The pursuit of greater efficiency and extended range in hybrid electric vehicles (HEVs) has shifted significant focus towards their thermal management systems. Unlike conventional vehicles, HEVs must manage heat from multiple sources—the internal combustion engine (ICE), the electric motor(s), the power electronics, the high-voltage battery management system (BMS), and the passenger cabin—each with distinct and often conflicting optimal temperature ranges. An inefficient thermal system can lead to reduced battery life, compromised motor performance, increased engine emissions, and excessive energy drain from the climate control system, directly impacting the vehicle’s overall efficiency and driving range. This article explores the modeling, simulation, and optimization of an integrated thermal management system for a series-parallel HEV, with a particular emphasis on innovative strategies for cabin air recovery to enhance battery management system cooling efficiency.
The core challenge lies in the integrated and optimal control of these thermal domains. The internal combustion engine operates efficiently at a high temperature (around 90°C), while the electric motor and power electronics require moderate cooling (around 60-70°C). The lithium-ion battery pack, the heart of the electric drivetrain managed by the BMS, performs best and enjoys the longest lifespan within a narrow window, typically 20°C to 35°C. The passenger cabin, of course, demands comfort, usually between 20°C and 25°C. A well-designed thermal management system must reconcile these needs, minimizing parasitic energy consumption from pumps, fans, and the compressor.
This study centers on a series-parallel hybrid configuration, chosen for its flexibility. It can operate in pure electric mode, engine-only mode, or various hybrid modes where both power sources combine. The thermal system, therefore, must be adaptable to these changing states. The primary subsystems include: the engine cooling circuit (with radiator, thermostat, and pump), a low-temperature circuit for the motor/generator and power electronics, a refrigerant circuit for cabin air conditioning and battery cooling (via a chiller), and the battery management system liquid cooling loop. The optimization proposed here involves creatively linking the cabin’s air outlet to the battery pack enclosure, repurposing conditioned cold air that would otherwise be expelled to the environment.
System Architecture and Component Modeling
The foundation of this analysis is a high-fidelity, one-dimensional model built in the AMESim simulation environment. The model integrates several key modules that interact dynamically under different driving cycles and ambient conditions.
1. Powertrain and Vehicle Dynamics
Accurate simulation of the thermal loads first requires a valid model of the vehicle’s power demand. The model includes the driver, engine, generator, traction motor, battery pack, and transmission. Component sizing follows vehicle dynamics principles. The required traction force $F_t$ must overcome rolling resistance $F_r$, aerodynamic drag $F_a$, gradient resistance $F_i$, and acceleration inertia $F_j$:
$$F_t = F_r + F_a + F_i + F_j = m_v g f_r \cos(\alpha) + \frac{1}{2} C_d A_f \rho v^2 + m_v g \sin(\alpha) + m_v \frac{dv}{dt}$$
where $m_v$ is vehicle mass, $g$ is gravity, $f_r$ is rolling coefficient, $C_d$ is drag coefficient, $A_f$ is frontal area, $\rho$ is air density, $v$ is velocity, and $\alpha$ is road slope. The power demand $P_{dem}$ is then:
$$P_{dem} = \frac{F_t \cdot v}{\eta_t}$$
where $\eta_t$ is drivetrain efficiency. The power sources (engine and motor) and energy storage (battery) are sized to meet this demand across all operational modes. The battery power $P_{batt}$ must satisfy the peak electric motor power $P_{m,max}$, considering efficiencies:
$$P_{batt} \geq \frac{P_{m,max}}{\eta_m \cdot \eta_{inv}}$$
The key parameters for the modeled HEV are summarized below:
| Category | Parameter | Value | Unit |
|---|---|---|---|
| Vehicle | Curb Mass | 1935 | kg |
| Drag Coefficient ($C_d$) | 0.29 | – | |
| Frontal Area ($A_f$) | 2.0 | m² | |
| Wheel Radius | 0.347 | m | |
| Target Cabin Temperature | 22 ± 2 | °C | |
| Battery (BMS Domain) | Type | Lithium-ion | – |
| Rated Capacity / Voltage | 45.5 Ah / 220 V | – | |
| Target Temperature | 28 ± 2 | °C | |
| Electric Motor | Peak Power / Torque | 75 kW / 318 Nm | – |
| Rated Voltage | 375 | V | |
| Internal Combustion Engine | Displacement | 1.5 | L |
| Peak Power / Torque | 103 kW @ 6300 rpm / 160 Nm @ 3750 rpm | – |
2. Thermal Management Subsystems
The integrated thermal model consists of the following key loops:
Engine Cooling Loop: A conventional circuit with a wax-type thermostat controlling flow between a bypass (small loop) and a radiator (large loop). A cooling fan activates based on coolant temperature to augment airflow.
Motor/Generator Low-Temperature Loop: A dedicated liquid circuit with a pump, radiator, and the electric motor/generator as heat sources. This loop operates at a lower temperature than the engine loop.
Refrigerant Loop (Air Conditioning & Battery Chiller): This is the core of the cooling system. The loop contains a compressor, condenser, thermal expansion valve (TXV), and two parallel evaporators: one for the cabin and one acting as a chiller for the battery management system. The chiller is a plate-type heat exchanger where refrigerant evaporates, absorbing heat from the battery coolant flowing on the other side. The system can be controlled to prioritize cabin cooling, battery cooling, or both simultaneously.
Battery Cooling Loop: A liquid circuit managed by the BMS. It includes the battery pack, a pump, and the chiller heat exchanger linked to the refrigerant loop. The battery management system activates the pump and requests cooling from the refrigerant loop based on battery temperature sensors.
The heat generation within the battery pack, a critical input for the BMS, is modeled using a common equivalent circuit approach combined with an energy balance. The heat generation rate $Q_{batt}$ is a sum of irreversible Joule heating and reversible entropic heat:
$$Q_{batt} = I^2 R_{int} + I T \frac{dU_{oc}}{dT}$$
where $I$ is current, $R_{int}$ is internal resistance, $T$ is absolute temperature, and $U_{oc}$ is open-circuit voltage. The BMS must remove this heat to maintain the optimal temperature.
3. Control Strategies and the Proposed Optimization
Three core cooling control strategies are defined for when both cabin and battery require cooling:
Strategy 1: Priority is given to cabin cooling. The refrigerant flow is directed primarily to the cabin evaporator until its target temperature is reached, after which capacity can be shifted to the battery chiller.
Strategy 2: Priority is given to battery cooling managed by the BMS. Refrigerant flow is directed primarily to the chiller to protect the battery first.
Strategy 3: Simultaneous cooling. The refrigerant system attempts to meet both cabin and battery management system demands concurrently.
The Proposed Optimization: Cabin Exhaust Air Recovery. In a standard air conditioning system, a portion of the cooled cabin air is exhausted outside (in fresh air mode) or recirculated. This exhaust air is significantly cooler than the ambient air. The proposed optimization captures this wasted cooling potential. A duct system is modeled to redirect 90% of the cabin’s exhaust air to flow over or through the battery pack enclosure, providing supplemental air-cooling. The remaining 10% is vented outside to maintain pressure balance. This “free cooling” assists the primary liquid-based battery management system, potentially delaying or reducing the need to activate the energy-intensive refrigerant chiller.
Simulation Methodology and Analysis
The model’s powertrain dynamics were first validated against the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and the New European Driving Cycle (NEDC). The simulated vehicle speed showed excellent tracking of the target cycle speed, confirming the model’s fidelity for load calculation.
Thermal simulations were then conducted under six distinct scenarios, combining two ambient temperatures (30°C and 40°C) with the three control strategies. The initial cabin temperature was set to 1.6 times the ambient, and the battery started at the ambient temperature.
| Scenario | Ambient Temp. (°C) | Control Strategy | Key Focus |
|---|---|---|---|
| 1 | 30 | 1 (Cabin Priority) | Baseline performance |
| 2 | 30 | 2 (Battery Priority) | BMS-centric cooling |
| 3 | 30 | 3 (Simultaneous) | Balanced approach |
| 4 | 40 | 1 (Cabin Priority) | High-stress baseline |
| 5 | 40 | 2 (Battery Priority) | High-stress BMS cooling |
| 6 | 40 | 3 (Simultaneous) | High-stress balanced |
Results and Discussion
All three control strategies successfully maintained both cabin and battery temperatures within their target bands under both WLTC and NEDC cycles. However, the trajectories and energy costs differed significantly.
1. Temperature Regulation Performance: As expected, Strategy 1 achieved the fastest cabin cool-down but resulted in the slowest battery cooling. Strategy 2 showed the opposite, prioritizing the battery management system needs. Strategy 3 offered a middle ground. At 40°C ambient, all systems worked harder, and the time to reach target temperatures increased due to higher thermal loads and reduced heat transfer differentials.
2. Impact of Cabin Air Recovery on Energy Consumption: The introduction of cabin exhaust air recovery yielded substantial energy savings, particularly for the battery cooling loop. The auxiliary pump in the battery management system loop consumes less energy when the supplemental air-cooling reduces its required runtime or flow rate.
The following table summarizes the percentage reduction in pump energy consumption for the battery loop across different scenarios with the optimized system:
| Driving Cycle | Ambient Temp. | Strategy 1 Pump Saving | Strategy 2 Pump Saving | Strategy 3 Pump Saving |
|---|---|---|---|---|
| NEDC | 30°C | 24.1% | 2.8% | 3.5% |
| NEDC | 40°C | 33.3% | 5.5% | 11.9% |
| WLTC | 30°C | 20.7% | 3.4% | 5.2% |
| WLTC | 40°C | 34.6% | 6.1% | 12.1% |
The savings are most pronounced for Strategy 1. This is logical because Strategy 1 cools the cabin rapidly, making a large amount of cold exhaust air available early in the cycle to pre-cool or assist in cooling the battery, thereby significantly reducing the subsequent load on the liquid cooling pump.
3. Compressor Work and Net System Energy Benefit: While the pump energy decreased, the compressor energy saw a modest increase in some cases. This is because the overall cooling capacity of the refrigerant cycle is now also contributing more effectively to battery cooling (aided by the air recovery), which may slightly alter its operational profile. However, the net system energy balance was positive. The reduction in pump work, coupled with the more efficient thermal management of the battery, led to an overall increase in the vehicle’s State of Charge (SOC) at the end of the drive cycle.
The improvement in final SOC is the ultimate metric of success for a hybrid vehicle’s ancillary system optimization. The air recovery system provided a consistent gain:
| Driving Cycle | Ambient Temp. | Best SOC Improvement (Strategy) | Energy Saved | Equivalent Consumption Reduction |
|---|---|---|---|---|
| NEDC | 30°C | +0.34% (Strategy 2) | ~ — | ~7.2% of used SOC |
| NEDC | 40°C | +2.50% (Strategy 1) | 526 kJ | 20.4% |
| WLTC | 30°C | +1.18% (Strategy 3) | ~ — | ~14.6% of used SOC |
| WLTC | 40°C | +4.27% (Strategy 1) | 448 kJ | 13.3% |
The coefficient of performance (COP) for the combined system can be conceptually viewed as improved. The useful cooling effect $Q_{cooling}$ now includes both the cabin load $Q_{cabin}$ and the enhanced battery cooling $Q_{batt,enhanced}$, while the work input $W_{in}$ sees a reduction in pump work $W_{pump}$ offset by a potential change in compressor work $W_{comp}$:
$$COP_{enhanced} = \frac{Q_{cabin} + Q_{batt,enhanced}}{W_{comp} + (W_{pump,original} – \Delta W_{pump,saved})}$$
Where $\Delta W_{pump,saved}$ is the positive savings from the optimization. The results confirm that the numerator increases or the denominator decreases, leading to a better overall energy utilization.
Conclusion
This study demonstrates the critical importance of an intelligent, integrated thermal management system for hybrid electric vehicles. Through detailed modeling and simulation in AMESim, the performance of different cooling control strategies was evaluated under standardized driving cycles and thermal conditions. The proposed optimization—recovering and repurposing cold exhaust air from the passenger cabin to assist the battery management system (BMS) cooling loop—proved to be a highly effective method for improving overall vehicle energy efficiency.
The key findings are: Firstly, all three control strategies (cabin-priority, battery-priority, simultaneous) are viable and can maintain thermal targets, but with different energy trade-offs. Secondly, the cabin air recovery scheme provides “free” cooling capacity, significantly reducing the energy consumption of the battery coolant pump by up to 34.6%. Thirdly and most importantly, this leads to a net increase in the vehicle’s usable energy, reflected in a higher State of Charge at the end of the drive cycle. Under high ambient temperature (40°C) conditions, SOC improvements of 2.5% (NEDC) and 4.27% (WLTC) were achieved, corresponding to total energy savings of 526 kJ and 448 kJ, respectively. This translates directly into extended electric driving range or reduced fuel consumption.
In conclusion, integrating waste energy streams, such as cabin exhaust air, into the holistic thermal management strategy, particularly for supporting the battery management system, presents a low-cost, high-impact opportunity for enhancing the efficiency and sustainability of hybrid and electric vehicles. Future work could involve hardware-in-the-loop validation and the development of predictive optimal control algorithms that dynamically manage all thermal loops based on driving and weather forecasts.