**1. Introduction**

Hybrid electric vehicles (HEVs) are one of the promising solutions for reducing carbon emissions in the transportation sector. During the past two decades, many different architectures for hybridized powertrains have emerged [1]. Unfortunately, despite their relative technology maturity and their proven effectiveness in reducing fuel consumption, the market penetration of HEVs is still poor [2]. The main factor for low sales rates is the higher initial cost of these vehicles compared to traditional vehicles with only internal combustion engines (ICEs). In contrast to expensive full HEVs, which use high-voltage/-power electric machines and electronics, this work investigates an economical low-voltage hybrid system, called a power split supercharger (PSS), as shown in Figure 1.

The PSS, configured with a 9 kW 48 V motor, can drive a supercharger to pressurize the intake air of the engine or it can operate as a regular parallel hybrid system and supply/draw torque directly to/from the crankshaft when the supercharger is locked and bypassed. The inadequate torque of a small naturally aspirated (NA) ICE requires conventional mild hybrid systems to employ larger NA or boosted ICEs for full performance. However, the PSS can provide sufficient boost to a small

engine to provide good acceleration while taking advantage of engine downsizing and hybridization to improve efficiency.

**Figure 1.** Powertrain schematic with a power split supercharger.

The planetary gear set and the electric motor speed control capability decouple the boost pressure generated by the PSS from the crankshaft speed, resulting in a fast torque response and improved vehicle drive-ability compared to traditional boosting devices, such as turbochargers or mechanical superchargers. Flexible supercharging can also be achieved with an electric supercharger such as the HyBoost system from Valeo [3]. However, powering the supercharger solely with electricity necessitates a larger battery and motor, leading to a higher system cost. Figure 2a shows the required supercharger mechanical power in a 1.6 L gasoline engine studied here, while Figure 2b shows the corresponding motor power in the PSS system for different engine speeds and torques. While for the range of operating points shown the supercharger power is as high as 15 kW, most of this power is supplied by the engine crankshaft. In every operating condition either a small portion of the power comes from the motor or the motor is slightly generating. This characteristic is especially useful for scenarios such as hill climbing, shown in Figure 3, where the supercharger has to provide a continuous boost pressure due to the high requested torque. For the simulated example shown in Figure 3, vehicle cruising at 110 km/h with a road grade of 5◦ for 20 min, a small SUV with the PSS would slightly charge a 2.5 kW.h battery, while a purely electric supercharger (eSC) would completely deplete the battery, as demonstrated in Figure 3b. The modeled vehicle and engine are explained in further detail in the following sections.

This electric power and energy accessibility problem has pushed vehicle manufacturers to use electric superchargers in combination with a turbocharger, examples of which are Volvo T6 and T8 engines [4]. In these powertrains, the turbocharger can be used during steady state, and the supercharger can make the transients faster. The PSS system, however, can be used as a stand-alone boosting device reducing the system cost in addition to enabling hybrid functionalities such as regenerative braking and start-stop. This work develops an online energy management system for an engine equipped with a PSS and experimentally verifies the fuel economy benefits of the device when it replaces a conventional turbocharger.

While in traditional vehicles the driver's entire requested torque is supplied by an ICE, HEVs need an effective energy management system to determine the power split ratio between the engine and the battery at each time instant. Energy management methods for HEVs are extensively investigated in literature. These methods are often classified as optimization-based methods and rule-based methods [5,6]. Rule-based approaches are usually a set of conditional statements based on simple principles and heuristics; hence, they are easily implementable. As an example, thermostatic control, which is developed for a series HEV [7], turns the engine on or off depending on the battery SoC. Although some rules are derived from optimization results, these methods do not fully exploit the powertrain flexibility and do not guarantee optimal performance. Furthermore, the generated rules are not reusable for a different powertrain configuration or control objective.

**Figure 2.** (**a**) Supercharger mechanical power, (**b**) corresponding motor power in the power split supercharger (PSS) system, both for a 1.6 L gasoline engine.

**Figure 3.** (**a**) Vehicle climbing a 5◦ hill at 110 km/h , (**b**) a 2.5 kWh battery state of charge (SoC) variation for the vehicle with PSS compared to the same vehicle with an electric supercharger (eSC).

Optimization-based approaches can more effectively identify optimum solutions at the price of complexity as they minimize a cost function subject to the system physics and constraints. Various performance metrics such as fuel consumption or emissions can be included in the optimization cost function to achieve different performance goals. The optimization horizon can vary from a single time step, as in equivalent consumption minimization strategy (ECMS) [8], to multiple time steps, such as with model predictive control (MPC) [9], or over the entire drive cycle, as in dynamic programming (DP) [10,11]. Note that only the methods that minimize fuel consumption over the full drive cycle give the global optimum solution; however, these methods are prohibitively computationally expensive while also requiring knowledge of future driver demands. Nevertheless, they provide a criterion for evaluating other energy management algorithms in addition to giving insight into optimal policies.

The charge-sustaining global optimal energy management strategy for a vehicle with the PSS was formulated and solved using DP in a prior work [12], and a simple online energy management system based on ECMS was also previously presented and tested in simulation [13]. This work extends our previous efforts by developing an Adaptive-ECMS (A-ECMS) and documenting the fuel economy benefits of the PSS through advanced hardware-in-the-loop (HIL) experiments. The main contributions of this work are as follows: first, an Adaptive-ECMS energy management system is introduced to select the PSS mode and its power split ratio. Second, the implementation of the hardware-in-the-loop experiments is described in detail, and some practical challenges are explained. Third, the operation of the PSS is demonstrated experimentally, and finally, the effectiveness of the PSS hardware and the developed controllers in fuel consumption reduction of a vehicle is quantified over the standard FTP75 cycle.

After introducing the utilized hardware and models, the global fuel consumption minimization problem is described briefly. An ECMS is formulated for selecting the PSS mode and its power split ratio in Section 3.2 and the Adaptive-ECMS is described in Section 3.3. Section 4 presents the engine dynamometer experimental testbed and the details of HIL implementation. The experimental demonstration of fuel economy results and PSS operation are shown in Section 5, and the paper concludes with the main findings of the work.

#### **2. Experimental Hardware and Model Framework**

The baseline engine is a 1.6 L Ford EcoBoost engine, which is a 4 cylinder spark ignition (SI) turbocharged engine. The turbocharger is replaced by the PSS in the alternative powertrain studied in this work. Figure 1 shows a schematic view of the engine with the PSS and other powertrain components. The PSS is configured with a planetary gear set, a roots supercharger, a motor, a bypass valve, and a brake. The sun gear is attached to the supercharger, the ring is connected to the motor, and the carrier is coupled with the engine crankshaft through a set of belt and pulleys. The PSS can enable two distinct operating modes by controlling the motor, bypass, and the brake. In boosting mode, the bypass is closed, the brake is released, and the motor can control the supercharger speed and resulting boost pressure independently of the crankshaft speed. In torque assist mode, the brake locks the sun gear and the supercharger is bypassed. In this mode the planetary gear set acts as a regular gear set, which enables the motor to supply/draw torque to the crankshaft for start-stop, regenerative braking, or assisting the crankshaft. The engine fuel consumption map, shown in Figure 4, is produced using a high-fidelity GT-Power model, which is described in detail and validated against engine dynamometer experiments elsewhere [14]. In Figure 4, *τ*max *<sup>e</sup>*,NA is the maximum torque that the NA engine can produce, *τ*max *<sup>e</sup>*,*<sup>B</sup>* is the maximum engine torque when the PSS is in boosting mode, and (*τe*,NA + *τ*TA)max is the powertrain maximum torque in torque assist mode (maximum motor torque added to the crankshaft).

**Figure 4.** Brake specific fuel consumption (BSFC) map for the engine with the power split supercharger (PSS). The naturally aspirated engine maximum torque, *τ*max *<sup>e</sup>*,NA, the powertrain maximum torque in torque assist mode, (*τe*,NA + *τ*TA)max, and the maximum engine torque during boosting mode, *τ*max *<sup>e</sup>*,B , are also represented.

The modeled vehicle is a MY2015 Ford Escape crossover SUV. The drivetrain model includes the crankshaft dynamics, a friction clutch, a torque converter, and a 6-speed automatic transmission. The model details and control strategy are described in a prior work [14]. The driver model is a gain-scheduled proportional + integral (PI) controller and uses a 1 s preview of the tracking error and vehicle acceleration. A 1.2 kWh lithium-ion battery is assumed for the rest of this study. An open circuit voltage with a resistance (OCV-R) is used to model the battery and compute its state of charge dynamics, detailed in [12].

#### **3. Energy Management System**

#### *3.1. Global Fuel Consumption Minimization*

The optimal energy management problem for a vehicle with a PSS and with the full driving profile preview was formulated and solved using DP elsewhere [12,15]. In this work the DP solution was used as a benchmark to evaluate the effectiveness of the online energy management algorithm; thus, only a summary of the DP formulation is presented. The cost function for the global fuel consumption minimization problem is given in Equation (1). Different terms from left penalize the fuel flow rate, the gear shifts, the engine cranking (for start-stop), and the PSS mode, respectively,

$$\min \left\{ \sum\_{k=1}^{N} \left( \dot{m}\_f(k) T\_\delta + a |n\_\delta(k) - n\_\delta(k-1)| + \beta \left( \max(\mathbf{x}\_\varepsilon(k) - \mathbf{x}\_\varepsilon(k-1), 0) \right) + \lambda \left( 1 - u\_{\text{Br}}(k) \right) \right) \right\} \tag{1}$$

where *k* refers to the *k*th step time, *N* is the problem horizon, which is the full drive cycle, *Ts* is the sampling time equal to 1 s, *m*˙ *<sup>f</sup>* is the fuel flow rate, *ng* represents the gear number, *xe* stands for the engine on/off state, and *u*br is the PSS brake position used to indicate the PSS mode, where *u*br = 0 is the boosting mode and *u*br = 1 the torque assist mode. The coefficient *α* controls the gear shift frequency, *β* is the engine cranking fuel penalty, and *λ* has a very small value to enforce the brake locked as the default mode. The full detail of the problem constraints are presented in the original work [15]. The battery state of charge, the engine on/off state, and the gear number are the modeled states. The latter two had to be modeled as states to be penalized in the objective function. The control inputs for this problem are the PSS mode, the commanded torque assist from the electric motor, the engine on/off command, and the gear shift command. A MATLAB-based dynamic programming function [16] was used to solve this problem.

In the prior work the manufacturer map for the electric motor was used to estimate the fuel economy, and no loss was assumed for the planetary gear set and pulleys. However, the experiments showed that both the motor efficiency and its torque limits are different from the manufacturer map. Hence, new experimentally validated maps were produced to update the results in this work. Figure 5 shows the measured efficiency from/to the electric power, measured by an AVL battery emulator, to/from the engine-dynamometer crankshaft, measured using a torque meter. The maximum and minimum motor torque limits are also presented. Compared to the prior maps, the losses were up to 15% more, especially at low speed and negative torques. The minimum motor torque was also slightly higher at lower engine speeds. Both of these reduced the recuperated power from regenerative braking during a cycle. The DP results presented later in Table 1 are updated with the new map.

**Table 1.** Drive cycle fuel consumption results for Ford Escape MY2015.


**Figure 5.** Experimentally generated motor and gear set map.

#### *3.2. Equivalent Consumption Minimization Strategy*

The equivalent consumption minimization strategy assigns an equivalent fuel flow rate to the electric energy consumption by using an equivalence factor and minimizes the sum of the engine and motor fuel flow rate at only the current time step [8,17]. As this strategy does not need any preview information it can be implemented online. The motor torque, *τm*, is calculated as

$$\pi\_m = \underset{\pi\_m}{\text{argmin}} \left( \dot{m}\_f(\pi\_{e\prime}, \omega\_{\varepsilon}) + a\_{\text{eq}} P\_m(\pi\_{m\prime}, \omega\_m) \right) \tag{2}$$

in which *τ<sup>e</sup>* and *ω<sup>e</sup>* represent the engine torque and speed respectively, *ω<sup>m</sup>* is the motor speed, and *α*eq is the equivalence factor. The energy management system (EMS) of a vehicle with a PSS has to select the PSS mode first. Only if the torque assist mode is selected will the optimum motor torque need to be determined in the next step to minimize the powertrain fuel consumption. When the boosting mode is selected the motor torque is not an optimization parameter but is instead used to control the boost pressure and, hence, the engine torque.

Boosting is only justified when the requested crankshaft torque, *τ<sup>d</sup>* crk, is larger than the torque limit that the naturally aspirated engine can produce, *τ*max *<sup>e</sup>*,NA (above the blue area in Figure 4), because simultaneous boosting and throttling is not an efficient policy [18]. Therefore, it is fuel efficient to lock and bypass the supercharger when *τ<sup>d</sup>* crk < *<sup>τ</sup>*max *<sup>e</sup>*,NA. On the other hand, due to the small motor size, the NA engine with direct torque assist from the motor cannot produce a torque higher than (*τe*,NA + *τ*TA)max, shown in Figure 4; thus, when a high torque in the yellow area of Figure 4 is requested, the PSS has to work in boosting mode. Finally, when the requested torque is smaller than the maximum powertrain torque in torque assist mode and larger than the NA engine torque limit (green area in Figure 4), the requested torque can be achieved through either mode. A consumption minimization rule is introduced to determine the PSS mode that produces the minimum equivalent fuel consumption as follows:

$$u\_{\rm br} = \begin{cases} 0 & \text{if } & \tau\_{\rm crk}^d > (\tau\_{\rmTA} + \tau\_{\rm c,NA})^{\rm max} \\ 1 & \text{if } & \tau\_{\rm crk}^d \le \tau\_{\rm c,NA}^{\rm max} \\ \arg\min(\dot{m}\_{f,\rm cq}) & \text{otherwise} \end{cases} \tag{3}$$

in which *u*br is the PSS brake position used to represent the PSS mode, and *m*˙ *<sup>f</sup>* ,eq is the equivalent fuel flow rate of the engine and motor, computed for each mode as

• Torque assist mode (*u*br = 1):

$$\dot{m}\_{f,\text{eq}} = \min\_{\tau\_{\text{m}}^d} \left( \dot{m}\_f \left( \tau\_e^d, \omega\_e \right) + a\_{\text{eq}} P\_{\text{m}} \left( \tau\_{\text{m}}^d, \omega\_{\text{m}} \right) \right) \tag{4}$$

$$
\tau\_c^d = \tau\_{\rm crk}^d - \tau\_{\rm IA}^d \tag{5}
$$

$$
\tau\_m^d = \frac{\mathcal{S}\mathcal{R}}{n\_{\rm im} n\_{\rm ri} (\mathcal{g}\_{\rm S} + \mathcal{g}\_{\rm R})} \tau\_{\rm TA}^d \tag{6}
$$

where the superscript *d* refers to the desired or commanded values, *gR* is the ring gear teeth number, *gS* is the sun gear teeth number, *nri* is the ring-to-idler gear ratio, and *n*im is the idler-to-motor gear ratio. The variable *τ*TA represents the torque assist from the motor on the crankshaft and is related to the motor torque through (6).

• Boosting mode (*u*br = 0):

$$
\dot{m}\_{f, \text{eq}} = \dot{m}\_f(\tau\_\text{\epsilon}^d, \omega\_\text{\epsilon}) + \text{a}\_{\text{eq}} P\_\text{m}(\tau\_\text{\epsilon}^d, \omega\_\text{\epsilon}) \tag{7}
$$

$$
\tau\_\text{\epsilon}^d = \tau\_{\text{crk}}^d \tag{8}
$$

$$
\tau\_c^d = \tau\_{\rm crk}^d \tag{8}
$$

$$
\tau\_{\rm crk} = \tau\_{\rm crk} \quad \dots \quad \dots \quad \dots \quad \dots \quad \dots \quad \dots \quad \dots \tag{9}
$$

Equation (8) indicates that during boosting mode the entire crankshaft requested torque has to be supplied by the engine. In this mode the supercharger pressure ratio is decoupled from the engine operating speed. Keeping the throttle valve open in the boosted condition reduces the engine losses and increases the efficiency. Adopting this strategy, the steady-state motor power for driving the supercharger can be mapped into engine operating points shown in Figure 2b. This map is used to calculate the equivalent fuel flow rate of the motor in boosting mode.

The solution to Equations (2) and (3) is computed for various values of the equivalence factor *α*eq. Figure 6a–c shows the solution to Equation (3) for equivalence factors of 0.13, 0.18, and 0.23 kg/kWh, respectively. The green color in these plots indicates boosting mode, while the red color shows torque assist mode. Figure 7a–c presents the optimum motor torque during torque assist mode generated from (2) for the same equivalence factors.

The equivalence factor represents the relative value of the electric power. A smaller equivalence factor uses the torque assist mode more often and uses the motor to assist the crankshaft over a larger operating region (more red color in Figures 6a and 7a). A higher equivalence factor increases the penalty for electric power, which causes the ECMS controller to use the boosting mode more often in Figure 6b,c while also using the motor to generate more energy, often at low loads (more green color in Figure 7b,c).

**Figure 6.** Equivalent consumption minimization strategy (ECMS)-generated PSS mode for different equivalence factors. The green color represents boosting mode, and the red color indicates torque assist mode. (**a**) *α*eq = 0.13 kg/kWh, (**b**) *α*eq = 0.18 kg/kWh, and (**c**) *α*eq = 0.23 kg/kWh. Increasing the equivalence factor shifts the optimal strategy from torque assist to favor boosting mode.

**Figure 7.** ECMS generated motor torque during torque assist mode for different values of equivalence factor (**a**) *α*eq = 0.13 kg/kWh, (**b**) *α*eq = 0.18 kg/kWh, and (**c**) *α*eq = 0.23 kg/kWh.
