As a researcher focusing on vehicle powertrain control, I have conducted a series of investigations on an extended-range electric commercial vehicle with a gross mass of 16 tons. The study was motivated by the fact that traditional rule-based energy management strategies often lack adaptability to varying driving cycles, cannot fully exploit fuel-saving potential, and may fail to protect the high-voltage battery from severe current fluctuations. In this article, I present my work on powertrain parameter matching, vehicle modeling, and the optimization of energy management strategies using particle swarm optimization and fuzzy logic control. I equally address the performance improvement through a dual fuzzy controller and a hybrid grey wolf optimizer–particle swarm optimizer.
Extended-range electric vehicles, commonly abbreviated as REEVs, are regarded as one of the most effective transitional solutions toward full electrification. Unlike pure battery electric vehicles, which are still limited by battery energy density, charging time, and charging infrastructure, an extended-range electric vehicle can use a small internal combustion engine to drive a generator when the battery state of charge is low. This solution combines the advantages of electric driving with the high energy density of liquid fuels. In my research, I focused on a 16-ton commercial vehicle. The target applications include urban logistics, distribution, and regional freight tasks. Such vehicles usually face complex traffic conditions with frequent starts and stops, varying payloads, and a wide range of speed profiles. Therefore, it is necessary to design an energy management strategy that can dynamically split the power demand between the internal combustion engine, the generator, and the battery pack.
The main contribution of my work is the proposal of a dual fuzzy control architecture. The architecture separates the driving phase from the braking phase and further distinguishes the pure electric mode from the range-extended mode. I also integrate a hybrid grey wolf optimizer and particle swarm optimizer to tune the membership functions of the fuzzy controllers offline. The objective is not only to reduce fuel consumption but also to suppress current spikes and minimize high-rate charge and discharge events that can degrade battery lifetime. The following sections describe the entire methodology in detail.
Powertrain Configuration and Parameter Matching
I selected a series hybrid architecture for the target extended-range electric commercial vehicle. In this architecture, the internal combustion engine is not mechanically connected to the wheels. Instead, the engine drives a dedicated generator. The electrical power generated by the generator can be supplied directly to the traction motor or stored in the power battery. The traction motor is the only power source that propels the wheels. This decoupling between the engine and the driving wheels enables the engine to operate near its optimal speed–torque region regardless of the vehicle speed. This feature is particularly beneficial for heavy commercial vehicles that often operate in urban stop-and-go traffic. The structure that I considered is illustrated by the conceptual energy flow in the following paragraphs.
For the parameter matching, I used the vehicle basic parameters listed in the table below. The target vehicle is a 16-ton commercial truck for regional freight.
| Parameter | Value | Unit |
|---|---|---|
| Overall length | 12000 | mm |
| Overall width | 2600 | mm |
| Overall height | 3960 | mm |
| Curb mass | 7980 | kg |
| Gross mass | 16660 | kg |
| Wheel rolling radius | 463 | mm |
| Frontal area | 10 | m2 |
| Aerodynamic drag coefficient | 0.6 | – |
| Rolling resistance coefficient | 0.013 | – |
| Driving axle final ratio | 6.058 | – |
The dynamic performance target of the vehicle is reported below.
| Index | Value | Unit |
|---|---|---|
| Maximum speed (full load) | 90 | km/h |
| Maximum gradient | 30 | % |
| Range under pure electric operation | 100 | km |
| Acceleration time from 0 to 30 km/h | 30 | s |
Matching of the Traction Motor
I compared different motor types available for automotive applications. Permanent magnet brushless motors have a high power density, high efficiency over a wide speed range, and low mass. After a comprehensive comparison, I chose a permanent magnet synchronous motor as the traction machine for my extended-range electric vehicle. The motor needs to satisfy three performance constraints: maximum cruise speed, maximum gradient at low speed, and acceleration capability.
The power required to sustain the maximum speed \(u_{max}\) can be calculated as:
\[
P_{m1}=\frac{1}{\eta_t}\Big(\frac{mgf u_{max}}{3600}+\frac{C_D A u_{max}^3}{76140}\Big)
\]
where \(\eta_t\) is the driveline efficiency, \(m\) is the gross mass, \(g\) is the gravitational constant, \(f\) is the rolling resistance coefficient, \(C_D\) is the aerodynamic drag coefficient, and \(A\) is the frontal area. Using the data in the vehicle parameter table, I obtained \(P_{m1}=124\) kW.
The power needed for climbing a constant gradient with a low speed \(u_p\) is:
\[
P_{m2}=\frac{u_p}{3600\eta_t}\Big(mg f \cos\alpha_{max}+mg\sin\alpha_{max}+\frac{C_D A u_p^2}{21.15}\Big)
\]
where \(\alpha_{max}\) is the maximum road angle. The result was \(P_{m2}=77.1\) kW.
The power required to achieve a specified acceleration time from rest to speed \(u_e\) is:
\[
P_{m3}=\frac{1}{3600\eta_t}\Big(mgf u_e+\frac{C_D A u_e^3}{21.15}+1.1 m u_e \frac{du}{dt}\Big)
\]
Considering the target acceleration time, the required power was \(P_{m3}=252.5\) kW. Therefore, the peak power of the traction motor was set above this value. I then selected a motor with a rated power of 200 kW and a peak power of 300 kW. The rated speed is 1500 r/min and the maximum speed is 4000 r/min. The rated torque and peak torque are computed with:
\[
T_e=9550 \frac{P_e}{n_e}, \quad T_{max}=9550 \frac{P_{max}}{n_e}
\]
The key motor parameters are listed in the following table.
| Parameter | Value |
|---|---|
| Rated power | 200 kW |
| Peak power | 300 kW |
| Rated speed | 1500 r/min |
| Maximum speed | 4000 r/min |
| Rated torque | 1273 Nm |
| Peak torque | 1910 Nm |
Battery Pack Design
The power battery of an extended-range electric vehicle must supply sufficient energy for a target all-electric driving distance and simultaneously satisfy the peak power requirement of the traction motor. I selected lithium iron phosphate cells because of their long cycle life, high safety, and acceptable cost.
The battery capacity needed to achieve a target pure electric range of 100 km under constant-speed driving can be expressed as:
\[
C_E \ge \frac{(mgf+\frac{C_D A u^2}{21.15}) S_1}{3600 \eta_t \eta_{mc} \eta_{dis} U_b (1-\eta_a) DOD}
\]
where \(S_1\) is the required pure electric driving distance, \(u\) is the constant cruising speed, \(\eta_{mc}\) is the motor efficiency, \(\eta_{dis}\) is the discharge efficiency, \(U_b\) is the terminal voltage, \(\eta_a\) is the accessory power consumption coefficient, and \(DOD\) is the depth of discharge. From the given vehicle parameters, I calculated the required capacity as 116 Ah. The battery capacity must also meet the power constraint:
\[
C_P \ge \frac{P_{e,max}+P_A}{k U_b}
\]
where \(P_A\) is the auxiliary power and \(k\) is the maximum discharge rate. Combining the energy and power constraints, I selected a battery pack that consists of 188 cells in series, with a nominal cell voltage of 3.2 V and a rated voltage of 600 V. The rated capacity is 116 Ah.
| Parameter | Value |
|---|---|
| Battery chemistry | Lithium iron phosphate |
| Cell voltage | 3.2 V |
| Number of cells | 188 |
| Nominal voltage | 600 V |
| Nominal capacity | 116 Ah |
Range Extender Matching
The range extender consists of an internal combustion engine and a permanent magnet generator. I matched the engine power based on the power needed to keep the vehicle running at its maximum speed. After considering auxiliary loads, I selected a diesel engine with a rated power of 150 kW and a rated speed of 1500 r/min. The generator has a rated power of 135 kW and a peak power of 150 kW. The table below shows the main range extender specifications.
| Component | Parameter | Value |
|---|---|---|
| Diesel engine | Rated power | 150 kW |
| Rated speed | 1500 r/min | |
| Maximum torque | 900 Nm | |
| Generator | Rated power | 135 kW |
| Peak power | 150 kW | |
| Rated speed | 1500 r/min | |
| DC bus voltage | 600 V |
Vehicle Modeling and Co-Simulation Platform
I built the forward vehicle model using AVL Cruise. The longitudinal dynamics of the vehicle obey the equation:
\[
F_t=F_f+F_w+F_i+F_j
\]
where the individual forces are the rolling resistance \(F_f\), aerodynamic drag \(F_w\), gradient resistance \(F_i\), and acceleration resistance \(F_j\). These terms are described by:
\[
F_f=mgf\cos\alpha
\]
\[
F_w=\frac{1}{2}\rho C_D A v^2
\]
\[
F_i=mg\sin\alpha
\]
\[
F_j=\delta m\frac{dv}{dt}
\]
The required torque at the wheel can be derived as:
\[
T_t=\frac{r}{i_g i_0 \eta_t}\Big(mgf\cos\alpha+\frac{C_D A v^2}{21.15}+mg\sin\alpha+\delta m\frac{dv}{dt}\Big)
\]
in which \(i_g\) is the transmission ratio, \(i_0\) is the final drive ratio, \(r\) is the wheel radius, and \(\delta\) is the rotational mass conversion factor.
Using AVL Cruise, I assembled the hybrid components, such as the driver model, motor, engine, generator, battery, mechanical braking, final drive, wheels, and a MATLAB interface module. I separately created the energy management controller in MATLAB/Simulink. The cruise model sends the current speed, acceleration, demand torque, battery state of charge, and applied pedal signals to Simulink. The controller computes operating commands for the range extender and battery power distribution and then returns those commands to Cruise. In this way, the closed-loop co-simulation platform can precisely evaluate the vehicle behavior and fuel consumption under any given driving cycle.
I verified the model by simulating a conventional diesel commercial vehicle and the newly created extended-range electric commercial vehicle under the C-WTVC cycle. The speed tracking result was accurate. The conventional vehicle consumed 31.79 L/100 km, while the extended-range electric vehicle consumed 28.67 L/100 km under the same conditions. The improvement in fuel economy was 9.7%, which confirmed that the vehicle model is reliable for further energy management studies.
Equivalent Consumption Minimization Strategy for the Range-Extended Electric Vehicle
The energy management problem of an extended-range electric vehicle can be considered as a power split problem between the range extender and the battery pack. I first explored the equivalent consumption minimization strategy, commonly known as ECMS. The fundamental idea is to convert the electrical power used from the battery into an equivalent virtual fuel consumption rate. This converted value is added to the actual engine fuel consumption rate, forming an instantaneous cost function.
The instantaneous equivalent fuel consumption can be written as:
\[
\dot m_{f,eqv}(t)=\dot m_f(t)+\dot m_{bat}(t)
\]
where \(\dot m_f\) is the actual fuel consumption of the engine and \(\dot m_{bat}\) is the virtual fuel consumption corresponding to the battery electrical power. The actual engine fuel consumption \(\dot m_f\) is read from the engine fuel consumption map at the current engine speed and torque:
\[
\dot m_f=f_{map}(T_f(t),n_f(t))
\]
The battery equivalent fuel consumption is approximated by:
\[
\dot m_{bat}=f_{eq}(t)\frac{P_{bat}(t)}{Q_{lhv}}
\]
where \(f_{eq}\) is the equivalence factor and \(Q_{lhv}\) is the lower heating value of diesel fuel. Under a discharge condition, \(P_{bat}\) is positive, while under a charge condition, \(P_{bat}\) is negative. Therefore, the instantaneous minimization problem is:
\[
J_{local}(t)=\min_{T_m(t)} \big(\dot m_f(t)+f_{eq}(t)\dot m_{bat}(t)\big)
\]
The control variables are constrained by physically admissible operating limits:
\[
T_{req}(t)=T_e(t)+T_m(t)
\]
\[
SOC_{min} \le SOC(t) \le SOC_{max}
\]
\[
T_{m,min} \le T_m(t) \le T_{m,max}
\]
\[
0 \le T_e(t) \le T_{e,max}
\]
To solve the instantaneous optimization, I discretized the torque range of the electric motor into finite candidate values. For each motor torque candidate, the corresponding engine torque is computed by subtracting the motor torque from the required torque. The algorithm then calculates the engine fuel consumption, electric battery power, and final virtual fuel consumption. The motor torque candidate that minimizes the local cost function is selected as the optimal command.
The equivalence factor \(f_{eq}\) is the most critical parameter in ECMS. A poorly selected equivalence factor can cause excessive battery depletion or a strong tendency to recharge the battery. I investigated this sensitivity by simulating three different values of \(f_{eq}\) under the same initial state of charge. The results are summarized in the following table.
| Case | Equivalence factor range | Final SOC value |
|---|---|---|
| Case 1 | [1.2238, 9.436] | 58.3% |
| Case 2 | [1.2687, 9.9437] | 65.32% |
| Case 3 | [1.0595, 9.1586] | 48.96% |
I also found that when the equivalence factor determined under the C-WTVC driving cycle is used directly in another cycle, the state-of-charge trajectory changes significantly. This means that the equivalence factor must be adapted online to the current driving condition.
Adaptive ECMS with PI Feedback
To maintain battery charge sustainability, I designed an adaptive equivalent consumption minimization strategy, denoted A-ECMS. A proportional–integral controller is introduced to adjust the equivalence factor based on the difference between the current state of charge and a reference state of charge. Let the SOC error be:
\[
\Delta SOC(t)=SOC_{ref}-SOC(t)
\]
The equivalence factor is then updated as:
\[
f_{eq}(t)=f_{eq0}+K_P \Delta SOC(t)+K_I \int \Delta SOC(t) dt
\]
The proportional term improves the response speed, while the integral term eliminates the steady-state error. The adjustment parameters \(K_P\), \(K_I\), and the initial equivalence factor \(f_{eq0}\) determine whether the final SOC converges to the reference. I carried out simulations to show how these parameters influence the SOC trajectory. The next table shows the final SOC deviation for different combinations of controller gains.
| \(K_P\) | \(K_I\) | \(\Delta SOC\) final deviation |
|---|---|---|
| 1 | 1 | 0.72% |
| 1 | 5 | 10.8% |
| 15 | 1 | 0.03% |
| 15 | 5 | 0.2% |
| 30 | 10 | 0.36% |
| 30 | 5 | 0.03% |
From the above results, a larger proportional gain can greatly improve the SOC convergence behavior. However, if the integral gain is too large, the equivalence factor can oscillate excessively. Therefore, I used the particle swarm optimization algorithm to obtain suitable values of \(K_P\), \(K_I\), and \(f_{eq0}\) automatically. The particle swarm algorithm minimizes an objective function that combines fuel consumption \(Q_s\) and battery electrical energy consumption \(E_s\) over a driving cycle. I defined the cost as:
\[
J=Q_s+\lambda E_s
\]
where \(\lambda\) is an oil–electricity equivalence coefficient. After running the optimization under the C-WTVC cycle, I obtained the optimized PI parameters as \(K_P=21.15\), \(K_I=0.069\), and \(f_{eq0}=0.323\). The corresponding fuel consumption was 27.68 L/100 km.
The resulting engine operating points are much more concentrated in the high-efficiency region than those of a simple rule-based strategy. I also compared the A-ECMS strategy with the rule-based control from the original vehicle. The A-ECMS achieved a fuel consumption of 26.68 L/100 km, while the rule-based control consumed 28.98 L/100 km. The state-of-charge deviation in the A-ECMS case was only \(-0.34\%\), whereas the rule-based control caused a positive deviation of 7.26%. These results show that the optimized A-ECMS performs well in terms of both fuel economy and battery charge maintenance.
Dual Fuzzy Controller Design
Even though A-ECMS improves fuel economy, I observed that current fluctuation at the battery terminals could still be significant. Sustained high-rate discharges and high-rate charges can shorten battery lifespan. To solve this problem, I introduced a dual fuzzy controller architecture. In this architecture, the vehicle modes are classified according to the power demand and the battery state of charge:
| Condition | Operating mode |
|---|---|
| SOC > threshold and \(P_{req} \ge 0\) | Pure electric driving |
| SOC > threshold and \(P_{req} < 0\) | Regenerative braking |
| SOC < threshold and \(P_{req} > P_{opt}\) | Range-extended discharge assistance |
| SOC < threshold and \(P_{req} < P_{opt}\) | Range-extended battery charging |
| Vehicle stationary and SOC < threshold | Charge mode with halted vehicle |
The main innovation of my proposed method is that a single fuzzy controller is replaced by a positive fuzzy controller and a negative fuzzy controller. When the vehicle is in a driving state, the required power \(P_{req}\) is positive, so the positive fuzzy controller determines the maximum allowable battery output power coefficient. When the vehicle is in a braking or energy surplus state, the negative fuzzy controller determines the regeneration ratio or the charge power coefficient.
For the positive fuzzy controller used in pure electric mode, the inputs are the normalized required power and the battery SOC. The output is the maximum allowable power discharge coefficient \(K_{dis}\). I used triangular membership functions for all input and output sets. The input required power is divided into four fuzzy subsets: ZE, PS, PM, PB. The battery SOC is divided into three subsets: L, M, H. The output coefficient \(K_{dis}\) is divided into five subsets: VS, S, M, B, VB. The rule table is shown below.
| Required power | L (\(SOC_b\)) | M (\(SOC_b\)) | H (\(SOC_b\)) |
|---|---|---|---|
| ZE | VS | VS | S |
| PS | S | M | B |
| PM | S | M | VB |
| PB | VS | B | VB |
When the required power is negative, the negative fuzzy controller is activated. Its inputs are the normalized braking power and battery SOC. The output is the braking energy recovery coefficient \(K_{reg}\). The rule table for the negative fuzzy controller is:
| Braking power | L (\(SOC_b\)) | M (\(SOC_b\)) | H (\(SOC_b\)) |
|---|---|---|---|
| ZE | LE | LE | LE |
| NS | HE | HE | LE |
| NM | HE | H | L |
| NB | H | M | LE |
The actual recovered power to the battery is:
\[
P_{rec}=P_{req} K_{reg}
\]
The friction braking power is:
\[
P_{mech}=P_{req}(1-K_{reg})
\]
The negative fuzzy controller ensures that when the battery SOC is high, the regenerative braking coefficient is small so that overcharge is avoided. When the battery SOC is low, a large recovery coefficient is used to capture more braking energy.
In the range-extended mode, I selected a point-following control strategy for the engine. The engine always operates at its optimal fuel consumption point and provides a constant power \(P_{opt}\). When the vehicle demand power is higher than \(P_{opt}\), the positive fuzzy controller determines how much of the power gap \(\Delta P_1=P_{req}-P_{opt}\) should be supplied by the battery. Conversely, when the demand power is lower than \(P_{opt}\), the negative fuzzy controller determines the charging power coefficient \(\Delta P_2=P_{opt}-P_{req}\). The same fuzzy inference method and defuzzification method are used.
Optimization of the Fuzzy Controllers Using GWO-PSO
The membership functions of the fuzzy controllers are usually chosen from experience, which can yield non-optimal results. To remove the dependence on empirical tuning, I adopted a hybrid grey wolf optimizer–particle swarm optimizer, denoted as GWO-PSO.
The particle swarm optimizer models the search as a population of particles. Each particle has a velocity and a position in the \(d\)-dimensional search space. At iteration \(t\), the velocity update rule is:
\[
v_i(t+1)=\omega v_i(t)+c_1 r_1\big(P_i(t)-X_i(t)\big)+c_2 r_2\big(P_g(t)-X_i(t)\big)
\]
\[
X_i(t+1)=X_i(t)+v_i(t+1)
\]
where \(\omega\) is the inertia weight, \(c_1\) and \(c_2\) are learning factors, \(r_1\) and \(r_2\) are random numbers in [0,1], \(P_i\) is the personal best position, and \(P_g\) is the global best position.
In the hybrid algorithm, the inertia weight is modulated by a chaotic operator. The position update of each particle is then adjusted according to the guidance of the three best wolves found in the grey wolf optimizer. The grey wolf search process is defined by:
\[
D_1=|C_1 X_a-X(t)|, \quad D_2=|C_2 X_b-X(t)|, \quad D_3=|C_3 X_c-X(t)|
\]
The new particle position is computed from the average of the three moving directions:
\[
X_1=X_a-A_1 D_1, \quad X_2=X_b-A_2 D_2, \quad X_3=X_c-A_3 D_3
\]
\[
X(t+1)=\frac{X_1+X_2+X_3}{3}
\]
I used the optimized parameter vector as:
\[
x=(x_1,x_2,\ldots,x_{36})
\]
The first 14 variables are centers of membership functions, and the remaining 22 variables are widths. The optimization objective was to minimize the total energy consumption, expressed as:
\[
J(x)=EC+FC
\]
where \(EC\) is the electrical energy consumption of the battery and \(FC\) is the fuel consumption of the engine over a driving cycle. The constraints include maximum speed, maximum grade, and acceleration performance.
The optimization process was performed offline in the MATLAB/Simulink environment. At each iteration, the candidate membership functions were written into the fuzzy logic controller, and the full driving-cycle simulation was executed. The fitness value was then returned to the optimizer. After 80 generations with a population of 40, the algorithm converged to an improved fuzzy controller. The optimized membership functions were not necessarily located exactly at the positions selected from prior expertise, which confirms that the optimization reduces the subjectivity in the controller design.
Simulation Results and Discussions
I verified the proposed strategy using the C-WTVC driving cycle, which is the standard China World Transient Vehicle Cycle for heavy-duty commercial vehicles. The cycle includes urban, suburban, and highway segments and lasts for 1800 seconds. I compared four strategies: single-source pure battery used directly, rule-based logic threshold, dual fuzzy control without optimization, and GWO-PSO optimized dual fuzzy control. The simulation was carried out in both pure electric mode and range-extended mode. The initial state of charge in pure electric mode was 0.9, while in range-extended mode the initial SOC was 0.3 because the range extender was activated below 0.3. In the following sections, I summarize the simulation results.
Results in Pure Electric Mode
Under the pure electric condition, the battery is the only energy source. The speed tracking performance was satisfactory in all strategies. The SOC trajectories at the end of the cycle are summarized in the following table.
| Control strategy | Final SOC (%) |
|---|---|
| Single-source pure operation | 74.8 |
| Logic threshold control | 78.6 |
| Dual fuzzy control | 83.4 |
| GWO-PSO dual fuzzy control | 85.7 |
Compared with the single-source control, the dual fuzzy strategy increased the final SOC by 11.48%, while the GWO-PSO optimized dual fuzzy control increased the final SOC by 14.56%. This means that the optimized strategy reduces the electrical energy consumption, which directly extends the pure electric range.
Battery current is closely related to battery lifetime. High-rate charge and discharge events can accelerate capacity degradation. I considered a current above 25 A as a high-rate current because the nominal capacity of the selected battery is approximately 25 Ah, which corresponds to 1C. The battery current results in pure electric mode are shown below.
| Strategy | Maximum discharge current / A | Average discharge current / A | High-rate discharge duration / s |
|---|---|---|---|
| Single-source | 179.44 | 25.86 | 226 |
| Logic threshold | 160.43 | 21.65 | 150 |
| Dual fuzzy control | 43.23 | 12.46 | 85 |
| GWO-PSO dual fuzzy control | 34.45 | 9.69 | 74 |
The GWO-PSO optimized dual fuzzy control reduced the maximum discharge current by about 20.69% compared with the non-optimized dual fuzzy control. The high-rate discharge duration decreased by 15.12% relative to the non-optimized control. Such a reduction is meaningful for the battery cycle life.
Results in Range-Extended Mode
In the range-extended mode, the engine–generator system is turned on to maintain the battery SOC around a target level. I set the SOC target to 0.3, and the range extender was activated when the SOC fell below 0.3 or when the demand power was high and the battery could not solely satisfy the request. The SOC behavior at the end of the cycle is listed below.
| Strategy | Final SOC (%) |
|---|---|
| Single-source operation | 75.0 |
| Logic threshold | 69.2 |
| Dual fuzzy control | 58.6 |
| GWO-PSO dual fuzzy control | 58.8 |
The lower final SOC values for the dual-fuzzy strategies indicate that the battery supplies more energy to assist the engine during the cycle. This is reasonable because the range extender is controlled to operate near its optimal point. However, the most important result is that the battery current profile is much smoother than in conventional strategies. I evaluated the charging current statistics as follows.
| Strategy | Maximum charge current / A | Average charge current / A | High-rate charge duration / s |
|---|---|---|---|
| Single-source | 125.97 | 40.74 | 74 |
| Logic threshold | 111.60 | 33.24 | 69 |
| Dual fuzzy control | 57.16 | 20.77 | 69 |
| GWO-PSO dual fuzzy control | 57.11 | 20.70 | 55 |
From the table, both dual fuzzy controllers significantly reduce the maximum charging current compared with the single-source strategy. More importantly, the GWO-PSO optimized dual fuzzy control reduced the high-rate charging duration to 55 seconds, which is 18.84% shorter than the non-optimized dual fuzzy control. As a result, the battery experiences fewer severe charge events, which is beneficial for extending the service life of the electric vehicle battery.
Fuel Economy Comparison
Fuel economy is a central concern for commercial vehicles. I evaluated the total fuel consumption per 100 km under the C-WTVC condition for several strategies. The results are compared in the table below.
| Control strategy | \(\Delta SOC\) | Fuel consumption / L per 100 km |
|---|---|---|
| A-ECMS (PSO tuned) with optimal PI | -0.34% | 26.68 |
| Rule-based logic threshold | 7.26% | 28.98 |
| GWO-PSO dual fuzzy control | -0.75% | 26.23 |
The GWO-PSO optimized dual fuzzy control achieved a fuel consumption of 26.23 L/100 km, compared with 28.98 L/100 km for the rule-based strategy. This corresponds to a fuel saving of 6.38%. The optimized dual fuzzy strategy also yields a small SOC deviation of \(-0.75\%\), which confirms that battery charge is effectively maintained during the cycle. The A-ECMS approach also shows good SOC balance, but the dual fuzzy control provides an additional 1.7% fuel saving. In addition, the dual fuzzy strategy achieves lower current fluctuations, thus helping to protect the battery.
I also compared the engine operating point distributions. With the rule-based strategy, many engine points were scattered in the low-torque and thus low-efficiency zone. With the dual fuzzy control, the engine is often kept at a constant power operating point near its optimum. When the vehicle requires a power above this optimum, the battery supplements the deficit. When the required power is below this optimum, the battery absorbs the surplus power. This strategy effectively keeps the engine out of undesirable operating regions.
Furthermore, the GWO-PSO optimization changes the shapes of the membership functions in a way that cannot be easily derived by experience. The optimized controller gives a more conservative discharge limit when the battery SOC is low, avoiding deep discharge. At high SOC levels, it allows a higher battery output power to preserve drivability. In braking conditions, the optimized controller prevents the battery from accepting excessive current when the battery is already full, thereby reducing the risk of overcharge and lithium plating.
Conclusion
In this work, I developed a comprehensive energy management strategy for a 16-ton extended-range electric commercial vehicle. I first matched the traction motor, battery pack, engine, and generator according to the vehicle dynamic requirements. I then built a co-simulation platform using AVL Cruise and MATLAB/Simulink to evaluate the energy management strategies under realistic driving cycles.
I studied the equivalent consumption minimization strategy and designed an adaptive version with a PI controller. A particle swarm optimizer was used to tune the PI gains and the initial equivalence factor automatically. The results show that this adaptive ECMS successfully maintains the battery state of charge and reduces fuel consumption compared with the conventional rule-based control.
To further suppress current fluctuation and protect the power battery, I proposed a dual fuzzy controller energy management strategy. The positive fuzzy controller limits the discharge power during acceleration and high-load driving, while the negative fuzzy controller adjusts the energy recovery ratio during braking. The two fuzzy controllers work together to keep the battery current within a safer range. I additionally used a grey wolf optimizer–particle swarm optimizer to optimize all membership functions offline. This reduces the subjectivity of the fuzzy controller design and improves the overall system performance.
Under the C-WTVC driving cycle, the optimized GWO-PSO dual fuzzy control achieved a 6.38% fuel saving over the rule-based strategy. In pure electric mode, the maximum discharge current decreased by approximately 20.69%, and the high-rate discharge duration was shortened by 15.12% relative to the non-optimized dual fuzzy control. In the range-extended mode, the high-rate charging duration was reduced by 18.84%. These results confirm that the proposed optimized energy management strategy is both effective and practical for improving the fuel economy of the extended-range electric vehicle.
Future work will be directed to real-world validation. I also plan to consider road grade and payload variations as additional inputs to the fuzzy controller. Intelligent algorithms with stronger online adaptation features will be explored to establish a more robust and universal energy management framework for next-generation commercial electric vehicles.