Abstract
With the trend of low emissions and sustainable development, the demand for hybrid electric vehicles (HEVs) has
increased rapidly. By combining a conventional internal combustion engine with one or more electric motors powered
by a battery, HEVs have the advantages over traditional vehicles in better fuel economy and lower tailpipe emissions.
Nevertheless, the power management strategies (PMSs) for conventional vehicles which mainly focus on the
efficiency of internal combustion engine are no longer applicable due to the complex internal structure of HEVs.
Hence, a large number of novel strategies appropriate for HEVs have been surveyed, but most of the researches
concentrate on discussing the classifications of PMSs and comparing their cons and pros. This paper presents a
comprehensive review of power management strategies adopted in HEVs aiming at specific challenges for the first
time. The categories of the existing PMSs are presented based on the different algorithms, followed by a brief study of
each type including the analysis of its pros and cons. Afterwards, the implementation and optimization of power
management strategies aiming at proposed challenges are introduced in detail with the description of their
optimization objectives and optimized results. Finally, future directions and open issues of PMSs in HEVs are
discussed.
1. Introduction
As the world's population and economy grow at an unprecedented pace, the number of private cars on the road is
rapidly increasing, putting a strain on energy distribution and environmental protection. [1]. To be more specific,
More specifically, conventional cars are mainly powered by fossil fuels (diesel and gasoline), both of which
are limited and nonrenewable resources [2]. Furthermore, poisonous gases emitted by vehicles not only pollute the air
and endanger human health, but they also exacerbate the greenhouse effect, increasing the risk of global climate
disasters (e.g., hurricane, tsunami, and rise of sea level).
One of the most innovative solutions to the problems listed above is hybrid electric vehicles (HEVs). Unlike
traditional gasoline-powered vehicles, which have a single power structure, HEVs have a power supply system that
includes a generator, an internal combustion engine, and a converter [3]. The HEV's energy sources are enriched by
such an internal structure, allowing it to run on both electricity and heat, reducing fossil fuel consumption and
emissions. Power management strategies for conventional vehicles, which primarily focus on the efficiency of internal
combustion engines, are no longer applicable due to the complex internal structure of HEVs. New strategies are
needed not only to optimize the internal combustion engine, but also to consider the battery's power, the flow and
distribution of energy, and the collaboration of internal components (such as the generator and the internal combustion
engine) [4]. The complexity is greatly increased when multiple management objectives are pursued. The following are
the four major issues in HEV power management:
(1) Real time optimization, as compared to static optimization, it allows the power management strategy to be
adjusted based on driving conditions, significantly improving the strategy's timeliness [5]. However, existing
technologies (e.g., GPS and BIMS) make it impossible to accurately predict, analyze, and assess future driving
conditions, such as road conditions, traffic flow, and the surrounding environment. As a result, adapting real-time
optimization to changing driving conditions is difficult [6].
(2)Battery durability is significantly reduced under dynamic loading conditions when compared to constant load
conditions [7]. Furthermore, frequent charging and discharging process, switching voltages, and the flow of energy
tend to accelerate battery aging.
(3) Computation load with the expansion of optimal objectives, the computation load generated by some power
management strategies, such as dynamic programming (DP), is likely to increase significantly [8]. Furthermore,
because current vehicular networks have limited computation capabilities, they may struggle to process such large
amounts of data in real time. As a result, the majority of these strategies are restricted to theoretical analysis rather
than actual implementation [9].
(4)Multiple energy sources unlike traditional fuel-powered vehicles, HEVs with multiple power sources have a variety
of internal energy flows and transitions. As a result, under a variety of driving conditions, internal component
cooperation (e.g., motor and internal combustion engine) and energy distribution become more complicated [10].
Although a large number of HEV power management strategies (PMSs) have been studied, the current research is
primarily focused on discussing PMS classifications and comparing their benefits and drawbacks [11, 12]. There
hasn't been a comprehensive and thorough review of power management strategies aimed at specific challenges
presented by academics. To close this gap, this paper conducts a comprehensive and detailed survey of recent research
performance of HEV and power management strategies in terms of the aforementioned challenges, providing explicit
research directions for future scholars [13].
The paper is organized as follows. Part II introduces the internal system framework of HEV, including power supply
system, the generation of energy, and the cooperation of each part. Part III presents an overview of power
management strategies of HEVs, providing their classifications and comparisons. After that, part IV reviews the
implementation and optimization of power management strategies according to specific challenges raised above.
Finally, part V discusses future trends and open issues of power management strategies in HEVs.
2. Classification of Hybrid Electric Vehicles
The classification of hybrid power systems differs depending on the standard used. This section summarizes and
analyzes the classification of HEVs based on various criteria such as the degree of hybridization, The position
of the motor and on The powertrain configurations
2.1. Based on Degree of Hybridization
In general, HEVs can be divided into four types according to the electrification level: micro, mild, and full, HEVs
as well as plug-in HEVs (PHEVs) [39]. Table 1 shows their main distinctions.



Micro HEV : A micro HEV is a vehicle powered by an electric motor that is connected to an integrated
alternator/starter that uses start/stop technology[40] which allows it to shut down the engine upon stopping,
and restart it when the brake pedal is released. At cruising speeds, the vehicle is only propelled by the ICE.
Examples of micro hybrids on the road today include the BMW 1 and 3 series, Fiat 500, SMART car, Peugeot
Citroen C3, Ford Focus and Transit, and Mercedes-Benz A-class.
Mild HEVs:Mild hybrid electric vehicles have many similarities to micro hybrid electric vehicles,
but they have a larger integrated
alternator/starter
motor
and
a
battery that provide power
assist to the vehicle during propulsion [41]. Typical gains in fuel efficiency for mild hybrid electric
vehicles are around 20% to 25% in real-world conditions. Examples of mild HEVs on the market include the
BMW 7 Series Active Hybrid, Buick La Crosse with e-Assist, Chevrolet Malibu with e-Assist, Honda Civic
and Insight Hybrid, and the Mercedes-Benz S400 Blue Hybrid[42,43].
Full HEVs have larger batteries and stronger electric motors than those in micro and mild hybrids, which are
often high in cost [9]. They enable the usage of the pure electric (EV) mode for a short duration in city
driving. PHEVs essentially have similar characteristics as those of full HEVs but include a larger battery
package that can be charged by plugging into the grid [44,45]. This allows PHEVs to be operated in EV mode
for extended periods. Two types of PHEVs are available in the market: extendedrange electric vehicles
(EREVs) and blended PEHVs. The former generally use series configuration, in which the gasoline engine
only generates electricity and the electric machine drives the vehicle; an example is the BMW i3 with a range
extender. The engine is not fired until the battery is depleted. In the latter, the engine is usually engaged to
directly power the vehicle. An electric machine acts as a motor/generator (MG) based on the driving demand
and battery SOC level, such as that installed in the Chevrolet Volt. Fig. 1 shows typical battery SOC ranges
and cycling ranges of all four types. The SOC range increases with the hybridization degree. Micro, mild, and
full HEVs usually have a narrower SOC window to avoid over-charging or over-discharging
2.2. Based on the Position of the Motor
At present, based on the position of the motor in the powertrain with single motor of HEV is mainly classified
into five categories named P0, P1, P2, P3, and P4 (Figure 1) [14,15]. In simple terms, the word “P” is the position
of the motor, which is placed in a different position with a different number code. In Figure 2, the two clutches
can separate and engage the engine and motor separately. When the torque is transmitted from the motor to the
engine to start the engine, it is transmitted by the K0 clutch (The letter "K" represents clutch).
Subtype P0 refers to the configuration in which a motor is installed before the ICE and is connected to ICE by a belt.
Therefore, it is also known as a belt-driven starter/generator HEV. Owing to the torque limitation of the belt, the
starter/generator is always small and can fulfill only the start–stop function [48].
The P1 subtype refers to the configuration in which the motor is mounted on the crankshaft of the engine. Here, the
motor is always referred to as an integrated starter generator (ISG) [49,50]. The installation position of the ISG always
restricts its size; this limitation does not allow ISG to provide high torque to operate the vehicle. Only some functions
can be fulfilled such as start–stop, regenerative braking and acceleration assistance.
The P4 subtype refers to a parallel hybrid in which the motor is mounted directly on the drive shaft or is incorporated
into the hub of a wheel using in-wheel motor technology (Fig. 3 (e)). P4 is generally not used independently but is
combined with other parallel subtypes, P2 and P3, particularly in four-wheel drive (4WD) vehicles [54].
Previous studies [55] and [56] have compared the performance of different parallel HEVs. A comparative study
through DP was also conducted [57] in which P2 is shown to have better fuel economy than P1 owing to its larger
motor, and P2 and P3 have similar fuel economy benefits
Fig. 3. Configurations of subtypes P0 to P4 in parallel HEVs
14. Bao, R.; Avila, V.; Baxter, J. Effect of 48V Mild Hybrid System Layout on Powertrain System Efficiency and Its Potential of Fuel Economy Improvement. In
Proceedings of the Wcx™ 17th Sae World Congress Experience, Detroit, MI, USA, 4–6 April 2017.
15. Zhou, W.; Yang, L.; Cai, Y.; Ying, T. Dynamic programming for New Energy Vehicles based on their work modes part I: Electric Vehicles and Hybrid
Electric Vehicles. J. Power Sources 2018, 406, 151–166. [CrossRef]
2.3 Based on the powertrain configurations
The main challenge in HEV design is to control energy transfer from sources to loads with minimal energy
loss, which is dependent on driving cycles. In comparison to conventional internal combustion engine
vehicles (ICEVs), HEVs have more electrical apparatus such as electric machines, power electronics,
electronic continuously variable transmissions, embedded powertrain controllers, advanced energy storage
devices, and energy converters [17]. There are three types of HEV powertrain configurations: series,
parallel, and series-parallel combination HEVs [10].
[10] Lo EWC. Review on the configurations of hybrid electric vehicles. In: 3rd international conference power electronics systems and
applications; 2009. p. 1–4.
[17] Katrašnik Tomazˇ. Analytical framework for analyzing the energy conversion efficiency of different hybrid electric vehicle topologies.
Energy Convers Manage 2009;50(8):1924–38.
2.1. Series Hybrid Electric Vehicles
A series hybrid is like a battery electric vehicle (BEV) in design. In this case, the combustion engine does not provide
propulsive torque to drive the vehicle. Its main function is to convert potential energy from fuel to mechanical
energy which is then converted to electrical energy using a generator. The electrical energy is used to propel the
motor via an inverter. As a result of this configuration, the engine speed can be independently controlled from the
vehicle speed, so that the engine can run at the optimal speed to minimize losses incurred in the electricity generation
process [26].
A series hybrid electric vehicle's power system is made up of multiple batteries, a decoupled-from-wheel
engine, an electric generator, and a motor [16]. Figure 1 show that the structure as well as the energy flow.
Rather than the engine, the battery pack provides the primary driving power for the wheels. The engines of
SHEVs are also used to power electric generators, which charge the battery pack. The motor will then be
driven by the power released by the battery pack to provide the necessary energy and torque for the wheels
[17].
Figure 1. Series hybrid electric vehicle.
2.1.2. Operation mechanism
Typical series HEVs have traction motors that operate the vehicle alone, whereas an ICE is connected to a generator
(Figure 1). The motors are powered by the battery and the generator, and they can be placed
on either the front or rear axle to achieve electric all-wheel drive. Since there no mechanical coupling exists between
the ICE and vehicle drive axle, the ICE could operate in its best efficiency area regardless of the vehicle speed and
power required by the driver. Moreover, the traction motor has a wider operating range and higher efficiency than the
ICE.
Series HEVs generally use traction motors to operate the vehicle alone, whereas the ICE is connected to a generator
(Fig. 4). The motors are powered by the battery and the generator and can be placed on both front and rear axles to
realize electric all-wheel-drive functionality. Since there no mechanical coupling exists between the ICE and vehicle
drive axle, the ICE could operate in its best efficiency area regardless of the vehicle speed and power required by the
driver. Moreover, the traction motor has a wider operating range and higher efficiency than the ICE. Therefore, a
transmission, which is a necessary component in a conventional vehicle, might not be necessary in series HEVs. Thus,
the series hybrid powertrain is simpler compared with other types, including configuration and energy management
[58].
2.1.2. Typical models
Only a few HEVs in the market use the series configuration expect range-extended HEVs [59]. The most successful
model of this type in the market is the BMW i3, which provides an optional gasoline-powered range extender
auxiliary power unit [60]. Recently, some OEMs have developed electric cars with range extended technique. A
typical example is the Nissan e-Power, which has a 1.2 L gasoline engine that acts solely as a generator for battery
charging [61].
2.1.3. Limitations and challenges
The fuel economy of series HEVs can be better than that of conventional vehicles. However, high energy conversion
losses can occur because 100% of the engine power must first be converted into electricity. Part of the electricity is
stored in the battery, and the remainder powers the motors to propel the vehicle. Even though the MGs have
relatively high efficiency and the ICE operates at high efficiency, the multiple energy conversions still result in low
overall efficiency. Additionally, the series configuration requires a large traction motor to meet the torque requirement
because the motor is the only traction device.
As for the parallel topology, the engine is connected to the powertrain by a mechanical coupling device while the
motor propels the vehicle. Either engine or motor could propel the vehicle according to different load conditions,
which makes it possible to greatly increase the fuel economy. The motor provides the power when the vehicle operates
at lower speed to reduce fuel consumption. Thus, this configuration is capable of maintaining a higher efficiency and
better fuel economy. The power summation is mechanical rather than electrical, and the engine and the electric
machines (one or more) are connected with a gear set, a chain, or a belt; thus, their torques are summed and
transmitted to the wheels [8]. In this configuration, there is no need to size one of the two electromechanical energy
converters to meet the maximum power demand for parallel hybrid powertrain; however, unless it is significant
oversize, the electric motors have less power than those used in a series hybrid powertrain (since not all the
mechanical power goes through them), thus reducing the possibility of regenerative braking. Meanwhile, the engine
operating conditions cannot be regulated as freely as in a series hybrid powertrain, since the engine speed is
mechanically related (via the transmission system) to the vehicular velocity [2,8]. The parallel topology is illustrated
in Figure 2.
2.2. Parallel hybrid electric vehicles (PHEV)
In PHEV, both the mechanical power output and the electrical power output are connected in parallel to drive the
transmission as shown in Fig. 3. There are various control strategies used for parallel configuration. In the most
common strategy, ICE is basically always in on mode and operates at almost constant power output at maximum
efficiency point.
If the power requested from transmission is higher than the output power of ICE, the electric motor is turned on, ICE
and electric motor supply power to the transmission. If the power requested from transmission is less than the output
power of the ICE, the remaining power is used for charging the battery packs. In this configuration, regenerative
braking power on a down slope driving is used to charge the battery. Insight model introduced by Honda is a specific
implementation of parallel HEV (PHEV). Other examples for PHEV applications are Ford Escape Hybrid SUV and
Lexus Hybrid SUV.
Figure 2. Parallel hybrid electric vehicle.
3.1.1. Operation mechanism
In parallel HEVs, both the ICE and MG are connected mechanically with the output shaft (Fig. 2) and can
simultaneously provide power to operate the vehicle. The available MG is used to shift the engine operating points to
a higher-efficiency area. It acts as a generator at low power demand and as a motor at high power demand. In this way,
the engine can work at higher efficiency than that in a conventional vehicle. In addition, parallel hybrids must include
a transmission to match high engine speed and low vehicle speed [46].
3.1.2. Sub-types and typical models
The parallel configuration can be considered as an incremental add on to a traditional powertrain and its design
requires relatively little investment and engineering effort. Parallel hybrid powertrains can be further classified into
five subtypes according to the location and size of the MG: P0, P1, P2, P3, and P4 (Fig. 3) [47].
Subtype P0 refers to the configuration in which a motor is installed before the ICE and is connected to ICE by a belt.
Therefore, it is also known as a belt-driven starter/generator HEV. Owing to the torque limitation of the belt, the
starter/generator is always small and can fulfill only the start–stop function [48].
The P1 subtype refers to the configuration in which the motor is mounted on the crankshaft of the engine. Here, the
motor is always referred to as an integrated starter generator (ISG) [49,50]. The installation position of the ISG always
restricts its size; this limitation does not allow ISG to provide high torque to operate the vehicle. Only some functions
can be fulfilled such as start–stop, regenerative braking and acceleration assistance.
Subtypes P2 and P3 are the two most popular variations of parallel HEVs. In these configurations, the motor is
mounted on the input and output of the transmission, respectively. The motor in P2 and P3 is much larger than that in
P0 and P1 and has the ability to operate the vehicle at relatively high speeds [46]. Recapturing more regenerative
braking and eliminating engine drag result in better energy efficiency than that in other subtypes.Many European and
Korean automakers have released P2-type HEVs such as the Volkswagen Passat hybrid [51] and the Hyundai Sonata
Hybrid [52]. In China, BYD used the P3 subtype in the BYD Qin [53].
The P4 subtype refers to a parallel hybrid in which the motor is mounted directly on the drive shaft or is incorporated
into the hub of a wheel using in-wheel motor technology (Fig. 3 (e)). P4 is generally not used independently but is
combined with other parallel subtypes, P2 and P3, particularly in four-wheel drive (4WD) vehicles [54].
Previous studies [55] and [56] have compared the performance of different parallel HEVs. A comparative study
through DP was also conducted [57] in which P2 is shown to have better fuel economy than P1 owing to its larger
motor, and P2 and P3 have similar fuel economy benefits.
3.1.3. Limitations and challenges
Parallel HEVs are efficient during city stop-and-go conditions. However, this might not be the most efficient
configuration because a mechanical connection still exists between the ICE and the output shaft. In addition, because
MG cannot be used to simultaneously charge battery and assist in powering the engine, the power assist and EV
operations must be controlled carefully to avoid battery depletion. This problem is exacerbated during city driving, in
which frequent start–- stops can consume a significant amount of battery energy and force the engine to generate
power in its low efficiency area. Because of these drawbacks, parallel HEVs have a smaller market share percentage
even a variety of models have become available.
2.3. Combination of parallel and series HEVs
Combination of parallel and series hybrid configurations into a single package is quickly becoming the standard in
passenger vehicle hybridization [27]. As the name implies the combination hybrid configuration is neither fully
parallel nor series configuration. Fig. 4 shows the essential of the combination architecture.
The combination architecture has features of both series and parallel hybrid configurations. Parallel and series
HEVs are mostly used in small automobiles, such as passenger cars [13]. They conjugate with power split devices
(planetary gear set) allowing for power path from ICE to wheels. A planetary gearbox connects two electric machines
and the ICE as shown in Fig. 5. Owing to the connection of the sun gear and the planet gears, the speed of
the engine can simply be adjusted by varying the speed of the generator [28].
Figure 3. Power-split hybrid electric vehicle
3.3.1. Operation mechanism
Power-split HEVs usually employ one or multiple planetary gear (PG) sets to couple the ICE, two MGs and the
driveshaft together (Fig. 5) [62,63]. The PG sets are the heart of the power-split hybrid powertrain, which is usually
referred to as a power split device (PSD). The PSD decouples the ICE from the vehicle speed and acts as a
continuously variable transmission (CVT), which results in efficient engine operation regardless of the vehicle speed.
Therefore, the PSD in power-split HEVs is also referred to as an electronic-CVT (E-CVT) [64]. Because of this
decoupling function, power-split HEVs generally show better fuel economy than both series and parallel HEVs,
particularly in city driving conditions. The PSD allows for power flows from the engine to the driveshaft: either
through the mechanical path or the electrical path (Fig. 6) [64,65]. In electrical path, the PSD operates like a series
HEV. Part of the ICE power is converted into electricity first by a generator, which drives the motor or charges the
battery. In the mechanical path, the PSD also enables the system to operate like a parallel HEV, in which the ICE
engine can generate power flow to the driveshaft directly. Therefore, the power-split HEV combines the advantages of
both series and parallel hybrids.
3.3.2. Sub-types and typical models
The power-split hybrid powertrain can be further classified into three subtypes according to the point of the power
split execution: i.e., input-split, output-split and compound-split [64]. In an input-split HEV, the ICE power is split at
the input to the transmission by collocating an MG with the output shaft and sometimes with an additional set of gears
in between. In an output-split vehicle, the ICE power is split at the output to the transmission by collocating an MG
with the ICE, also sometimes with an additional set of gears in between. In a compound split vehicle, no MG
collocation occurs with the output shaft or the engine.
The most successful power-split hybrid on the market is the input split subtype, such as the Toyota Hybrid System
(THS) or Hybrid Synergy Drive (HSD), introduced by the Toyota Motor Corporation[66]. This subtype was first
implemented in the Toyota Prius in Japan in 1997 and was then extended to the company’s Camry and Lexus
hybrid vehicles in the following years. The second-generation THS was announced in 2004, offering increased system
efficiency, enhanced power, and improved scalability [67]. Scalability enables the THS to adapt to different vehicle
sizes by changing the reduction paths of ICE/ MG1 and MG2. A similar input-split concept was adopted by the Ford
Motor Company in its Fusion Hybrid and C-max models. However, General Motor developed two classes of powersplit powertrains by using the other two subtypes in a power-split configuration, the Voltec Hybrid Powertrain with an
output-split mode [68] and the Allison Hybrid System (AHS) with both an input-split and a compound-split mode
[69].
Power-split HEVs have a variety of design variations by changing the locations of the employed components [70].
To explore all possible designs, Liu and Peng proposed an automated modeling method to efficiently build the
dynamics of a power-split HEV and identified a design with optimal fuel economy [71]. Barak et al. enumerated all
feasible powertrain by using a bond graph and generated complete sets of feasible designs based on an exhaustive
search [72]. Kim et al. reorganized all the possible single PG configurations into a compound lever design space and
screened the optimal design by balancing the fuel benefits and the drivability [73,74].
3.3.3. Limitations and challenges
As discussed in Section 3.3.1, the electrical path incurs higher energy loss than the mechanical path because of the
extra energy conversion. More ICE power delivered through the electrical path indicates more energy loss caused by
the PSD. When the speed of either MG is equal to zero, the engine–generator–motor path has zero power
transmission, and the energy transition is the most efficient. This condition is known as the mechanical point. The
energy dissipation in the electrical path might cause power-split HEVs to have greater energy losses than
those in parallel HEV in some situations, particularly in high-speed cruising [64].
3.4. Multi-mode hybrid powertrain
3.4.1. Operation mechanism
The multi-mode hybrid powertrain system can be developed by adding clutches to parallel or power-split powertrain
systems, which can become any of the three hybrid configurations (series, parallel and power split) in the same
powertrain. Its subtype is also referred to as the operating mode. The freedom to choose from different modes makes it
possible to achieve higher energy efficiency and performance than that realized by using the other HEV configuration
types introduced above.
Table 4. summarises the main advantages and disadvantages of the three HEV
powertrains in more detail.
Powertrain
Series
Advantages
Disadvantages
• Efficient operation of the ICE
• Many components
• Good performance at low speeds..
• Bad performance at high speeds
• Simpler control of the ICE
• Low efficiency
• Long operational life
• Multiple energy conversions
Application: Larger vehicles such as heavy-duty buses, trucks and locomotives
Parallel..
• No necessity of electric generator
• Control of the ICE is more complex
• ICE reduced size
Mixed.
• Great flexibility.
• Very complex architecture
• High efficiency..
involving many components
• Economic gain at high cost
• High voltages needed for efficiency
Application: urban passenger cars
Series –parallel
•
• Zero emission operation possible
• Very expensive system
Control complexity
• Very expensive system
• Larger traction drive system
• Optimised efficient traction driveline (engine downsizing)
• Modular power plant possibilities (space packaging advantages)
• Excellent transient response
• Zero emission operation possible
Application: Larger vehicles such as heavy-duty buses, trucks and locomotives.
• Zero emission operation possible
• Complex space packaging
• Zero emission operation possible
• Very expensive system
• Control complexity
• Complex space packaging
Application: passenger cars, light duty vehicles.
3. Power management strategy
The PMS mainly aims to divide the demanded power among different energy sources to sustain the battery SOC,
optimize the drivetrain efficiency, and decrease the fuel consumption and emissions or other concerned
objectives. In this section, the PMSs are divided into two classes (i.e. offline and online) in accordance with the
criterion of use in real-time implementation because the purpose of designing any PMS is to apply it in realworld applications or to be used as a benchmark to verify the effectiveness of other methods. 3.1. Offline PMS
Usually, this type of PMSs cannot be used in real-time application because it requires prior knowledge of global
information such as the whole drive cycle, and the algorithms take too much computational effort before finding
the solution. However, the main benefit of such methods is to verify the optimality of others. They generally
refer to the global optimization algorithms or stochastic search methods such as genetic algorithm (GA),
simulated annealing (SA), particle swarm optimization (PSO), parallel chaos optimization algorithm (PCOA), bee
algorithm (BA), etc. More information about their exclusive features can be found in reference [11].
4. The Implementation and Optimization of PMSS
Although the HEVs have made great progress in improving fuel economy, reducing emissions, and achieving better
vehicle performance, they still face significant challenges in power management. The challenges include real-time
optimization, battery durability, computational load, and the power allocation among multienergy sources. To this
endeavor, with the continuously advancing investigations, the novel power management strategies have been
proposed. In this section, the advanced power management strategies aiming to address these challenges are discussed.
4.1. The Real-Time Optimization
In the real-time optimization, the optimal solution is often obtained based on the forecast of future
conditions. It indicates that the future driving conditions, such as traffic conditions, road grade, and
surrounding environment, are prerequisites for the real-time optimization. Accordingly, the methods utilized
for the prediction of the future information are essential for the optimization of the power management
strategy in real time.
4.2. The Battery Durability
The HEV system requires high energy capacities for long driving distances and high power capacities for
accelerating, climbing, or braking. These requirements (high energy capacities and high power capacities)
keep the battery in frequent discharge-charge condition. Nevertheless, the battery durability is impaired by
the high discharge-charge rates, leading to a reduction in fuel economy. Hence, a proper power management
for HEV is required to fulfill the durability of the battery.
To deal with the battery durability, a system-level design is essential. Capasso et al. [51] developed an
optimal control strategy, which exploits the off-line solution of an isoperimetric problem and dynamically
optimizes the battery durability via reducing peak current. The results showed the effectiveness of the
strategy in reducing the high charging/discharging current peaks to increase the battery durability. In
addition, Zhang et al. [52] proposed a hysteresis control strategy for HEV with three fuel cell stacks, where
each fuel cell stack works at a fixed operating point, and its active time is shortened by on-off switching
control. Combined with the power capability and SOC states, Wang et al. [53] designed a finite state
machine-based management strategy and presented an optimal oxygen excess ratio control to maximize the
net power of fuel cell. The simulated results indicated that the method guarantees the required power during
the driving cycles. Additionally, by taking the battery durability into consideration, the power management
strategies can obtain the near-optimal solution. Zhang et al. [54]
proposed an optimal power management strategy based on the DP algorithm and verified by the different battery SOC
and battery state-of-health (SOH) conditions, which guarantees a better strategy control performance. To optimize the
control of the fuel cell system, Robin et al. [55] designed a mechanistic catalyst dissolution model to predict the
lifetime of fuel cell and utilized a model inversion to forecast the performance loss. The mechanistic catalyst
dissolution model successfully passed the battery durability test in dynamic operation conditions.
4.3. Computational Load
Due to the curse of dimensions, the data required to support a reliable result tends to multiply exponentially
with the increase in variables. A mass of data leads to a high computation load or even an inability to
calculate. Consequently, some power management strategies (e.g., dynamic programming) fail to be applied
for the power management of HEV without the optimization for computational complexity in practice.
Larsson et al. [56] put forward a method based on local approximation of the gridded cost-to-go and utilized
local approximations at the appropriate control signal to reduce the quantized interpolation. Combined with
the particle swarm optimization (PSO), Yang et al. [57] proposed a rapid-DP optimization strategy to select
the optimal control state of the motor and further improved fuel economy of the vehicle.
With the proposal of level-set DP in [58], the computing time of DP was decreased by 300 times.
Nevertheless, more challenges, e.g., the Markov and standardization problems, were raised. To deal with
these problems, Zhou et al. [59] proposed a unified solution method, where the Markov characteristics of DP
were utilized to construct a unified equation of state. A massive amount of data in the computing was
reduced by filters based on state variables and control variables. This method was faster than the
conventional DP, reducing computation time by 96.48% and 23.44% compared with basic DP and level-set
DP.
Due to the low computational complexity of neural network, Li et al. [60] presented a power management
strategy based on reinforcement learning without worrying curse of dimensionality in complex
environments, where stochastic gradient descent and experience replay were adopted to guarantee the
accuracy and stability of the method.
The strategies to address the computational load discussed are listed in Table 3.
4.4. The Power Allocation of Multiple Energy Sources
Compared with EVs, PHEVs have longer driving ranges. On the one hand, the engine allows the vehicle to
work when the battery SOC is at a low state, which is similar to the situation of conventional vehicles. On
the other hand, the engine will be turned off, and the vehicle will be driven by the electric power system
when the speed or the power demand is low. Therefore, the driving performance depends on the power
allocation among multienergy sources.
Many valuable works related to power management for HEVs with multienergy sources, where intelligent
strategies (e.g., fuzzy logic, dynamic programming, and particle swarm optimization [48]) are utilized to
optimize energy allocation among multiple sources, have been widely conducted. Nevertheless, most of the
works investigate power management strategies for the HEVs powered by the battery and engine or the
battery and ultracapacitor, and few of them aim at the power management strategies of vehicles with more
than two sources.
5. Open Issues and Challenges
This section puts forwards some remaining challenges of power management strategies in HEVs that should be taken
into account, and the open issues and future trends will also be discussed.
5.1. System Stability
The power system in HEVs consists of multienergy sources (e.g., motor, engine, and battery), power
switching unit, and converters. These components tend to be affected by the changes in parameters such as
temperature, discharge-charge rate, and load variation [10]. Consequently, the disturbance from the
parameters leads to changing power demands, battery overload, and low power quality, which eventually
results in the instability of the vehicle power system [65]. To deal with the challenge for system stability, the
design of the power management strategies is ought to take the terminal cost and stability constraints into
account to guarantee the stable system operation [66, 67].
5.5. Battery Aging
Battery aging is a common issue in many types of batteries. During the charging and discharging process,
chemical reactions take place inside the battery constantly which corrodes the cathode of the battery until the
cathode completely deteriorates. Batteries should be replaced regularly if the aging issue is serious. Thus,
battery exerts an important influence on the overall cost of HEVs. Although many studies have been
conducted on power management strategies of HEV, only a few of them take this issue into consideration
[79].
One promising solution is to combine a supercapacitor with the battery. Compared to supercapacitor, battery has better
energy density but poor power density to release energy sharply. Moreover, the cycling life of battery is much shorter.
On the other hand, although supercapacitor has lower energy density, it generally has much higher power density. The
combination of the both can play a complementary role.
5. Conclusions
The decreasing of fossil fuel energy sources and the increasing of the carbon emission force the technology to the
systems which have renewable energy sources as a design philosophy. Among several technologies, hybrid electric
vehicles are a hot subject today that has the advantages of lower fuel consumption, lower operating costs, lower noise
pollution, low emissions, smaller engine size and long operating life. Hybrid electric vehicle studies are expected to be
more popular in future years with development of battery technology, control techniques and government support
to the automobile owners and manufacturers.
This paper has presented an overview of HEV with a focus on general hybrid power train configurations and energy
management strategies . The practical vehicle applications and patents are also exhibited to illustrate the current state
of HEV technologies. Different strategies and future trends have been critically discussed. The review of published
articles, research projects and patents show that the engine size on the vehicle will get smaller and/or disappear in the
next future. HEV topologies can also change in near future with the decrease in the cost price of
other renewable energy sources. Finally, the comprehensive overview of studies in the literature helps to document
present trends and explore future trends of HEV.
3. Conclusion Environment protection and energy crisis have urged the development of EV. However, PEV is not
widely used currently. The main reason is that they could not satisfy the consumers’ need due to high initial cost and
short driving range. Thus, BEV will be designed mainly for short range, such as community transportation. Although
FCEV has longterm potential for future main stream vehicles due to zero emission and comparable driving range with
conventional vehicles, the major challenge for developing FCEV is how to investigate low-cost FC and refueling
system. Consequently, HEV can meet consumers’ need currently and will grow in faster rate. The main issue of HEV
is how to optimize the multiple energy sources to obtain best fuel economy or low emission at lower cost. This paper
has presented an overview of HEV with focus on the configurations, main issues, especially the control of HEV, and it
elaborates the EM approaches. The EM problem is the hot research area until now, and it is also the critical
technology for HEV. Global optimization approach can obtain global optimum, but it requires substantial amount of
computational time and does not implement real time, so the global optimization approach, such as DP, usually serves
as a benchmark for evaluating other EM strategies. Instantaneous
4. Power management strategy
The PMS mainly aims to divide the demanded power among different energy sources to sustain the battery
SOC, optimize the drivetrain efficiency, and decrease the fuel consumption and emissions or other
concerned objectives. In this section, the PMSs are divided into two classes (i.e. offline and online) in
accordance with the criterion of use in real-time implementation because the purpose of designing any PMS
is to apply it in real-world applications or to be used as a benchmark to verify the effectiveness of other
methods.
3.1. Offline PMS
Usually, this type of PMSs cannot be used in real-time application because it requires prior knowledge of global
information such as the whole drive cycle, and the algorithms take too much computational effort before finding the
solution. However, the main benefit of such methods is to verify the optimality of others. They generally refer to the
global optimization algorithms or stochastic search methods such as genetic algorithm (GA), simulated annealing
(SA), particle swarm optimization (PSO), parallel chaos optimization algorithm (PCOA), bee algorithm (BA), etc. More
information about their exclusive features can be found in reference [11].
3.2. Online PMS Differing from the offline PMS, the online PMS consists of rule-based and optimization-based PMS
as shown in Fig. 4
3. Powertrain control techniques for HEV
The most important criteria in HEV design is to get most effective results with controlling conversion of energy on
the powertrain. This overall effectiveness is usually checked against one of the so-called standard drive cycles in the
EU, USA or Japan to enable fair comparisons to be made. Therefore the controller design is the key point of the
design process. The aim of this section is to review powertrain control strategies for HEVs and to discuss their
performances. The control strategies aim to satisfy a number of goals for HEVs. There are four key goals [31]:




Maximum fuel economy.
Minimum emissions.
Minimum system cost.
Good driving performance.
To achieve these goals, hardware configurations and powertrain control strategies are designed together [32]. The
hardware configuration dictates to some extent what control strategies make sense.
3.1. Energy management strategies for HEV
The electric motors in HEV systems are powered by battery pack throughout electronic devices. The discharged
battery is recharged by ICE or from regenerative braking. The electric drive mode is very limited according to battery
power. If a more powerful battery is applied to the vehicle, it will increase electric drive range of the vehicle and it
improves the fuel efficiency. However, the battery packs need to be recharged using an electric outlet
since the regenerative braking and limited engine usage will not be sufficient to fully recharge the larger battery packs.
The operation modes of the HEV fed from battery and ICE are described in Fig. 6.
When the vehicle is started, ICE warms up [33]. If necessary, the electrical motor works as a generator and it converts
mechanical energy to the electrical energy. The produced electrical energy is stored in the battery. The ICE supplies
energy to the powertrain and, if needed, to the battery when the vehicle is at cruising speed. If the vehicle needs more
power such as in ‘‘Passing Mode”, the ICE and battery supply energy to the powertrain. Nonetheless, the regenerative
braking converts otherwise wasted energy from braking into electricity and stores it in the battery when the vehicle is
in ‘‘Braking Mode”. When the vehicle is in neutral position, such as at a red light, the ICE and electric motor
shut off automatically, so that energy is not wasted in idling. The battery continues to supply power auxiliary systems,
such as air conditioner, warning indicators and audio system. The operation modes of HEV are determined by energy
management strategies which are philosophy behind the power controller as shown in Fig. 7. Energy management
control strategies can be divided into the two main topics as shown in Fig. 8: Rule Based (RB) Control Strategy and
Optimization Based Control Strategy [35].
Fig. 8. Energy management control strategies for HEV.
3.1.1. Rule Based Control Strategy
The main aspect involved in rule-based energy management approaches is their effectiveness in real time supervisory
control of power flow in a hybrid powertrain. The rules are designed based on heuristics, intuition, human expertise
and even mathematical models and generally, without a priori knowledge of a predefined driving cycle. These
strategies can be classified into deterministic and fuzzy rule based methods. The main idea of rule based strategies is
commonly based on the concept of load-leveling [35]. The idea behind load-leveling an ‘‘IC-dominated hybrid” is to
move the actual ICE operating point as close as possible to some predetermined value for every instant in time during
the vehicle operation. If the best efficiency is needed, the vehicle operation points will be forced in the vicinity of the
best point of efficiency at a particular engine speed. If the best fuel economy is needed, then vehicle operation points
will be forced in the vicinity of that point. The resulting power difference will be used or contributed by the electric
machine (EM). Since the EM has a considerably smaller power rating than the ICE and the degree of hybridization
(DOH) is on the ICE side, it can be reasonably assumed that the efficiencies of the EM and the battery pack will have
a smaller influence on the overall efficiency [36].
3.1.1.1. Fuzzy rule based methods
Looking into a hybrid powertrain as a multi domain, nonlinear and time varying plant, fuzzy logic seems to be the
most logical approach to the problem. In fact, instead of using deterministic rules, the decision making property of
fuzzy logic can be adopted to realize a real time and suboptimal power split. In other words, the fuzzy logic controller
is an extension of the conventional rule based controller. The main advantages of fuzzy rule based methods are the
following: (1) robustness, since they are tolerant to imprecise measurements and component variations and (2)
adaptation, since the fuzzy rules can be easily tuned, if necessary [35]. Fuzzy rule based methods are composed of
Conventional Fuzzy Strategy [37], Fuzzy Adaptive Strategy [38] and Fuzzy Predictive Strategy [38].
3.1.1.2. Deterministic rule based methods
Heuristics based on analysis of power flow in a hybrid powertrain, efficiency/fuel or emission maps of an ICE and
human experiences are utilized to design deterministic rules, generally implemented via lookup tables, to split
requested power between power converters [35]. Deterministic rule based methods consist of Thermostat (on/off)
Strategy [39], Power Follower (Baseline) Control Strategy [40], Modified Power Follower (Base Line) Strategy [41]
and State Machine Based Strategy [42].
3.1.2. Optimization Based Control Strategy
These control strategies maximize the efficiency of the powertrain while minimizing the loss [43]. The optimal
reference torques for power converters and optimal gear ratios are calculated by minimization of a cost function
generally representing the fuel consumption or emissions. If this optimization is performed over a fixed driving cycle,
a global optimum solution can be found. In fact, the global optimal solution is no casual in that it finds the minimum
fuel consumption using knowledge of future and past power demands. Obviously, this approach cannot be used
directly for real time energy management; however, it might be a basis of designing rules for online implementation or
comparison for evaluating the quality of other control strategies [35].
3.1.2.1. Global optimization.
A global optimization algorithm on the powertrain flows has been developed on the basis of the Bellman optimality
principle and applied to fuel cell HEVs. The global optimization aims at minimizing the cumulative energy loss
throughout the cycle [44]. There are several reported solutions to achieve performance targets by optimization of a
cost function representing efficiency and emissions over a drive cycle, yielding to global optimal operating points
[35]. Global optimization control strategies can be divided into Linear Programming [45], Control Theory Approach
[46], Dynamic Programming [47], Stochastic DP [48], Game Theory [49] and Genetic Algorithm [50].
3.1.2.2. Real time optimization.
The global optimization techniques are not directly application for real time development, considering the fact that
they are casual solutions. In order to develop a cost function used in instantaneous optimization, in addition to a
measure for fuel consumption, variations of the stored electrical energy should also be taken into account to guarantee
electrical self-sustainability [35]. Real time optimization control strategies can be divided into Real Time Control
Based on Equivalent Fuel Consumption [51,52], Decoupling Control [53], Robust Control Approach [54] and Optimal
Predictive Control [55].
3.2. Electronic controller units in HEVs
Electronic controller unit is the embedded system that manages electrical systems or subsystems in a conventional
vehicle such as the energy conversion, the air conditioner, vehicle speed and the warnings on the instrument panel.
The electronic controller units of the hybrid system can be classified as battery management unit (BMU), ultra
capacitor control unit (UCU), electronic vehicle control unit (EVCU), engine control unit (ECU), electronic braking
system control unit (EBSCU) and drive system control unit (DSCU) as shown in Fig. 9.
Fig. 5.1 Rear-wheel-drive series HEV: a schematic diagram, b energy flow diagram
Table 2
Benefits of the series hybrid configuration [26].
Features
Optimized efficient power plant
Modular power plant
possibilities
Optimized efficient
traction drive line
Engine down sizing
Space packaging
advantages
Fast ‘‘black box” service
exchange possible
Long operational life
Mature well proven
technology
Excellent transient
response
Zero emission operation
possible
Disadvantages Larger traction drive
system
Careful design algorithms
a prerequisite
Multiple energy
conversions
Vehicles systems/
applications
TEMSA Avenue Hybrid Tesla ultra light rail
Orion VII Conventional light rail
Wrightbus electrocity New Tesla buses
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7. Conclusion and Future Direction
As HEVs are gaining more popularity, the role of the energy management system in the hybrid drive train is
escalating. A thorough description and comparison of all the control strategies to optimize the power split between
the primary and secondary sources of HEVs/PHEVs used are given here. Evolution of control strategies from
thermostat to advanced intelligent methods is included in the study. Rule-based controllers are easily
implementable, but the resultant operation may be quite far from optimal; that is, the power consumption is not
optimized for the whole trip. In order to achieve the global optimality a priori information of trip is required.
Although real-time energy management is not directly possible using optimization-based methods, an instantaneous
cost function based strategy may result in real-time optimization. The strategies discussed in this paper are real-time
implementable and are robust in nature. Table 1 is the concluding table and serves as a guide to choose the correct
method of optimization. It is suggested that strategies should take less computational time, provide global optimal
results, and get fit to the dynamic simulation environment. To obtain reduced liquid fuel consumption and larger
electric operating range without compromising with the speed and performance of vehicle, a new technology, that
is, a PHEV, is in practice globally. PHEVs’ charge depletion mode of operation is desirable, but a blended mode of
operation