2.3.1. Influence of Vehicle Stability Control Methods on Speed Prediction

In order to reach the destination faster, the driver or the intelligently connected vehicles will choose a higher speed, which means that the vehicle's speed is greater than the speed of the surrounding vehicles (traffic), so it needs to overtake and change lanes frequently. In bad weather conditions such as rain and snow, this can bring the risk of accidents such as vehicle skidding. Moreover, in a cold region's road traffic environment, traffic accidents caused by overtaking and lane change on snow and ice slippery road surface are common. In addition, vehicle steering characteristics, driving characteristics, control methods, mass, the height of center of mass, wheelbase, tire lateral stiffness, and other factors affect vehicle handling characteristics in a road traffic environment. Therefore, in order to assess the traffic risk caused by sideslipping and predict the speed variation of the vehicle, a method of lateral traffic risk assessment and speed prediction based on vehicle handling characteristics should be established. This lies in the connection between personalized lateral traffic risk assessment demand and vehicle speed prediction demand. Analyzing the mechanism of mutual coupling between the two demands is beneficial to improve the accuracy of risk assessment prediction and vehicle speed prediction at the same time, so as to achieve the goal of improving road traffic safety and energy saving.

Modern vehicles are equipped with advanced vehicle lateral control strategies, such as active front steering (AFS) and direct yaw moment control (DYC) to enhance vehicle lateral motion control. In recent years, there have been continuous publications on vehicle lateral dynamics control, such as [72–74]. At present, the research literature on distributed electric drive is limited, and it mainly focuses on the theme of four wheel independently actuated electric vehicle. The research content mainly focused on the innovation and application of control methods, such as [75–77]. The uncertainty of tire-road friction factor and vehicle load distribution affect the lateral stability and handling performance of the vehicle. The vehicle dynamics solution for this problem is through a combination of active rear wheel steering (ARS) (also known as "four-wheel steering") and DYC, or through AFS combined with DYC. The study [78] proposed a cooperative control method of AFS and DYC based on optimal guaranteed performance to achieve stability and better vehicle maneuverability. Literature [79] considered the lateral dynamics stability of the vehicle under the condition of time-varying vehicle longitudinal speed. In the work [80], an augmented linear variable parameter model based on combined proportional integral control rate and a robust gainscheduling state-feedback controller is proposed. It minimizes the energy-to-peak control performance of the AFS/DYC system. Other researchers have proposed the combination of front-wheel active steering and direct yaw moment control to promote vehicle handling and stability in the literature [81]. The innovation of the above control methods focuses on the vehicle, closely following the reference path in the interference environment, but the speed prediction problem is not studied. Studies that combine speed prediction with vehicle lateral dynamics are usually found in driving decision problems, such as [82]. A receding horizon method based on mixed logic dynamics constraints with the objectives

of the steering wheel of the driver, longitudinal speed control, and lateral lane tracking performance is established through a safety-guaranteed optimization model.

#### 2.3.2. Development Trend

There is no control strategy to restore the stability of a vehicle after the tire forces on all four wheels are saturated, because there are no more controllable external forces acting on the vehicle. In this case, the way to avoid danger is to anticipate the danger in advance and take actions to avoid it. This is reflected in advance during the deceleration of vehicles, that is, the "speed prediction and planning" mentioned above. Therefore, it can be said that accurate prediction and planning of vehicle speed is very important for both vehicle energy efficiency and vehicle handling stability.

The existing distributed drive control theory research mainly focuses on the fourwheel independent control of electric vehicles. As the degree of vehicle electrification increases, there will be more and more research on the comprehensive consideration of distributed drive control and energy management of hybrid vehicles [65].

The advantages, disadvantages, and applications of the above traffic velocity and vehicle speed forecasting methods are shown in Table 1.

#### *2.4. The Relationship between Energy Management and Velocity Prediction of HEVs*

The United States Energy Information Administration forecasts that oil and other liquid fuels will continue to dominate the transportation industry from 2010 to 2040 although it is noted that their share will decrease significantly (for example, from 98% to 80%) [83]. In other words, fossil energy will still be the main energy resource under the current situation that diesel vehicles account for the majority. Carbon dioxide emissions produced by the transportation industry account for 22% of all emissions, which leads to climate change issues such as global warming [84]. To address air pollution, climate change, and energy shortages, vehicle engineering researchers and policymakers are looking for sustainable alternatives that are less dependent on oil and cause less pollution. HEVs is one of the most promising alternatives.

The main purpose of energy management is to allocate energy demand among different energy sources in order to maintain battery state-of-charge (SoC), optimize energy efficiency, and reduce fuel consumption and emissions, among other related purposes. Huang, Y. et al. [85] divides energy management strategies into two categories: offline and online. The benchmark for this classification is whether the algorithm can operate in real-time, because energy management is designed to be applied in real-time, or as a benchmark to prove the effectiveness of other approaches. Online energy management algorithms can be divided into rule-based energy management and optimization-based energy management [85], as shown in Figure 4.


*Energies* **2021**, *14*, 3431

**Table 1.**

Advantages

 and

disadvantages

 of different velocity prediction methods (VPM).

**Figure 4.** Online classification of energy management algorithms.

Velocity prediction and the quality of prediction results have great impacts on the performance of corresponding predictive energy management strategies (PEMSs) [15]. Velocity prediction should pay attention not only to the accuracy of the prediction, but also the length of the forecast time.

#### 2.4.1. The Accuracy of Velocity Prediction in the Effectiveness of Energy Management

It is common to study an improved velocity prediction in the existing energy management methods of HEVs. Velocity prediction is usually associated with hybrid systems to achieve the objectives of minimum energy consumption, increasing battery life, improving driving safety, etc. Typical examples are given below:

A driver-oriented velocity prediction is created by a deep fuzzy predictor utilizing fuzzy granulation technology, and vehicle speed and acceleration are learned by transition probabilities of a finite-state Markov chain. A chaos-enhanced accelerated swarm optimization is presented with the dual-loop online intelligent algorithm to optimally determine power distribution between two power sources [86]. Markov chain and fuzzy C-means clustering are proposed for cooperative velocity forecasting that is composed of predictive sub-models to deal with various driving patterns. Forecasted velocity profiles are blended via the entire sub-models using quantified fuzzy membership degrees to obtain the final prediction results [87].

Under different short-term velocity horizons, i.e., 5 s, 10 s, and 15 s horizons, a deep neural network is utilized. At the same time, to calculate the optimal power-split at each MPC decision step, the dynamic programming method is applied [88]. Another strategy to speed forecast is introduced via a multi-stage neural network in [89]. ARIMA (autoregressive integrated moving average)-based data-driven strategy to predict shortterm speed and road gradient in real-time is demonstrated [90].

Based on the driving power distribution under different driving cycles, a reinforcement learning controller (RLC) trained by the Q-learning algorithm is studied. A multi-step Markov speed prediction model-based RLC is embedded into a stochastic MPC to find optimal battery power in the predicted time [91]. To achieve the cross-type knowledge transfer between deep learning-based EMSs, a transfer-learning-based method is designed [92].

Another method is the pattern sequence-based speed predictor for accurate short-term speed prediction [93]. It is important to highlight that there are numerous speed prediction

strategies based on the driver model. The main idea is that the speed prediction is obtained with optimization of the engine torque, the brake force, and the gearshift schedule, taking into account safe driving distance and traffic speed limits [94].

Through literature research, it can be concluded that accurate speed prediction can improve the above-mentioned objectives. The objective of speed planning and energy management is generally conflicted with driving safety cost, energy consumption cost, and battery life loss when the EMS is defined as a co-optimization problem over the moving horizon [95]. To better understand the influence of horizon length in speed prediction, the following section is introduced.

## 2.4.2. The Time Length of Velocity Prediction

For the prediction length of traffic parameters, it is relatively easy to accurately predict the change of traffic parameters within a short period of time, because the existing literature assumes that the traffic parameters remain unchanged within a short period of time [15]. However, short-term prediction of traffic parameters has limitations in practical application. For example, when short speed prediction is applied to the energy management of HEVs, it will lead to suboptimality of the solution [15]. In addition, short-term traffic parameter prediction cannot adequately provide the information required for vehicle path planning, and incorrect path planning will lead to the complete loss of fuel economy improved by predicted energy management [15].

These are the current challenges in accurately predicting traffic parameters: (1) The contradiction between prediction accuracy and computation burden (for example, the number of parameters to identify past traffic flow segments); (2) How to choose the duration of data collection and traffic parameter prediction so as to balance the contradiction between prediction accuracy and application requirements?

#### 2.4.3. Development Trends

The goals of energy management for hybrid vehicles are as follows [65]:


The second goal is the core issue of vehicle energy management, that is the "speed forecast and planning" that should be as accurate as possible in the future. The goal of "speed forecast and planning" is not limited to individual vehicles, but should be extended to the entire transportation system. In future, vehicular communication networks (vehicle-to-everything (V2X)), i.e., internet of vehicles and traffic infrastructure (mainly referring to traffic sensors, communication network, and big data analysis and prediction), should be established, and improvements should be made in vehicle intelligence vehicle electrification—braking should be mainly completed by regenerative braking of the motor. One goal of NEVs is to focus on braking without the frequent use of brakes, that is, to pursue accurate prediction and planning speed. Braking is only used in case of emergency.

#### **3. Questions Raised**

From the above research status and development analysis, the following key issues need to be resolved urgently: (1) Establishing a mixed traffic flow model to measure the overall operation of the comprehensive transportation network. The mixed traffic flow model is a complex transportation system model that breaks through the traditional single road network level and integrates multiple types of road networks. (2) Most safety risk assessments are based on accident data analysis, and the identification and quantitative assessment of risk points before accidents need to be improved. At the same time, the vehicle speed prediction method based on vehicle lateral dynamics needs to be studied carefully. Therefore, it is an interesting research direction to study the vehicle speed prediction method, which integrates vehicle handling stability and mixed traffic flow model with its mechanism and implementation method for improving vehicle safety and energy savings. It will have an important impact on improving traffic efficiency, reducing traffic risks, and improving energy utilization on a theoretical basis, revealing the dynamic evolution law and traffic operation situation of road traffic network, mastering the formation mechanism of traffic bottlenecks and traffic accidents. Further, the safety risk of road traffic network needs to be evaluated, so as to lay the foundation for early prediction and avoidance of accidents under adverse weather conditions such as rain and snow. In terms of practical engineering applications, the results provide personalized traffic risk assessment for vehicles with dynamic and handling characteristics, which can improve vehicle safety (safe path planning method) and energy utilization efficiency (energy-saving speed planning method), and promote the application and market-oriented development of intelligent vehicles and intelligent transportation systems.

## *3.1. Traffic Flow Model (Both Macro and Data-Based)*

The macroscopic traffic flow model has experienced a relatively long period of development, and it is still continuing. In recent years, machine learning methods have been used to study and predict traffic parameters. Both model-based and data-based traffic flow models have their own advantages. Their comparison and possible combination and complementarity will be a research direction in the future.

For macroscopic traffic flow models, the following questions need to be answered:


For data-based traffic flow models, we have the following questions:


#### *3.2. Influence of Vehicle Lateral Dynamic on Speed Prediction*

For the influence of vehicle lateral dynamic on speed prediction, we have the following questions:


#### **4. The Application Field of Speed Prediction**

The development of modern intelligent vehicles and intelligent transportation systems requires that different research directions in the past be integrated and comprehensively considered to meet the requirements of simultaneous realization of multiple goals in vehicle design and transportation fields. The comprehensive consideration of vehicle "lateral handling stability" and "optimal energy efficiency" is becoming a trend. The power and steering structures of the existing NEVs are shown as Figures 5–7. The existing literature mainly focuses on the optimal multi-objective control problem of the handling stability and energy efficiency of pure electric vehicles [96,97], and its structure is shown in Figure 5. Energy management studies are common in the design of HEVs, for example: [98]. The stability control of HEV is usually obtained from a series hybrid vehicle similar to the four-wheel independent drive pure electric vehicle, for example: [99]. However, the optimal multi-objective control problem of handling stability and energy efficiency of parallel HEVs and Split HEVs is lacking. In Figure 7, the feature of this arrangement is that the front and rear wheels can be steered separately, and the two front wheels are driven by a wheel motor separately. "Lateral vehicle dynamics" is concerned with the handling stability of the vehicle, namely vehicle "safety".

**Figure 5.** Power system layout structure of a four-wheel independent control electric vehicle.

**Figure 6.** The layout structure of the power system of an ordinary front-wheel steering HEV bus.

Traffic flow parameters such as traffic velocity and traffic density have a direct impact on the speed prediction of vehicles. As shown in Figure 8, traffic parameters are collected by roadside sensors (such as millimeter-wave radar, cameras, etc.) and high-altitude unmanned aerial vehicles. Accurate traffic historical data and real-time data are very important for the accurate prediction of traffic velocity. The data collected by the sensors is applied to macroscopic traffic flow models and learning models to predict traffic velocity in short and relatively long horizons.

**Figure 7.** The layout structure of the power system of an all-wheel-steer distributed-drive new energy vehicle.

**Figure 8.** Use traffic sensors to collect changes in traffic parameters.

Once we understand the evolution of traffic flow and can predict changes in traffic parameters, we then need to understand the dynamics of the vehicles we are riding in. In order to improve traffic efficiency, we assume that the minimum speed of the vehicle is the traffic velocity at this time. However, to get to their destination earlier, passengers sometimes need to travel faster than the traffic speed. At this time, vehicle dynamics plays a key role in the decision-making and speed prediction of intelligent vehicles. Because at this time, whether in the curve or during an overtaking lane change condition, vehicle tire force is closer to its adhesion limit. As shown in Figure 9, when the host vehicle (HV3) is in these conditions, it needs to comprehensively consider the distance (s13 means the relative distance between HV3 and SV1, s23 means the relative distance between HV3 and SV2, s12 means the relative distance between SV1 and SV2), relative speed (V1, V2, V3 indicate the speed of the SV1, SV2, and HV3, respectively) with surrounding vehicles, and the speed limit on the curve. The vehicle needs to assess traffic risks and predict changes in its speed before performing these actions. At this time, the speed prediction is no longer passive,

but an active planning process. Thus, one gets a more accurate prediction of speed when traffic flow and vehicle dynamics are taken into account.

**Figure 9.** An example of the influence of vehicle lateral dynamics on lane change decisions and speed prediction.

For the problem of vehicle routing, most of the existing literature adopt the optimization of single objective as the criterion. This paper proposes a multi-objective optimization method for hybrid vehicle path planning as follows:
