,P,} SOC; < SOC < SOC p\ € 1 =[P,P|SOC,, < SOC < SOC, 1, =|P,, RB] SOC < SOC, ,

Step 10: Revise the optimization model for the third time: ROCEMS is represented by the following optimization model, which integrates the rules shown in equation (23) under the constraints of the optimization model, min AC, _ Yule + ub, + Buby po.max AU 1 + VE i]

Pr Bun LH, Bing, LHV AU por, fe 2N por Oven stif SOC =0.8, thenP, = OkW if 0.7< SOC <0.8, thenP, = 0kWor6kW if 02<SOC<0.7, thenP,, =0kWor6kW <P, <12kW if SOC <02, then12kW <P, <30kKW

B, =a(SOC -S0C,), B =a(SOC,, — SOC)

Compared with the OEMS optimization model, the ROCEMS optimization model has added more constraints, which are based on the rules of battery SOC and the same enumeration method is used to solve the optimization model.

The specific power level in Step 1 is specifically that PEMFCS is limited to operate at five power levels: P1=0 kW off state, P2=6 kW, P3=12 kW, P4=24 kW, and P5=30 kW maximum power.

The P2 and P4 are critical points in the high-efficiency area, with 20% and 80% of their maximum power. The purpose is to maintain PEMFCS to operate as much as possible in the high-efficiency area, which corresponds to power levels P2 to P4, compared to the low-efficiency area, the degradation rate of these power levels is lower, and the degradation rate of PEMFCS in the off state P1 is zero. However, it may lead to an increase in degradation in the future, and PEMFCS will only be turned off when the battery SOC reaches the upper limit to avoid unnecessary degradation.

The output power in Step 2 is specifically to operate in the high-efficiency area P2 to P4, reducing hydrogen consumption and degradation, only ascending to the maximum power P5.

When the battery SOC reaches the lower limit, it indicates a high power demand from the vehicle, and PEMFCS operates at its highest efficiency point while avoiding low-efficiency areas.

The optimization in Step 5 uses an enumeration method to find an approximate global optimum, and the design variable Pfc is discretized using a regular grid with a grid width of 1 kW.

The objective function value is calculated at each discrete point, and the point with the smallest objective function value is selected as the optimal solution.

Adjusting the power in Step 8 is specifically to utilize the two adjustment coefficients BH and

BL to control the SOC of the battery, and they only take effect when the SOC of the battery approaches the allowable boundary.

Embodiment 1:

In the present invention, PEMFCS are initially kept off-state for REMS, and when the battery

SOC drops to 0.35, it starts at around 6 kW. Subsequently, the battery SOC gradually drops to 0.3, and PEMFCS shifts to 12 kW until the end of the driving cycle, resulting in the battery SOC gradually increasing to 0.59, and in the case of DP-REMS, PEMFCS initially started from 12 kW and transitioned to 11 kW after 600 seconds; the battery SOC gradually increased to above 0.7, after which PEMFCS reduced its power output until stopped, and REMS results in an initial drop and subsequent increase in battery SOC, whereas DP-REMS initially increases and then decreases battery SOC, eventually converging to a similar level at the end of the driving cycle, In OEMS, when the battery SOC is around 0.2, PEMFCS start at 30 kW after 900 seconds, causing a rapid increase in battery SOC to 0.7, from 1500 seconds on, PEMFCS power drops to 6 kW until the end of the driving cycle, resulting in a gradual decrease in battery SOC to 0.65. DPOEMS has the

PEMFCS power profile similar to DP-REMS, but it keeps PEMFCS active longer due to the higher final battery SOC, for ROCEMS, PEMFCS start at 12 kW and 900 s and maintains this power level until the end of the driving cycle, ROCEMS's The battery SOC profile is similar to REMS, but the former leads to a lower minimum SOC value of the battery and finally returns to 0.5; in the case of DP-ROCEMS, the power distribution of PEMFCS is similar to DP-REMS, but DP-ROCEMS closed

PEMFCS earlier, with the aim of achieving a final battery SOC of 0.5. In terms of operating costs,

OEMS is significantly more costly than other strategies; this is because OEMS allows PEMFCS to operate at 30 kW, which is an low-efficiency and areas of high degradation; on the other hand, 094997

ROCEMS achieves the lowest equivalent cost among the three strategies by maximizing the operating time of PEMFCS in the highest efficiency range, and DP-REMS and DP-ROCEMS perform slightly better than REMS and ROCEMS, but the difference is negligible, see detailed cost breakdown for each EMS, Equivalent hydrogen consumption cost (FC-C + B-C) is very similar for all strategies except OEMS, ranging from $1.83 to $1.85,due to prolonged operation time in low- efficiency areas, OEMS lead to higher hydrogen consumption costs, and in terms of degradation costs, ROCEMS is the lowest and OEMS is the highest; therefore, OEMS has the highest total operating cost at $3.17, followed by REMS at $2.77, and ROCEMS at $2.75, the operating cost of the DP-based strategy is slightly lower than that of REMS and ROCEMS, respectively, but the difference is minute and negligible. Therefore, the proposed REMS and ROCEMS exhibit almost the same optimal performance as DP, and the analysis shows that among all EMS strategies, the hydrogen consumption cost and degradation cost of PEMFCS accounted for about 65% and 25% of the total operating cost, respectively. It is obvious that the start-stop cycle significantly leads to the degradation cost of PEMFCS, as shown by the sharp increase in operating cost, so, for real driving conditions throughout the FCHEV's life cycle, reducing the frequency of start-stop cycles and ensuring that PEMFCS operates in the high-efficiency area are key measures to minimize the total operating cost.

Embodiment 2:

In the present invention, long-distance driving cycles are used to assess the operating costs of taxis, buses, and coach commercial vehicles; in this study, the FCHEV under investigation is considered a taxi because it is classified as a light vehicle, in order to take into account various standard driving cycles, the driving cycles of LA92, NEDC, HWFET, and UDDS were combined in sequence to form an extended driving cycle, in order to simulate the daily operation of a taxi, the joint long driving cycle was repeated 10 times, with a total driving distance of 550 km, due to the high computational cost of the DP method over the entire 10 repeated driving cycles (about 13 hours of operating time), an improved method was used to obtain the solution for a single combined long driving cycle using the DP method, and then apply this solution to subsequent driving cycles. Considering the computational efficiency, we only considered the DP-ROCEMS strategy because it is the least costly to operate among the three strategies; In order to align the

SOC of the battery with that of ROCEMS at the end of 10 repeated driving cycles, the battery SOC of the DP method needs to be adjusted,, for example, the final battery SOC of ROCEMS is 0.7, and the initial value is 0.5, then DP-ROCEMS sets the final battery SOC of a single integrated driving cycle to 0.52, ensuring a final battery SOC of 0.7 after 10 repeated driving cycles, the OEM Or strategy exhibits more start-stop cycles compared to other strategies, resulting in an incremental increase in PEMFCS degradation cost with each start cycle, In addition, OEMS run PEMFCS at maximum power for a certain period, resulting in higher hydrogen consumption cost and PEMFCS degradation cost, on the other hand, REMS and ROCEMS strategies show similar power profiles for PEMFCS, PEMFCS operate at 6 kW most of the time, operating occasionally at 12 kW, thereby reducing operating costs, and for DP-ROCEMS, PEMFCS power was varied between 7 kW and 8 kW, while the SOC of the battery was gradually varied to match the ROCEMS value at the end of the driving cycle.

Embodiment 3:

In the present invention, it is assumed that the user drives home to the office at 7:00 in the morning, and returns home at 6:00 pm on a working day. Each trip is represented by a standard driving cycle. Here, the NEDC driving cycle is taken as an example, and the simulation duration is set to one week, consisting of 5 working days, during which there are 10 standard driving cycles with several hours of downtime between each successive driving cycle, simulation results for a commuter NEDC driving cycle lasting one week, with the x-axis to represent the number of days;

Observe that PEMFCS is not required for every trip, for REMS, PEMFCS is started 5 times, while for OEMS and ROCEMS, it is started only 3 times, which means that REMS activates PEMFCS every — 2 times and OEMS and ROCEMS activates every 3 times. During a trip without PEMFCS start, the vehicle relies entirely on the battery, resulting in a rapid drop in battery SOC; on the contrary, when PEMFCS is started during a trip, the battery SOC increases rapidly, and DP adopts two different strategies to generate reference solution, the first strategy, DP-S, uses DP to generate a reference solution for a single trip and then repeats this solution for each trip, and the second strategy, DP-ROCEMS, utilizes DP to generate solutions only for trips initiated by PEMFCS under the control of ROCEMS, therefore, the operation of PEMFCS in the database is consistent with that of ROCEMS, Because the PEMFCS start with trip of all ems in 5th, in the simulation results, for DP-S and DP-ROCEMS, PEMFCS start from the trip; however, DP-S runs for a shorter time on

PEMFCS compared to the database; this is because DP-S aims to achieve a final battery SOC equal tothe initial value of 0.5, while DP-ROCEMS allows the battery SOC increases from 0.27 to 0.72, which requires longer operation time for PEMFCS, for the proposed EMS strategy, they start

PEMFCS at the later stage of the trip, and continue to operate until the battery SOC reaches the upper limit of 0.8, at this stage, OEMS mainly operates PEMFCS at its maximum power allowing for faster battery charging and early shutdown; PEMFCS in ROCEMS operate at a peak efficiency point of 12kW most of the time, reducing hydrogen consumption costs. In contrast, REMS enables 794997

PEMFCS to operate at 6 kW, thereby extending the charging time of the battery.

In the present invention, by considering the degradation of PEMFCS and battery to minimize the total operating cost of FCHEV, REMS adopts a shift strategy based on battery SOC to reduce costs, and OEMS optimizes the instantaneous cost by adjusting the battery SOC coefficient adjustment weight; ROCEMS combines features of REMS and OEMS to prioritize targets, assessed under different driving conditions: short-distance driving cycle, long-distance driving cycle, and commuting driving cycle, for short- and long-distance driving cycles, REMS and ROCEMS performs similarly to offline dynamic programming (DP) solutions in terms of operating cost, however,

OEMS is significantly more costly to operate as PEMFCS operates longer in low-efficiency areas and generates more start-stop cycles, in the commuting driving cycle, REMS has the highest operating costs, while ROCEMS has the lowest operating cost, compared with other strategies,

REMS has more PEMFCS start-stop cycles, resulting in higher PEMFCS degradation costs. In order to minimize the operating costs in continuous driving cycles, it is crucial to improve the efficiency of PEMFCS operating points, for the commuting driving cycle, reducing the start-stop cycle of

PEMFCS is key; ROCEMS outperforms other strategies by improving the average work efficiency, the efficiency of PEMFCS and the reduction of start-stop cycles under different driving conditions make it the overall best performing strategy.

Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the field can still modify the technical solutions described in the aforementioned embodiments, or perform equivalent replacements for some of the technical features. Within the spirit and principles of the present invention, any modifications, equivalent replacements, improvements, etc., shall be included in the protection scope of the present invention.

Claims (5)

Claims LU504997

1. An online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization, characterized in that, including the following steps: Step 1: Propose a strategy: a strategy to limit the operation of PEMFCS to a specific power level; Step 2: Adjust output power: PEMFCS will adjust its output power level according to the SOC of the battery; Step 3: Determine the shifting strategy: Adopt a shifting strategy similar to that of a traditional gearbox. The battery SOC needs to be kept in a intermediate area or range, otherwise the performance of battery will decline rapidly. Therefore, the battery SOC is used as the shifting state of PEMFCS, and the downshift point is SOCH1, SOCH2, SOCH3, and SOCH4, while the upshift points are represented by SOC L1, SOCL2, SOCL3, and SOCL4; Step 4: Determine the adjustment coefficient: Determine two coefficients related to SOC, BH and BL; Step 5: Establish an optimization model: Assuming a time step of 1 second, the cost of operating increments at each time step is represented as AC, = AC ju, "AC, +AC,, -Pe 4 Tals | Velde | VolioYon!| Aol NelHVy ng JH AUgy pe 2Npo Oca By minimizing the equation, at each time step, the total operating cost is expected to decrease, but this optimization may result in the battery's SOC exceeding its allowable range of changes, BH and BL are introduced to adjust the objective function. The corrected objective function is represented as AC, = Yule, ¥uly |, Publ PAU | Vols le Bun LHV Bin LHV ar AU po. je 2N 501 Qcen When the battery SOC approaches the allowable lower limit, the battery output power should be minimized, therefore the coefficient BL increases the cost of using batteries; Step 6: Revise the optimization model for the first time: simplify the expression to, B, = a(SOC = SOC, ), B, = (SOC, — SOC), where a is the slope of the linear function, and at the same time, these two coefficients are limited to changes between 0.01 and 1. Considering that the lower and upper boundaries of SOC are set to SOCL=0.2 and SOCH=0.8; Step 7: Revise the optimization model for the second time: In order to maintain the SOC of the battery within the allowable range, it is necessary to adjust the power output of PEMFCS. It only takes effect when the SOC of the battery approaches the allowable boundary. Finally, REMS is represented by the OEMS optimization model as follows 7904997 minac, = 7 SN 7 NY. 7.27. CS 1 1 Py Pun LHVy Bin LHV 7 AU por. 2N por Qeen SIP, =0kW,or6kW <P <P, .. B, = SOC — SOC, ), B,, = alSOC — SOC) Step 8: Adjust the power: Modify the fixed power of PEMFCS under REMS conditions to the power range; Step 9: Select detailed power: Use the optimization model in OEMS to select the detailed power of PEMFCS from the power range. The specific rules are represented as follows, 1, = R,SOC > SOC #1 a 301143 L4 — H3 I, =|P,, RB] SOC < SOC, Step 10: Revise the optimization model for the third time: ROCEMS is represented by the following optimization model, which integrates the rules shown in equation (23) under the constraints of the optimization model, minac,, = vibe vb BaBiY em SU, YoFo)fcul Pr Bun LH Var Bing 4 LHV, AU po, fe 2N por Oca stif SOC =0.8, thenP, = 0kW if 0.7< SOC <0.8, thenP, = 0kWor6kW if 02<SOC<0.7, thenP,, =0kWor6kW <P, <12kW if SOC<0.2, then12kW <P, <30kW B, = SOC -80C,), B, = a(SOC, —SOC) Compared with the OEMS optimization model shown in the equation, the optimization model of ROCEMS is regarded as the original optimization model of OEMS with more constraints, the constraints are based on the rules of the battery SOC, and the same enumeration method is used to solve the optimization model.

2. An online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization according to claim 1, it is characterized in that: The specific power level in Step 1 is specifically that PEMFCS is limited to operate at five power levels: P1=0 kW off state, P2=6 kW, P3=12 kW, P4=24 kW, and P5=30 kW maximum power. The P2 and P4 are critical points in the high-efficiency area, with 20% and 80% of their maximum power. The purpose is to maintain PEMFCS to operate as much as possible in the high-efficiency area, which corresponds to power levels P2 to P4, compared to the low-efficiency area, the degradation rate 9997 of these power levels is lower, and the degradation rate of PEMFCS in the off state P1 is zero. However, it may lead to an increase in degradation in the future, and PEMFCS will only be turned off when the battery SOC reaches the upper limit to avoid unnecessary degradation.

3. An online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization according to claim 1, it is characterized in that: The output power in Step 2 is specifically to operate in the high-efficiency area P2 to P4, reducing hydrogen consumption and degradation, only ascending to the maximum power P5. When the battery SOC reaches the lower limit, it indicates a high power demand from the vehicle, and PEMFCS operates at its highest efficiency point while avoiding low-efficiency areas.

4. An online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization according to claim 1, it is characterized in that: The optimization in Step 5 uses an enumeration method to find an approximate global optimum, and the design variable Pfc is discretized using a regular grid with a grid width of 1 kW. The objective function value is calculated at each discrete point, and the point with the smallest objective function value is selected as the optimal solution.

5. An online real-time energy management method for fuel cell hybrid electric vehicles combining rules and optimization according to claim 1, it is characterized in that: Adjusting the power in Step 8 is specifically to utilize the two adjustment coefficients BH and BL to control the SOC of the battery, and they only take effect when the SOC of the battery approaches the allowable boundary.