Presence of an alternative energy source along with the Internal Combustion Engine (ICE) in Hybrid Electric Vehicles (HEVs) appeals for optimal power split between them for minimum fuel consumption and maximum power utilization. Hence HEVs provide better fuel economy compared to ICE based vehicles/conventional vehicle. Energy management strategies are the algorithms that decide the power split between engine and motor in order to improve the fuel economy and optimize the performance of HEVs. This paper describes various energy management strategies available in the literature. A lot of research work has been conducted for energy optimization and the same is extended for Plug-in Hybrid Electric Vehicles (PHEVs). This paper concentrates on the battery powered hybrid vehicles. Numerous methods are introduced in the literature and based on these, several control strategies are proposed. These control strategies are summarized here in a coherent framework. This paper will serve as a ready reference for the researchers working in the area of energy optimization of hybrid vehicles.

Hybrid Electric Vehicles (HEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) consist of two power sources, that is, (1) Internal Combustion Engine (ICE) and (2) battery. Power split between these two is of utmost importance to minimize the fuel consumption without affecting the vehicle speed. The literature reveals that various power split strategies have been developed and implemented. These strategies vary in optimization type (global or local), computational time, structural complexity, a priori knowledge of driving pattern, and effectiveness of the algorithm. A survey of these available methods would be of great use for researchers and practitioners working on HEVs/PHEVs.

This paper includes several powerful methods of energy optimization proposed in the literature.

These methods are not mutually exclusive and can be used alone or in combinations. The authors have compiled more than 180 papers cognate with optimal performance of HEVs/PHEVs published till 2012. The authors apologize if any paper, method, or improvement is unintentionally omitted. Figure

Graphical representation of papers published per year.

Automobiles have made great contribution to the growth of modern society by satisfying the needs for greater mobility in everyday life. The development of ICE has contributed a lot to the automobile sector. But large amounts of toxic emissions in the form of carbon dioxide (CO_{2}), carbon monoxide (CO), nitrogen oxides (NO_{
x}), unburned hydrocarbons (HCs), and so forth have been causing pollution problems, global warming, and destruction of the ozone layer. These emissions are a serious threat to the environment and human life. Also, as petroleum resources are limited, consumption of petroleum needs to be reduced. One prominent solution to these problems is to go for an alternate transportation technology, which uses ICE as primary power source and batteries/electric motor as peaking power source. This concept has brought the new transportation medium such as Electric Vehicles (EVs), HEVs and PHEVs, which are clean, economical, efficient, and environment friendly.

The EVs are enabled by high efficiency electric motor and controller and powered by alternative energy sources. The first EV was built by a Frenchman Gustave Trouve in 1881. It was a tricycle powered by a 0.1 hp direct current motor fed by lead-acid batteries. EV is a clean, efficient, and environment friendly urban transportation medium but has limited range of operation.

Due to higher battery cost, limited driving range, and performance of EVs, HEVs came into existence. HEVs use both electric machine and an ICE to deliver power during vehicle propulsion. It has advantages of both ICE vehicles and EVs and eliminates their disadvantages [

In HEVs batteries are charged either by engine or by regenerative braking and are not plugged-in externally which limits its electric range. They also take longer time in recharging. PHEVs offer a promising medium-term solution to reduce the energy demand as the batteries are charged through the grid. PHEVs are displacing liquid fuels by storing the energy in a battery with cheaper grid electricity [

HEVs are classified mainly into three categories: (1) series hybrid, (2) parallel hybrid, and (3) series-parallel (power-split) hybrid. The series configuration consists of an electric motor with an ICE without any mechanical connection between them. ICE is used for running a generator when the battery does not have enough power to drive the vehicle; that is, ICE drives an electric generator instead of directly driving the wheels. Series hybrids have only one drive train but require two distinct energy conversion processes for all operations. These two energy conversion processes are gasoline to electricity and electricity to drive wheels. Fisher Karma, Renault Kangoo, Coaster light duty bus, Orion bus, Opel Flextreme, and Swiss auto REX VW polo use series configuration.

In parallel configuration, single electric motor and ICE are installed in such a way that both individually or together can drive the vehicle. Parallel hybrids allow both power sources to work simultaneously to attain optimum performance. While this strategy allows for greater efficiency and performance, the transmission and drive train are more complicated and expensive. Parallel configuration is more complex than the series, but it is advantageous. Honda’s Insight, Civic, Accord, General Motors Parallel Hybrid Trucks, BAS Hybrid such as Saturn VAU and Aura Greenline, and Chevrolet Mali by hybrids utilize parallel configuration.

Power split hybrid has a combination of both series and parallel configuration in a single frame. In this configuration engine and battery can, either alone or together, power the vehicle and battery can be charged simultaneously through the engine. Basically, it extends the all-electric range (AER) of hybrid vehicle. The current dominant architecture is the power-split configuration which is categorized into two modes: (1) one (single) mode and (2) two (dual) modes. Single mode contains one planetary gear set (PGS) and dual mode contains two PGS which are required for a compound power split. It is further classified into three types: (1) input split, (2) output split, and (3) compound split as determined by the method of power delivery.

In the input split power configuration or single mode electromechanical infinitely variable transmission (EVT), planetary gear is located at the input side as shown in Figure

Power-split configurations: (a) input split, (b) output split, and (c) compound split.

The output split power train consists of one planetary gear at the output side as shown in Figure

In dual mode configuration, the two clutches provide a torque advantage of the motor at low speed while fundamentally changing the power flow through the transmission as shown in Figure

All the configurations of HEV can be employed in PHEV’s drive trains. In PHEVs battery is initially charged through the mains power supply to the full capacity, which supports HEV architecture to propel it for longer distances with a very less fuel consumption.

The presence of two power sources focuses on the need of designing an energy management strategy to split power between them. The strategy should be able to minimize the fuel consumption and maximize the power utilization. In HEVs, the battery is a supporting power source which gets charged when ICE powers the vehicle and also through regenerative braking. In HEVs the state of charge (SOC) of the battery is the same at the start and end of the trip; that is, it works in charge sustaining mode. In PHEVs, the batteries are charged through mains; therefore it can be depleted to the permissible minimum level at the end of the trip; that is, it works in a charge depletion mode. PHEVs may call upon to work in charge sustaining, charge depletion, or combination of both based on the requirement.

Due to the complex structure of HEVs/PHEVs, the design of control strategies is a challenging task. The preliminary objective of the control strategy is to satisfy the driver’s power demand with minimum fuel consumption and toxic emissions and with optimum vehicle performance. Moreover, fuel economy and emissions minimization are conflicting objectives; a smart control strategy should satisfy a trade-off between them.

Various control strategies are proposed for optimal performance of HEVs/PHEVs. The strategies published till 2012 are reviewed and categorized here. A detailed overview of different existing control strategies along with their merits and demerits is presented. A broad classification of these strategies is given in Figure

Classification of control strategies.

There is no commonly accepted answer for “structural complexity” but the intersection of almost all answers is nonempty. Structural complexity deals with the complexity classes, internal structure of complexity classes, and relations between different complexity classes. Complexity class is a set of problems of related source-based complexity and can be characterized in terms of mathematical logic needed to express them. Computation time is the length of time required to perform a computational process.

A controller designed for a particular set of parameters is said to be robust if it performs fairly well under a different set of assumptions. To deal with uncertainty, robust controllers are designed to function properly with uncertain parameter set or disturbance set.

Local optimal of an optimization problem is optimal (either maximal or minimal) within a neighboring set of solutions. A global optimal, in contrast to local, is the optimal solution amongst all possible solutions of an optimization problem.

Control strategies are broadly classified into rule-based and optimization-based control strategy and all other subcategories are classified based on these two main categories.

Rule-based control strategies are fundamental control schemes that depend on mode of operation. They can be easily implemented with real-time supervisory control to manage the power flow in a hybrid drive train. The rules are determined based on human intelligence, heuristics, or mathematical models and generally without prior knowledge of a drive cycle.

The rule-based controllers are static controllers. Basically, the operating point of the components (ICE, traction motor, and generator, etc.) is chosen using rule tables or flowcharts to meet the requirements of the driver and other components (electrical loads and battery) in the most efficient way. The decisions are related to instantaneous inputs only. This strategy is further subcategorized into deterministic rule-based and fuzzy rule-based.

By recognizing the road load, an energy management system for belt driven starter generator (BSG) type hybrid vehicle is developed by Shaohua et al. [

The rules are designed with the aid of fuel economy or emission data, ICE operating maps, power flow within the drive train, and driving experience. Implementation of rules is performed via lookup tables to share the power demand between the ICE and the electric traction motor. Kim et al. [

Thermostat control strategy uses the generator and ICE to generate electrical energy used by the vehicle. In this strategy the battery SOC is always maintained between predefined high and low levels, by simply turning on/off the ICE. Although the strategy is simple, it is unable to supply necessary power demand in all operating modes.

Electric assist control strategy utilizes ICE as the main source of power supply and electric motor to supply additional power when demanded by the vehicle. Due to charge sustaining operation, the battery SOC is maintained during all operating modes.

L. A. Zadeh introduced the term fuzzy logic and described the mathematics of fuzzy set theory. Fuzzy logic system is unique to handle numerical data and linguistic knowledge simultaneously. Fuzzy sets represent linguistic labels or term sets such as slow, fast, low, medium, high, and so forth. In fuzzy logic, the truth of any statement is a matter of degree. Fuzzy control is simple, easy to realize, and has strong robustness. It can converse experience of designer to control rules directly. Fuzzy logic is a form of multivalued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise.

Intelligent control is performed using fuzzy logic as a tool. Fuzzy logic enables the development of rule-based behavior. The knowledge of an expert can be coded in the form of a rule base and used in decision making. The main advantage of fuzzy logic is that it can be tuned and adapted if necessary, thus enhancing the degree of freedom of control. It is also a nonlinear structure and is especially useful in a complex system such as an advanced power train. In essence a fuzzy logic controller (FLC) is a natural extension of many rules based controllers implemented (via lookup tables) in many vehicles today. Fuzzy logic based methods are insensitive to model uncertainties and are robust against the measurement of noises and disturbances but require a faster microcontroller with larger memory.

In the optimum fuel use strategy, the FLC limits instantaneous fuel consumption, calculated from the fuel use map, and maintains sufficient battery SOC, while delivering demanded torque. In the fuzzy efficiency strategy, the ICE has operated in its most efficient operating region. The operating points of the ICE are set near the torque region, where efficiency is highest at a particular engine speed. Load balancing is achieved using electric motors. This control strategy uses a motor to force ICE to operate in the region of minimal fuel consumption, while maintaining SOC in battery. Load balancing is necessary to meet power demand and avoid unnecessary charging and discharging of the electrical storage system (ESS). A major drawback of this control strategy is that the peak efficiency points are near high torque region; thereby ICE generates more torque than required, which in turn increases fuel consumption. Also, during load balancing, heavy regeneration overcharges the ESS. To avoid this, the control strategy should be used with a downsized ICE.

_{
x}, CO, and HC emissions. In order to measure the interrelationship of the four contending optimizing objectives with a uniform standard, it is essential to normalize the values of fuel economy and emissions by utilizing the optimal values of fuel consumption and emissions at current speed. The optimal values of fuel economy and emissions at particular ICE speed can be obtained from the ICE data map.

The relative weights are adaptively assigned to each parameter based on their importance in different driving environments. Moreover, weights must be selected for each ICE, based on their individual data maps. This control strategy is able to control any one of the objectives, by changing the values of relative weights. Further, tremendous reduction in vehicle emission is achieved, with negligible compromise in fuel economy.

Being robust and fast, it is advised to design FLCs for nonlinear and uncertain systems. FLCs result in small overshoot, short adjustment time, and good dynamic/static quality. Using mix-modelling approach, Arsie et al. [

In optimization-based control strategies, the goal of a controller is to minimize the cost function. The cost function (objective function) for an HEV may include the emission, fuel consumption, and torque depending on the application. Global optimum solutions can be obtained by performing optimization over a fixed DC. These control techniques do not result in real-time energy management directly, but, based on an instantaneous cost function, a real-time control strategy can be obtained. This instantaneous cost function relies on the system variables at the current time only. It should include equivalent fuel consumption to guarantee self-sustainability of electrical path. Optimization-based control strategies can be divided into two main groups, namely, global optimization and real-time optimization. These are discussed in the following sections in detail.

A global optimization technique for energy management strategy in an HEV requires the knowledge of entire driving pattern which includes battery SOC, driving conditions, driver response, and the route. Due to computational complexity, they are not easily implementable for real-time applications. Linear programming, dynamic programming, genetic algorithms, and so forth are used here to resolve vehicle energy management issues. Based on optimal control theory and assuming that minimizing the fuel consumption reduces the pollutant emissions, a global optimization algorithm is developed [

In hybrid power trains, better degree of freedom to control exists. By controlling the gear ratio and torque, an optimized design and control of a series hybrid vehicle are proposed in [

The very essence of this technique is based on the principle of optimality. Having a dynamical process and the corresponding performance function, there are two ways to approach the optimal solution to the problem. One is the Pontryagin’s maximum principle and the other is Bellman’s dynamic programming. It has the advantage of being applicable to both linear and nonlinear systems as well as constrained and unconstrained problems. But it also suffers from a severe disadvantage called curse of dimensionality which amplifies the computational burden and limits its application to complicated systems.

Since the knowledge of the duty cycle is required beforehand, the DP algorithm cannot be implemented in real time. However, its outputs can be used to formulate and tune actual controllers. The power management strategy in an HEV is computed through dynamic optimization approach by various researchers as mentioned below.

Power optimization can be done offline for known DC using deterministic DP [

For better optimality in comparison to supervisory control strategy, [

Unlike the conventional gradient based method, GA technique does not require any strong assumption or additional information about objective parameters. GA can also explore the solution space very efficiently. However, this method is very time consuming and does not provide a broader view to the designer.

Piccolo et al. [

A genetic algorithm is a powerful optimization tool which is particularly appropriate to multiobjective optimization. The ability to sample trade-off surfaces in a global, efficient, and directed way is very important for the extra knowledge it provides. In the case where there are two or more equivalent optima, the GA is known to drift towards one of them in a long term perspective. This phenomenon of genetic drift has been well observed in nature and is due to the populations being finite. It becomes more and more important as the populations get smaller. NSGA varies from GA only in the way the selection operator works. Crossover and mutation operations remain the same. This is similar to the simple GA except the classification of nondominated fronts and sharing operations. MOGA is a modification of GA at selection level. MOGA may not be able to find the multiple solutions in case where different Pareto-optimal points correspond to the same objective.

Due to the causal nature of global optimization techniques, they are not suitable for real-time analysis. Therefore, global criterion is reduced to an instantaneous optimization, by introducing a cost function that depends only on the present state of the system parameters. Global optimization techniques do not consider variations of battery SOC in the problem. Hence, a real-time optimization is performed for power split while maintaining the battery charge.

Instantaneous optimization techniques based on simplified model and/or efficiency maps are proposed in [

ECMS is developed by calculating the total fuel consumption as sum of real fuel consumption by ICE and equivalent fuel consumption of electric motor. This allows a unified representation of both, the energy used in the battery and the ICE fuel consumption. Using this approach, equivalent fuel consumption is calculated on a real-time basis, as a function of the current system measured parameters. No future predictions are necessary and only a few control parameters are required. These parameters may vary from one HEV topology to another as a function of the driving conditions. ECMS can compensate the effect of uncertainties of dynamic programming. The only disadvantage of this strategy is that it does not guarantee charge sustainability of the plant.

Equivalent fuel consumption is calculated based on the assumption that SOC variation in the future is compensated by the engine running at current operating point. Jalil et al. [

Using MPC, West et al. [

NN’s adaptive structure makes it suitable for any control applications. A well designed network can get fit to any lookup table and can adapt itself by training to update the table data. This feature makes it better than rule-based controllers. Recurrent NNs are networked with dynamic feedback which means they can also be modelled as dynamic controller. NN is an effective approach for pattern recognition and function fitting.

Baumann et al. [

PSO is a metaheuristic approach as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. However, metaheuristics such as PSO do not guarantee an optimal solution. More specifically, PSO does not use the gradient of the problem being optimized, which means PSO does not require the optimization problem to be differentiable as is required by classical optimization methods, such as gradient descent and quasi-Newton methods. PSO can therefore also be used on optimization problems that are partially irregular and noisy, change over time, and so forth.

The multilevel hierarchical control strategy optimized by the improved PSO algorithm can properly determine the direction and quantity of the energy flow in the HEVs/PHEVs and make the main power train components operate at high efficiency so that the fuel consumption can be reduced.

For parallel HEV, a multilevel hierarchical control strategy is proposed by [

Geering [_{2} emission. A supervisory energy management strategy is implemented as a global optimization problem and then converted into local and using PMP, optimal energy utilization for PHEVs is obtained. For real-time implementation of an energy management strategy, the tools used by [

As the trajectory derived from PMP might not be a global optimal solution, therefore, the control based on PMP can be considered as inferior to the DP. DP requires more computing time than PMP because DP solves all possible optimal controls to fill the optimal field. Since DP is a numerical representation of the HJB equation, it needs a similar computation load as the Hamilton-Jacobi-Bellman equation, which solves a partial differential equation. PMP solves just nonlinear second-order differential equations. The drawback of DP with regard to the computational load becomes compounded due to the “curse of dimensionality.”

Power management methodology with CVT for HEVs is implemented to optimize the power [

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

Comparison chart for various control strategies.

Methods | Structural complexity | Computation time | Type of solution | Requirement of a priori knowledge |
---|---|---|---|---|

Fuzzy logic | N | S | G | Y |

Genetic algorithm | Y | M | G | N |

Particle swarm optimization | N | M | G | N |

Energy consumption minimization strategy | Y | S | L | N |

Pontryagin’s minimum principle | N | S | L | Y |

Dynamic programming | Y | M | G | Y |

Model predictive | N | S | G | N |

Stochastic dynamic programming | Y | M | G | N |

Neural network | Y | S | G | Y |

G = global, L = local, N = no, Y = yes, M = more, and S = small.

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 may be a promising solution to extend operating electric range. The control strategies suggested so far are required to be explored more in context of operating specifications and their true potential for PHEVs.

The authors declare that there is no conflict of interests regarding the publication of this paper.

The authors are grateful to Professor S. K. Choudhary, Department of Humanities and Social Sciences, B.I.T.S. Pilani, for giving his valuable time to improve the quality of paper grammatically.