What Should Be Fuel Economy of Hybrid Electric Vehicle at Different Gear Ratios UPDATED

What Should Be Fuel Economy of Hybrid Electric Vehicle at Different Gear Ratios

Abstruse

Free energy conservation and emissions reduction have become increasingly pregnant for automobiles due to the severity of the electric current energy situation. Hybrid electric vehicle (HEV) technology is one of the most promising solutions. This study investigated the total efficiency of a HEV powertrain. To meliorate the total efficiency, the engine should be regulated to piece of work at its highest efficiency and bulldoze the wheels directly as much as possible. To achieve this, we developed an energy management strategy based on the straight drive surface area (DDA) of the engine's efficiency map. Several typical HEV models were built to compare the fuel consumption using DDA and rule-based strategies. Furthermore, the part of the HEV transmission arrangement with DDA was considered. The transmission in a HEV should regulate the engine to work at its highest efficiency as much as possible, which is rather different than the regulation in an internal combustion engine vehicle. The functional change may lead to transmission systems with fewer gears but optimal gear ratios. If this tendency is realized, the manufacturing cost of HEVs could be largely reduced.

Introduction

Transportation demands are increasing as modernistic cities continue to grow quickly [ane,2,3,4]. This trend leads to increased pollution, energy shortages, and other serious bug [5, half dozen]. Tailpipe emissions from vehicles must be controlled to enable sustainable development [7,8,9,10,xi]. Efforts accept been made in countries across the world to accost the problems of large energy consumption in the transportation sector [12,xiii,14], including limiting transportation activity, restricting the purchase of automobiles, enacting strict emission regulations, improving vehicle fuel efficiency, and developing new energy sources for vehicles [15, xvi]. Among the suggested solutions, the development of new energy sources is considered one of the most promising and applied methods [17,eighteen,nineteen]. Researchers and automotive manufactures have therefore committed a not bad effort to progress in this field.

A hybrid electric vehicle (HEV) is a complex system that integrates electronic, mechanical, chemical, and thermodynamic technologies. When a HEV is in motion, power and information are being transferred and transformed by various flows. The potential for HEVs to salvage energy and reduce emissions is adamant by the means by which components are connected mechanically and the management of energy transfers between the internal combustion engine (ICE) and electrical motor. The HEV onboard generator can accuse the battery and recover free energy by regenerative braking, assuasive the battery to sustain an advantageous state of charge (SOC). A HEV that uses a smaller, more efficient ICE can achieve meliorate fuel economy. The ICE can also operate within its most efficient operating surface area nigh of the fourth dimension and tin be switched off when necessary.

Past combining the advantages of ICEs and electric vehicles (EVs), HEVs are quite promising for vehicle technology development in the brusque to mid term [20]. Lave and MacLean [21] compared a hybrid car to an Ice machine and plant that HEVs are not but effective for improving fuel economy and reducing emissions, but also, with gasoline price rise in the time to come, significantly reducing vehicle operating costs. Nordelof et al. [22] investigated the usefulness of different types of life-cycle assessments of HEVs and EVs to provide an overview of the environmental impacts. Hannan et al. [23] made a comprehensive review in the field of HEVs, terminal that existing technologies are capable of enabling good HEV performance, simply that the reliability and "intelligence" of HEV systems are all the same somewhat inadequate. Cummings et al. [24] investigated the outcome of sensitivity for sensing and prediction in vehicle fuel economy improvements. Morais et al. [25] presented distributed energy resources management using plug-in HEVs equally a fuel-shifting demand response resource, which scheduled the EV's charging and discharging processes to fugitive network congestion. Chen et al. [26] investigated the influence of electrification on transportation and road systems. Lave and MacLean [21] fabricated an ecology-economic evaluation between the HEV Toyota Prius and its Water ice vehicle (ICEV) edition, the Corolla, pointing out that with fuel prices ascension, the HEV would have significantly more marketplace share in the hereafter.

Different types of HEVs are classified by their degree of hybridization or their mechanical configuration. The main challenge for HEVs is splitting the power in an optimal way while delivering the desired performance under arrangement constraints [27, 28]. Specifically, an free energy management system is needed to select or combine the power sources for driving the vehicle [29,thirty,31]. Many unlike mechanical designs and energy management strategies have previously been presented by researchers using simulations and road tests [32,33,34,35]. Researchers accept made nifty progress in regard to HEV general configurations, power electronic components [32, 33], and free energy management strategies [34, 35]. The technology has developed chop-chop and seen many breakthroughs. Morteza and Mehdi [36] developed a new energy direction strategy for power splitting in HEVs using a multi-input fuzzy logic controller to further improve their fuel economy, tailpipe emissions, and performance in various driving cycles. Finesso et al. [37] applied an equivalent consumption minimization strategy to identify the optimal command strategy for a parallel HEV (P2 HEV). Li et al. [38] introduced a downshift strategy, and their hardware-in-the-loop simulation showed that this strategy tin improve the energy conservation of HEV regenerative braking by 10%–32%. Hannan et al. [39] studied a control organization for a multi-energy source HEV. The control algorithm was adult to fulfill diverse driving conditions, and the simulation model [40] was built under the ECE-47 driving cycle. Their results showed that their multi-source control strategy could exist efficient and conserve energy.

However, virtually inquiry on the topic of HEVs is based on an architecture that uses traditional transmissions. Because the transmission system in a HEV should have functions unlike from those in an ICEV, its development direction should be different from that for ICEVs. In this context, this newspaper discusses the future management of HEV transmission development.

Modeling of Hybrid Electric Vehicles

Base Parameters

In this study, several configurations of base of operations ICEVs and P2 HEVs were modeled. The fuel consumption (FC) of these models was used as a reference baseline. To obtain the FC values, the energy consumption was calculated offset with a vehicle dynamics formula. The formula includes rolling resistance, gradient resistance, aero resistance, acceleration resistance, and inertial resistance to model vehicle-specific running weather condition and obtain vehicle-specific energy consumption. The vehicle dynamics formula is as follows:

$$ F = mgf + \frac{{C_{\text{d}} Au^{ii} }}{21.15} + m\frac{{{\text{d}}u}}{{{\text{d}}t}} + \frac{{I_{\text{r}} + I_{\text{e}} i_{\text{T}}^{2} i_{0}^{2} \eta }}{{r^{two} }}\frac{{{\text{d}}u}}{{{\text{d}}t}}, $$

(ane)

where F is the longitudinal force to drive the vehicle, 1000 is the curb weight, g is the gravitational acceleration, f is the rolling resistance coefficient, C _d is the elevate coefficient, A is the front confront surface area, u is the running speed, I _r is the rotary inertia of the wheels, I _{due east} is the rotary inertia of the engine, r is the bicycle radius, i _T is the transmission gear ratio, i ₀ is the gear ratio of the last drive, and η is the mechanical efficiency of the power organisation.

The New European Driving Cycle (NEDC) was applied to the adding. The NEDC was established by the Eu and is applied in China. In the NEDC cycle, the vehicle is operated according to a given velocity curve. The total cycle lasts for 1200 s and has a top speed of 120 km/h. With vehicle velocity every bit input information, the strength and power needed in a NEDC cycle for a typical car are obtained. The total energy consumption in a NEDC cycle is obtained by integration of the ability data.

A typical mid-sized sedan was used in this report for the calculation. Key parameters of the vehicle are listed in Tabular array 1. The sedan was equipped with a five-speed transmission having the gear ratios shown in the table.

Table ane Key parameters of the vehicle

Total size table

A typical Water ice was used in the modeling, and its efficiency map is shown in Fig. ane. The engine has its highest efficiency of 40% at 2100 rpm and 100 N k. The FC rate of the engine at the optimal brake-specific fuel consumption was too obtained for use in the FC calculation.

Fig. one

Engine efficiency map

Full size image

Rule-Based Energy Management

The existing energy direction strategies for HEVs can be mainly classified into rule-based and optimization-based management. Both types accept been extensively studied. The researches take covered various aspects of these strategies, including the state of the art of energy management strategy, general formalization of the energy management problem, and characteristics and control furnishings of dissimilar strategies [32,33,34,35,36,37,38,39]. In an industrial application, rule-based strategies are more widespread. Although they cannot obtain an optimum solution, they are easy to implement considering they have fixed rules. Thus, rule-based strategies have been successfully used in commercial HEVs, and they provided the baseline for this study.

The logic menstruum of the dominion-based strategy for P2 HEVs used in this study is as follows. Get-go, the demand power P, demand torque T _r, and acceleration a _cc are obtained every bit base demands. 2nd, the torques of best efficiency, motor working surface area, and maximum torque of the engine at a certain speed are determined. Third, the charging or discharging piece of work manner is determined using the battery SOC. Finally, the demand engine and motor torque and battery charge or discharge land are obtained.

Fuel Consumption Baselines

With the necessary data and free energy management strategy ready, the baseline ICEV and HEV models could be created. The NEDC status was practical to calculate the FC, every bit implemented past the FC testing and evaluation provisions of the China National Standard.

The FC values of the baseline models are given in Tabular array 2, including an ICEV without a manual, ICEV with a five-speed transmission, ICEV with a continuously variable transmission (CVT), typical P2 HEV with dominion-based energy management, and serial HEV. The NEDC includes idle conditions for ICEVs, and the idle FC was set up as 1.5 L/h, which is a typical value for an ICE in a sedan. For the HEVs, the engine close downwardly when the demand vehicle velocity was 0 and thus did non consume fuel when at a standstill.

Table two FC values of baseline models

Full size tabular array

The engine working points distribution for each model is shown in Fig. 2. The working points distribution can explain differences in FC values for the baseline models. For example, note that in Fig. 2a, some points are outside the engine full-load curve because this model has no manual to regulate the speed and torque.

Fig. two

Working points of baseline models. a ICEV without transmission. b ICEV with five-speed transmission. c ICEV with CVT. d P2 HEV without transmission. e P2 HEV with transmission

Total size epitome

Direct Drive Area (DDA) Energy Direction

Improving Total Efficiency

A HEV is driven by an Ice in combination with ane or more than electric motors. A battery parcel is connected to the motors as a secondary system to provide power to them. Reducing the FC of a HEV is equivalent to improving the efficiency of the HEV powertrain system, peculiarly the working efficiency of the Water ice. Every bit the high-efficiency area of an engine efficiency map is rather minor, engine speed/torque regulation is necessary to keep information technology working in this area.

Notwithstanding, regulating the engine working points to remain only at the best efficiency point is not the all-time solution. This regulation is usually done past motor torque adjustment. When the engine working point is regulated to the best efficiency point or line, the free energy loss of motor and bombardment efficiency is inevitable. If the torque demand is less than the engine'south best torque at a sure speed, the generator works and the energy is stored in the battery. Otherwise, if the torque demand is more than the engine's best torque, the motor works to provide the torque needed. Neither approach will cause energy loss in the motor drive or battery.

Thus, when the ICE drives the wheels directly, at that place is an surface area near the engine's all-time efficiency point in which the total ICE efficiency is college than that obtained by the best efficiency regulation. We call this area the "straight drive surface area" (DDA), and it is determined by the efficiencies of the motor and battery charging/discharging cycle. If the motor and battery efficiencies are higher, the DDA will exist larger; otherwise, it is smaller. The DDA of the engine used in this study is shown in Fig. 3. The outline of the DDA is obtained by multiplying the best efficiency value by the average motor efficiency and bombardment efficiency. And so, the DDA tin be drawn as an engine efficiency map, as shown in Fig. iii.

Fig. 3

Direct driving expanse

Total size image

In the DDA strategy, the engine will directly drive the wheels in the DDA, rather than being regulated to the best efficiency bespeak. In this mode, energy loss in the motor and battery is avoided. Actually, similar results have been obtained using a dynamic programming (DP) method [27]. The working points regulated by the DP method are usually distributed near the DDA, and the results testify good FC reduction. But the DP method can only exist used when the whole working condition is already known, and the adding speed is rather boring. Thus, it is not used in industrial applications. While the DDA method is a rule-based strategy, the DDA is adamant in accelerate. During the driving wheel, the working bespeak of the torque and speed is determined. If it is within the DDA, the engine directly drives the wheels; otherwise, the working betoken is regulated to the all-time efficiency point, with the motor profitable the ability output.

Application of DDA Method

The DDA method can be realized in several means. Information technology can be applied in a typical P2 HEV or a series–parallel (SP) HEV. The compages of the SP HEV, which was developed by Honda Co., is shown in Fig. 4. When the engine working condition is in the high-efficiency expanse, the clutch is locked so that engine power directly drives the wheels. Otherwise, under low-speed conditions for instance, the engine efficiency is rather low and the clutch is released to drive the wheels only with the motor. The HEV organisation works as a series HEV in this case.

Fig. 4

Compages of SP HEV

Full size paradigm

To demonstrate that the SP HEV is a adept application of the DDA, we built a model. Figure five shows the engine working point distribution of the SP HEV model without a transmission. The red square shows the engine DDA, outside of which the working points are regulated to the best efficiency point (as a series HEV). Annotation that only few direct driving points are available in the NEDC because its driving load is more often than not rather low. In the SP case, FC is four.12 L/100 km, which compares with 4.13 L/100 km in the series HEV instance. The slight improvement comes from the fewer direct driving points. When a high-load or high-speed working condition is considered, a improve FC reduction may be achieved. Although the working point distribution in Fig. 5 is obtained past investigating a typical SP HEV model, information technology withal provides the common features of an SP system.

Fig. 5

Working point distribution of SP HEV. a Working points of SP HEV in DDA. b Working point regulation

Total size paradigm

DDA can besides be practical to P2 HEVs. When a rule-based strategy is applied to P2 HEVs, all engine working points are regulated to the best efficiency, including those in the DDA. In this study, the control strategy was changed using the rule-based strategy. For those points inside DDA, the Ice drives the bicycle directly, as described above. The engine working points distribution when the DDA method is applied to a P2 HEV with a five-speed transmission is shown in Fig. 6b, comparing with the working points of P2 HEV without applying DDA in Fig. 6a. As can exist seen, some of the points in Fig. 6b are away from the best efficiency line, when the ICE directly drives the wheels. Other points that are regulated to the best efficiency line in Fig. 6a are the points outside the DDA area, which tin exist found in Fig. 2b. In general, the FC of a P2 HEV controlled with the DDA method is 4.09 Fifty/100 km, compared with the 4.29 50/100 km of a dominion-based P2 HEV and the 6.48 L/100 km of a pure ICEV with a manual. Note that the FC value is fifty-fifty lower than that for the SP HEV because the transmission regulates some working points into the DDA. If nosotros add a manual to the SP HEV to regulate the working points, the FC value will be lower than that for the P2 HEV.

Fig. vi

Working point distribution of P2 HEV. a Working points of DDA method. b Working points of rule-based strategy

Full size paradigm

HEV Transmission Improvement Tendency

Functions of HEV Transmission

Awarding of the DDA method changes the function of the transmission in a HEV. In an ICEV, the office of the transmission is to regulate the working bespeak every bit close as possible to the best efficiency line of the engine at a given power. This ways that more gears in a transmission result in improve regulation. But more gears mean higher costs. Thus, about commercial transmissions take five or six gears. In theory, the CVT could accomplish an infinite number of gears to realize the best efficiency point nether any condition. Withal, the manual efficiency of CVTs is rather low due to the application of a torque converter. Transmissions in ICEVs have already been thoroughly addressed along with the development of vehicles.

When an electric motor is introduced into vehicles, the function of the transmission changes, specially when applying the DDA method. In HEVs using the DDA method, the FC volition be significantly reduced if in that location are more points in the DDA. The manual could regulate engine working points to realize this. The function of manual is to regulate a broad speed range (1500–3000 rpm, e.yard.) to a rather narrow DDA surface area speed interval (500–600 rpm in this written report). To illustrate the new function of a transmission in a HEV controlled with the DDA method, a typical five-speed manual was added to the SP HEV model. The engine working point distribution is shown in Fig. vii, along with that of the baseline ICEV. It tin can be seen that more points are within the DDA, where the wheels are direct driven. For other points, they are regulated to the engine'southward best efficiency signal, as in a serial HEV. With more direct driving points, the FC value is 4.06 50/100 km, compared with 4.12 L/100 km in a SP HEV without speed regulation.

Fig. 7

Working point distribution of SP HEV with five-speed transmission. a SP HEV with manual. b Baseline ICEV with manual

Full size prototype

Every bit the engine working points regulated by the manual follow a hyperbola, different operating profiles are revealed at higher and lower speeds. When regulating the higher-speed working points to lower speed, the torque will increase correspondingly to ensure the same ability output. Considering most loftier-speed weather are associated with lower torque demands, points in this area could be regulated into the DDA with a high probability of amend efficiency.

Nevertheless, when regulating a lower-speed betoken to high speed, the torque decreases. If the original torque is low, then the regulated torque decreases and might autumn out of DDA. In this case, the hybrid mode volition be engaged. If the torque is as well low, the electric mode becomes active and the vehicle is driven only by the motor. Otherwise, the hybrid mode is active, and the driving point is forced to the best torque/speed ratio that drives the wheels while charging the battery. Meanwhile, the battery SOC is besides considered to decide which style to select. In either case, the objective is to make the total efficiency higher than the original (unregulated) efficiency signal. For example, some driving points at lower speeds have higher torque. In this example, these points could probably exist regulated into the DDA to improve total efficiency.

The above considerations indicate that the future HEV transmission development trends might exist as follows. First, as the regulation target is an interval rather than a bespeak, more gears are not necessary for HEVs. 2nd, to regulate a wide speed range into a narrow speed interval, transmission ratios must be redesigned.

Transmission Comeback

Verification models were congenital to support evaluation of the two major directions of HEV manual development. The SP HEV system could be considered equally an electric CVT between the engine and wheels. This is because all the points exterior the DDA can exist regulated to the best efficiency point (2100 rpm/100 N m), merely like a continuous speed change. In contrast, in the P2 HEV organization, the transmission changes speed in steps, and speed intervals can exist accomplished only by regulating the efficiency points. From the perspective of cost, it is suggested that a step-irresolute transmission is preferable when applying the DDA method. Recall that the FC value of an SP HEV with transmission is iv.06 L/100 km, and that of a P2 HEV with transmission is 4.09 L/100 km. The slight modify comes from the regulated points that are outside DDA. In the SP HEV arrangement, these points are regulated to the best (40%) efficiency bespeak. In the P2 HEV system, the points are along the line nearest the all-time point, with a slightly lower average engine efficiency. Note that the P2 HEV organization is constructed with fewer components, that is, engine, motor, and manual. The SP HEV system with transmission has these components plus an additional generator. Information technology should exist noted that the commercial SP HEV cars of Honda have no transmission, as shown in Fig. 4. As the P2 HEV has a meliorate cost operation, we farther studied transmission applications based on P2 HEV systems.

A P2 HEV model with a three-speed transmission was built. The three-speed manual was obtained past using the outset, 3rd, and fifth gears of the original v-speed transmission. The working betoken distribution is shown in Fig. 8 and compared with the v-speed manual. The FC value is 4.09 L/100 km, which is equal to that of the five-speed version. In Fig. 8, it can be seen that the ii models share similar bespeak distributions because they share three gear sets. More than points in the three-speed version are outside the DDA, and they are regulated forth the best efficiency line. But the full number of these points is too small-scale to influence the final FC effect. Even for the points outside the DDA, the engine efficiency is still more than 30%. Furthermore, the ratios of gears may not be optimal, and the gear shift strategy could also be improved. Thus, the FC can yet exist farther reduced. If the command strategy's potential is fully developed, the FC might come across even stricter FC regulations.

Fig. 8

Effect of reducing the number of gears in an HEV manual. a 3-speed transmission driving points. b Five-speed transmission driving points

Full size image

The results presented above back up the utility of the HEV transmission evolution trends that we have proposed. With fewer gears just optimal ratios, the cost of HEV transmissions could be reduced and help increase HEV penetration in the vehicle marketplace.

Conclusions

This paper described how the hereafter function of HEV transmissions will be different, which volition regulate engine speed into a sure speed interval, rather than to the best efficiency betoken for a given power. Development trends of HEV transmissions based on this new function were predicted, that is, that fewer gears will be used in HEV transmissions, but the gear ratios volition be optimized. This decision is based on several HEV models, an energy management strategy considering total efficiency, and an engine working betoken distribution assay. The presented DDA method made the engine drive the wheels directly in the loftier-efficiency area. In contrast, the rule-based strategies forced the engine to work along the best efficiency line in the efficiency map. The DDA method was verified by applying it to different HEV models. With fewer transmission gears, the total cost of an HEV will be reduced, which is beneficial for increasing marketplace share.

Abbreviations

DDA:

Direct bulldoze expanse

DP:

Dynamic programming

CVT:

Continuously variable transmission

EV:

Electric vehicle

FC:

Fuel consumption

HEV:

Hybrid electrical vehicle

Water ice:

Internal combustion engine

ICEV:

Internal combustion engine vehicle

NEDC:

New European driving cycle

P2 HEV:

Parallel HEV

SOC:

Country of charge

SP HEV:

Serial–parallel HEV

A :

Front confront surface area

C _d :

Drag coefficient

f :

Rolling resistance coefficient

I _r :

Rotary inertia of bike

I _e :

Rotary inertia of engine

i _T :

Manual gear ratio

i ₀ :

Gear ratio of final drive

chiliad :

Curb mass

R :

Wheel radius

u :

Vehicle running speed

η :

Mechanical efficiency of power organisation

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Acknowledgements

This report is sponsored by the National Natural Science Foundation of Prc (71403142, 71774100, 71690241), Young Elite Scientists Sponsorship Program of the Chinese Association for Scientific discipline and Engineering science (YESS20160140), and Beijing Natural Science Foundation (9162008).

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Affiliations

1. Tsinghua Automotive Strategy Research Found, Tsinghua University, Beijing, 100084, People's republic of china

   Tianze Shi, Fuquan Zhao, Han Hao & Zongwei Liu

2. Country Key Laboratory of Automotive Condom and Free energy, Tsinghua University, Beijing, 100084, China

   Tianze Shi, Fuquan Zhao, Han Hao & Zongwei Liu

Corresponding author

Correspondence to Zongwei Liu.

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Shi, T., Zhao, F., Hao, H. et al. Evolution Trends of Transmissions for Hybrid Electric Vehicles Using an Optimized Energy Management Strategy. Automot. Innov. ane, 291–299 (2018). https://doi.org/ten.1007/s42154-018-0037-5

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• Received: 19 August 2018

• Accepted: 03 November 2018

• Published: 28 November 2018

• Event Date: November 2018

• DOI : https://doi.org/10.1007/s42154-018-0037-5

Keywords

• HEV
• Direct drive area
• Energy management
• Transmission cost reduction

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