Simulated and Experimental Verification of Fuel-Efficient Truck Platooning With Model Predictive Control Under Grade and Traffic Disturbances

Efficient mobility in transportation is well enabled through automation, in which driver-assistive or driverless technologies have the potential for more optimal and robust decision making as compared to their manually driven counterparts. Whereas manual drivers can be limited in their ability to precisely command the motion of a vehicle, automation instead offers tight control that allows for motion planning and actuation aimed at optimizing particular performance categories such as fuel economy, energy economy, timeliness, comfort, and safety. Automation in particular also has the ability to consume more information about the surrounding environment to probabilistically reason about their future strategies—sourced from multiple on-board sensor measurements or connected information available from infrastructure or other vehicles. The connected exchange of information from surrounding automated vehicles ultimately reduces uncertainty in their imminent motion by broadcasting current and potentially future plans, leading to more cooperative driving strategies which enable harmony and boosted performance of transportation [1].

Heavy-duty long-haul truck transportation has received much attention in automated longitudinal control to achieve performant driving [2]. Automation offers potent strategies when managing a convoy of long-haul trucks, whereby automation can ease the burden of long trips on trained manual drivers, and has soon-realizable potentials for reducing costs in various economic facets like more efficient usage of fuel for reduced expenses, eco-planning of the trip for predictable travel times, and end-to-end driverless trips [3]. Cooperative and adaptive cruise control (CACC) has been the classical and most common choice, typically actuated with an adaptive cruise controller with additional connected exchange of acceleration information of other trucks as a feedforward term [4,5]. Advanced planning strategies can choose to form a platoon between trucks in the convoy when under appropriate highway conditions, so that drafting effects are exploited to reduce aerodynamic drag losses and thereby improve fuel economy [6]. Platooning is demonstrated in Fig. 1. PID-based CACC has been shown to be a suitable choice of control for heavy trucks in a platooning configuration when under low-intensity disturbances, such as Ref. [7] that nominally show an 8% reduction in fuel expense when platooning trucks under test-track conditions, and more recently validated in Ref. [8] for heterogeneous trucks. However, classical CACC is of insufficient performance in all typical conditions that can arise over the course of a trip: whereas real-world highway conditions were experimented and reported on in Ref. [9] for a platooned convoy of heavy-duty trucks, which found that all fuel benefits of having platooned were negated due to poor control during uphill climbs. Furthermore, the classical CACC demonstrated poor harmonization between trucks and was susceptible to disengagements that require operator intervention. With demonstrably weak performance in certain scenarios, classical CACC ultimately requires driver attentiveness and will scale poorly as automation strategies become more commonly deployed. Recently, commercial autonomous solutions for single-truck control have been found to improve fuel economy over their human-driven counterparts by up to 20% in low-speed driving and 3% in highway speed driving [10,11].

Notably, some of these strategies neglect road grade perturbations for simplicity in experimental evaluation, and in general, there are not yet well-adopted control strategies that compensate against heavy road grade in line-haul truck convoys [8,12], though some optimally guided trucks have explicitly considered road grade disturbances in planning. In the motion planning of automated trucks for improved fuel economy, He et al. [13] analytically derive fuel-optimal trajectories to guide a heavy truck through a nominal traffic disturbance and road profile, whereas in Ref. [14], a numerical fuel-optimal strategy was implemented to assess heavy truck performance on real roads with varied altitude, and Turri et al. [15] use a hierarchal control strategy that centrally plans the velocities of each truck in the convoy through dynamic programming. Engine and transmission considerations have been recently considered in Ref. [16] to improve kinetic energy usage through optimal selection of transmission modes and engine torque. Finally, Liu et al. [17] directly model fuel consumption through a pedal-to-engine framework and optimize the motion of platooned heavy trucks in a nonlinear fashion.

More advanced control strategies exist as an opportunity to improve the independence of automated vehicles and boost overall performance in a wider variety of scenarios [18]. Look-ahead predictive control algorithms can pose a multi-stage optimization problem to select a sequence of actions to predict and better mitigate the effects of system dynamics, actuator limitations, and any disturbances that are assumed to act on the system. Such formulations allow for a system designer to impose an objective function as an explicit performance criteria for a motion planning control to meet. Improved vehicle energetics are shown in Ref. [19] by calculating coasting distances from a preceding vehicle to cut unnecessary braking, improved fuel economy in Ref. [20] to account for powertrain dynamics, and in Ref. [15] to additionally exploit drafting effects for reduced aerodynamic drag in heavy trucks. Cooperative control can additionally be introduced through look-ahead preview by sharing planned intentions of distributed and connected agents [21]. Efficient string stabilization of a system of autonomous vehicles conditioned on communications between nearest neighboring agents are shown in Ref. [22], and improved safety via coordinated braking of platooned agents is shown in Ref. [23]. Importantly, Dunbar and Caveney [24] evaluate string stability of platoons under receding horizon control and give bounds on the feasible platoon sizes, and Ard et al. [25] consider communication delay and model uncertainty effects in stable platoon performance. Cooperative motion planning has been simulated on in Ref. [26] and then verified through experimentation in Ref. [27] for a large-scale mixed-traffic scenario, nominally showing up to 30% benefit in fuel economy for passenger vehicles due to harmonizing effects and shockwave suppression.

Frameworks of motion planning naturally marry with long-term routing optimizations. The formation of truck platoons are coordinated in Ref. [28] by pairing trucks with similar route destinations, the intelligent decision of when to form or re-form platoons in the event of platoon dissipation is shown in Ref. [29], and the evaluation of the large-scale fuel and emissions performance of a transportation network is shown in Ref. [30] when enabling truck platooning. Motion planning strategies also enable disturbance compensation not easily realizable with manual drivers: prediction of upcoming traffic shockwaves is leveraged in Ref. [31] to throttle down velocity ahead of time to avoid unnecessary propulsion and braking—estimating up to 20% reduction in fuel cost of heavy trucks.

When forming a look-ahead optimal control problem (OCP), a control horizon of N stages is used to directly form and evaluate a control sequence against a possibly uncertain dynamic model and constraints. In this, for computational reasons and due to uncertainty in reasoning about the future, terminal components can be designed to truncate the end of the control horizon via an estimated cost-to-go for stages N + 1 → ∞, or via constraining terminal sets the states must lie within [32]. These components are critical in performance and stability of the strategy and can be utilized to enable intelligent behavior of a controller such as in Refs. [15,19] which encourage coasting over wasteful braking in automated cruise control. Recently, an active area of research involves learning the terminal components via prior experience and exploration of a feedback control policy [33]. These learning methods can additionally be fused with more traditional adaptive methods for online identification of a model, constraints, and their uncertainties. These include methods such as learning terminal safe sets and a cost-to-go by repeated trials [34], learning a model through data-driven approaches and then exploiting through a look-ahead control [35], and identification and compensation against disturbances [36,37]. As another aspect of learning, model approximators, such as neural networks (NNs), can be fused into a look-ahead predictive control approach, such as Ref. [38] which learn a recursive stochastic process and uncertainty bounds for a then formal tube-based robust treatment using a model predictive control (MPC). Such stochastic control methods are more thoroughly reviewed in Refs. [33,39] and could be further explored for truck platooning in the future.

Recently available highway test data exposed problematic control performance from a classical CACC for platooning operation of two homogeneous line-haul trucks deployed under real-world conditions [9]. In this, a gain-scheduled PID compensator was used to engage autonomous longitudinal operation of the trailing truck while maintaining safety through strict gap tracking and velocity harmonization via designer requirements such as responsive rise time, settling time, etc. Whereas the compensator successfully attenuates environmental road grade disturbances to regulate gap and velocity errors, and it aggressively requests engine torque which compromises fuel, comfort, and wear performance. Figure 2 illustrates experimentally measured operation of the autonomous trailing truck in the platoon to regulate tracking errors [9]. Overall, the measured fuel performance of the two-truck platoon under the PID compensator as compared to a non-platooned variant is summarized in Fig. 3 for varied conditions of low grade variation, medium grade variation, and high grade variation. This measures that platooning on the highway is a 3–4% fuel improvement over non-platooning when under low and medium grade conditions, but the control becomes problematic under high grade for a net 1% loss in fuel performance. In general, the platooning operation must be cost effective to justify its use.

This paper compares the design of several feedback control and predictive control strategies for the efficient guidance of a convoy of line-haul trucks under highway operation in a distributed manner. In doing so, trucks in the convoy operate under one of two modes: (1) cruising operation, designated for the leading truck that follows a pre-calculated fuel-efficient velocity profile over the route, and (2) platooning operation, designated for trailing trucks that aim to tightly track close distances from their predecessor and benefit from aerodynamic drafting. Explicit drafting models are leveraged to accurately predict the control power requirements during platooning operation. The platooning control in this case is also designed to be connected, in which acceleration information is exchanged from the preceding truck. Figure 1 depicts a line-haul truck convoy engaged in platoon operation.

To address the experimentally noted short-comings of PID-based CACC for platooning control:

1. The rest of Sec. 2 concludes with a description of the classical CACC design used as a baseline longitudinal controller and as available in Ref. [9].

2. Three MPC formulations of varied predictive model design are presented in Sec. 3: (1) detailed powertrain and engine mappings are modeled to directly minimize fuel consumption, (2) truck energetics are modeled and minimized as a surrogate to reduce fuel consumption, and (3) a neural network trained to predict longitudinal truck dynamics and fuel consumption given easily measurable driving data. The platooning controller performances utilizing both a first-principles model and a data-driven model are compared to a controller using a high fidelity, but difficult to obtain, powertrain model.

3. Extensive high-fidelity platooning simulation is done in Sec. 4 to evaluate the different model predictive controllers. Results of each controller under representative traffic and road grade conditions are examined, and gained fuel efficiency in platooning due to eco-driving and aerodynamic platooning are examined. Additionally, fuel expenditure is correlated to braking losses.

4. Experimental testing and validation are done using MPC with a real two-truck platooning system in Sec. 5.

Section 6 then concludes and summarizes the manuscript with notes for future work.

Here, u is the final acceleration command at a given time instant t, K_P and K_D are, respectively, the proportional and derivative gains that are scheduled via measurements d and Δv, K_I is the constant integral gain, e_d is the current distance error from a reference time headway between trucks, and e_Δv is the relative velocity error between the ego truck and its predecessor. The PID is operationally similar to approaches such as Refs. [5,7].

Here, $τ~$ is a feedforward torque to compensate for aerodynamic and rolling frictions, m_e is the effective inertial mass of the truck, and $σ(ı^)$ is a transmission mapping to engine torque with current gear $ı^$⁠.

Truck platooning is a safety critical application, in which a controller must compensate for actuation and sensor noise while actively mitigating road disturbances. In doing so, the controller is tuned to softly meet strict design requirements that are otherwise not guaranteed to be met, and operationally the PID is aggressive to enforce safe platooning gaps—in which this behavior may not be efficient in powertrain operation. This motivates the need for more advanced control methodologies in platooning operation, in which explicit guarantee of constraint satisfaction can be anticipated and handled, and additionally fuel usage can be directly optimized.

To explicitly address platooning system requirements, an MPC can be formed to perform a constrained optimization for motion planning that enforces safety, road laws, and create performative driving strategies through a designable objective function. In this section, two model-based designs requiring varying system-specific knowledge (e.g., designs requiring detailed powertrain knowledge or designs requiring only fundamental knowledge of longitudinal motion) are compared, and an additional data-driven model that is trained on simulation data to predict longitudinal motion is also proposed. The fundamentals of longitudinal motion by describing force-at-the-wheel kinetics subject to aerodynamic and road losses are given here for brevity before presenting MPC designs, however more complete descriptions of longitudinal vehicle dynamics models are available in Ref. [40].

The state-space model with state x, control input u, and external disturbance w that is used to predict motion is expressed generally as $x˙=f(x,u,w)$⁠. How this model relates to fuel consumption is the subject to be changed within each separate MPC design for platooning.

Constraints on states of the control system can then be imposed to enforce gap safety and road laws.

Here, subscript i indicates the stage of the optimization—which is evaluated for a total of N look-ahead stages. It is assumed that the current road grade α and the current gear $ı^$ are constant over the prediction horizon, and a prediction is made that the leading truck drives with constant acceleration until reaching the speed limit or coming to a stop.

It follows that output engine torque y(t) ≜ τ(t) is bounded by the set $Y$ from Eq. (7), and that states $x(t)≜[d(t)v(t)]⊺$ are bounded by the set $X$ from Eq. (8). Additional slack decision variables are used to soften the state constraints with an $L1$ penalty for run-time feasibility, but are omitted for brevity [43]. Here, boldface indicates a vector of decision variables, e.g., $F=Fi∀i∈1…N$⁠.

The control model of Sec. 3.1 assumes detailed and accurate models of powertrain response, however in reality when controlling real hardware, significant modeling errors may accumulate due to manufacturing defects, measurement and process noise during system identification, cumulative wear-and-tear, etc. As such, it is desirable to approximate platform-specific response from measured data to automatically synthesize models of the platooned system—as well as compare the quality of controller performance based on neural network approximators to the performance of controllers leveraging detailed but manually calibrated models.

Two networks with weightings θ are developed to predict next-stage truck speed v_i+1 and instantaneous fuel consumption m_f,i given current velocity, gear, and grade observations v_i, $ı^$⁠, α—as well as truck acceleration a_i.

Each feedforward network architecture is composed of two fully connected hidden layers with four nodes and tanh activation function, and a fully connected, linear output node. Training is done with the Adam optimizer for stochastic gradient descent [44] with gradient threshold 1.0 and learn rate 2 × 10⁻³. The data for training can source from readily available sensor data during real-world driving, but in this case is collected from high-fidelity simulation of the platooning system. To sufficiently excite the system for all operating conditions, data are collected from multiple scenarios of different truck speeds, road grades, and acceleration aggressiveness, and represents 13 h of driving data. Eighty percent of the collected scenarios are used for training and the remaining 20% are used in predictor accuracy validation. Overall, the model trains to a RMSE of 8.1 mm/s in speed prediction accuracy and trains to a RMSE of 2.2 g/s in instantaneous fuel prediction accuracy.

The platooning operation is excited by imposing two separate major disturbances: road grade and downstream traffic. The leading truck is driven using a predictive cruise controller (PCC) that tracks a set-point velocity in a fuel-optimal manner with respect to road grade [17] and offers V2V connectivity by broadcasting its current speed and acceleration. The trailing truck is driven using the baseline and MPC controllers of Secs. 2 and 3.

The platooning system is first simulated in a high fidelity, Simulink-based software stack that includes the longitudinal vehicle and powertrain dynamics under closed-loop control to virtually realize the performance of the physical trucks. The simulator is similar to Truckmaker [46] and additionally includes platform-specific radar, IMU, DSRC, and drivetrain characterization.

A nominal traffic cycle to represent typical traffic fluctuations during line-haul operation is designated for the leading truck to follow. This 1.3 h traffic cycle is synthesized in Ref. [9] to replicate kinetic intensity of forced truck speed slow-downs due to typical downstream traffic—as found from the NREL fleet DNA database [47].

Additionally, a road elevation profile as from Columbus, IN, to Evansville, IN, USA, is imposed, where the leading truck is assigned to maintain cruising operation in a round-trip mission [9]. To assist in road grade compensation, Cummins PCC features are available as a route and engine-specific fuel-efficient velocity profile as recommended by look-ahead road grade information [48]. As mentioned previously in Ref. [9], the route is distinguished into continuous low, medium, and high grade segments that are approximately similar in length. They have, respectively, grade intensities of 0.5%, 0.9%, and 1.6% on average. Figure 6 depicts a segment of the road grade profile of the considered highway with the PCC-recommended velocity profile for the leading truck to follow, given a 28.7 m/s driver-set velocity.

The PCC system on the leading truck improves fuel economy through eco-driving. When used in a platooning system with homogeneous weight class and hardware specifications—as is the case for the results of this paper—the eco-driving benefits also extend to the trailing trucks, since they are designed to drive at similar velocities during platooning operation. Table 1 measures the energy benefits of utilizing the PCC system on the leading truck. The approach is found to improve fuel economy by 1.9% in low grade driving, and up to 5.9% in high grade driving—which correlates with experimental validation from Ref. [9]. This is attributed to improved vehicle energetics, in which unnecessary braking is significantly attenuated and vehicle overspeeding is attenuated to reduce unnecessary aerodynamic friction losses.

The platooning operation is then managed for the trailing truck using the CACC-enabled PID and the outlined MPC designs. For brevity, the fuel-efficient MPC is focused on due to its consistent performance in simulation, and its later usage in platooning experimentation of Sec. 5, but all MPC performed comparably well and exhibited similar trends.

The qualitative performance of the fuel-efficient MPC compared to the PID is depicted in Fig. 7 for the Columbus–Evansville representative road grade route and is depicted in Fig. 8 for the representative traffic cycle [9]. As can be seen by correlating road grade and gap tracking error, the PID had more significant tracking issues during segments of high road grade than the MPC. In addition, the MPC attenuated unnecessary engine throttle—which led to reduced fuel consumption and improved transient response.

To compare the platooning performance as a whole, the truck energetics for both the leading truck when driven using PCC features and the trailing truck are shown in Fig. 9. The performance over low, medium, and high grades are given, as well as the simulated performance of the representative traffic cycle. Overall, the aerodynamic energy benefits for the trailing truck reduce total work done by 12%, and, for this system, rolling work accounts for 37% and 43% of total work economy for the leading and trailing trucks, respectively (which is hardware-dependent and cannot be improved through control strategies).

Importantly, the consequences of controller behavior appear in the total braking energy dissipated. Braking energy wastes truck momentum and chemical energy burned to propel the truck forward, and so ideal truck operation cuts unnecessary braking as much as possible. However, it is not feasible to completely eliminate braking: necessary system constraints are enforced to prevent collisions with surrounding traffic, and speed limit enforcement can require braking during downhill sections to prevent unsafe speeding. Figure 7 further shows this in the speed limit lines and engine braking lines for the high grade segment of the route, in which a majority of the braking that occurred was during downhill segments that require speed limit maintenance.

Platooned truck fuel consumption over braking energy economy is depicted in Fig. 10, which shows the holistic performance of each platooning controller during the grade and traffic scenarios. Additionally, a linear regression with confidence bounds of the regression parameters is shown to extrapolate the data. This suggests that fuel consumption scales approximately linearly with braking energy dissipated, and that the maximal possible fuel savings with respect to the worst-performing scenario is 15% when no braking is utilized. Indeed, the high grade portion of the route consumed the most fuel, and the other scenarios were close to their maximal fuel efficiency with the performant MPC designs.

The simulation results for fuel consumption, gap tracking performance, and energy dissipated of all controllers are summarized in Table 2. Overall, each MPC is found to more accurately track the gap target compared to PID. Additionally, braking work was found to reduce by 90% when driving on low grade as opposed to high grade, and the traffic cycle realized between 40% and 75% reduction in braking work when utilizing MPC as compared to the PID. Small aerodynamic work differences occurred due to different operational velocities in the scenarios. Overall, choosing MPC over PID is found to improve fuel economy for the platooned truck by 1% in low and medium grade driving, 3% in high grade driving, and between 4–8% in traffic.

Finally, the simulation study is concluded by re-examining the platooning fuel results as compared to the non-platooning case when under high road grade (as is the motivation from CACC testing results represented in Fig. 2). Table 3 depicts fuel improvements of the platooning controllers compared to the non-platooned case. As was found previously, the simulation results suggest that the PID consumes more fuel if platooning under high road grade, however the MPC approaches indeed successfully platoon to improve fuel economy.

The platooning system control is then validated through experimentation. In this, the representative traffic cycle is used to validate the performance of a two-truck platoon at the Transportation Research Center (TRC) high-speed proving grounds—which tested the platooned system for over 80 km of driving. This flat-road testing is depicted in Fig. 1.

The trucks used in testing are of homogeneous hardware specifications. The truck makeup is summarized in Table 4. In addition, the hardware stack enabling autonomy on the testing trucks consists of a radar and service braking control system, Cummins engine controller, Cohda wireless on-board device for V2V communications, and Speedgoat prototyping microcontroller with a 1.4 GHz Intel Celeron. Bus communications are through J1939 CAN protocol.

The MPC designs are cast as nonlinear optimization problems subject to non-convex constraints. Real-time embedded implementation of the algorithms are solved using Forces PRO via an interior point method with L-BFGS Hessian approximation [49], which combines with CasADi for generating gradient expressions of the resulting optimization [45]. Locally optimal solutions are then found in the region local to the supplied initial guess. The continuous-time model equations are discretized with Δt = 0.5 s and explicitly integrated with a second-order Runge Kutta method with 10 integrating nodes between each optimization stage. So that the controllers have similar computation times, the fuel-efficient and neural network MPC then both use a 8 s look-ahead interval, and the kinetic MPC uses a 12 s look-ahead interval. The MPCs presented in this work are runnable at control frequencies of 10 Hz on the embedded hardware.

For experimental evaluation, the two-truck platoon is driven similarly to the testing methodology of the simulation from Sec. 4—in that the leading truck is assigned to track a desired velocity profile using a speed-tracking predictive controller, and the platooned trailing truck is driven using both the baseline PID and fuel-efficient MPC. The experimental study focuses on the representative traffic cycle for repeatability in measuring multiple controllers and as a necessary first stage of control verification before driving on real highways with road grade.

The engine duty cycle is measured in Fig. 11, depicting a heat map of the frequency content of engine torque-speed pairs. It can be seen that the MPC maintains the engine at more fuel-favorable operating conditions than the PID, and reduced the amount of engine braking conducted. Such results are consistent with the simulation study.

Figure 12 then depicts the realized trajectories of the PID and MPC platooning controllers. Contrary to the simulation model provided by the brake-split control system which did not predict engagement of the wheel brake as in Fig. 8, the experimentation realized sometimes significant wheel brake engagement for both controllers. Overall, wheel braking is less accurate in controlling speed of the truck compared to engine-assisted torque braking, and is best-suited for assisting in emergency collision avoidance or bringing the truck to a stop. Experimental testing of the platooning system saw a significantly increased need to apply braking to maintain safe distances from the leading truck and avoid gap constraint violation, thereby dissipating more propulsive energy from fuel. As established in Sec. 4, the increased use of braking likely reduced potential fuel benefits in the chosen control strategies, but offers opportunity for later research to improve the control to maximize fuel savings potentials. Table 5 measures the fuel and energy usage between the PID and fuel-efficient MPC when platooning the trailing truck and reports fuel usage of the truck compared to non-platooned operation (gaps greater than 200 m). It was found that platooning PID improved fuel economy by 6.2%, and MPC improved fuel economy by 9.1%. Additionally, the MPC was found to improve fuel economy by 3.0% over the PID under this representative traffic cycle.

During experimentation, unmodelled exogeneous disturbances possibly changed the acceleration response and polluted the performance of the designed controllers. More significantly, the system is challenged by the model uncertainty, which increases the magnitude of corrective actions from feedback control. Comparing the measured braking work in Table 5 to the simulation-predicted braking work in Table 2, experimentation realized threefold higher amounts of braking and is present in both PID and MPC performance. Braking can be engaged necessarily to avoid system constraint violation and, as in the case of the measured results, occurred to avoid collisions with the leading truck. Such corrective actions to maintain constraint satisfaction were shown to reduce the aerodynamic energy benefits of having platooned as in Sec. 4. MPC was found to perform better in fuel economy than the gain-scheduled PID for platooning operation, and exists as a framework to even better compensate for such uncertainties in future designs.

Truck platooning of heavy-duty line-haul trucks offers potentials in improving fuel economy of trucks by exploiting drafting effects from predecessor trucks, thereby reducing operational costs and reducing harmful emissions. The model predictive platooning controller designs presented in this paper exploit knowledge of these drafting effects to optimize energy consumption during operation and harmonize with the preceding truck while enforcing gap, speed, and powertrain capability constraints to enable safe operation.

Overall, it was found that direct minimization of fuel consumption during platooning operation had a mild 1–2% benefit in fuel economy as compared to first-principles approaches when dealing with grade variations, but lead to greater benefits of 3–4% in fuel economy when dealing with the nominal traffic scenario. Additionally, a model predictive control embedded with trained neural network model approximators was found to perform comparably well to the control approach that exploited fully detailed, manually calibrated powertrain maps.

Common to any performative control approach, eco-driving aspects can attenuate unnecessary braking and reduce overspeeding. Here, the effects of eco-driving in control are independently accounted for by considering the single-truck case without platooning: in low grade environments eco-driving improved fuel economy by 1.9%, and in high grade environments eco-driving improved fuel economy by 5.9%.

These eco-driving effects are then further improved by introducing aerodynamic benefit with platooning. Test-track testing measured a 3.0% additional fuel benefit when using fuel-efficient model predictive control over gain-scheduled PID for platooning control under representative traffic conditions. Additional simulation of real highway conditions with high road grade found a further 3.0% fuel benefit in using the MPC over PID. In total, up to 9.1% fuel savings is measured by enabling eco-driving and platooning control features.

During both simulation and experimentation, it was shown that enforcing necessary gap and speed constraints required braking input for the platooned trucks, which did degrade energy performance by up to 15%. Such necessary braking was found to occur when the system was subjected to high road grade to maintain road speed limits, as well as corrective braking required to enforce gap safety constraints.

We emphasize that platooning control strategies should predictively mitigate the use of braking systems—which will tend to become more active due to maintaining strict gap tracking for safety—which has otherwise been shown here to increase energy losses that limit platooning fuel and safety performance.

This material is based upon work supported by the Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), under Award Number DE-EE0008469. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

There are no conflicts of interest.

The authors attest that all data for this study are included in the paper.